added cicd modified
This commit is contained in:
Utsho Dey
parent
9c15f7a59c
commit
019e07e1b9
16 changed files with 481 additions and 430 deletions
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@ -11,7 +11,6 @@ cache:
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stages:
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- setup
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- lint
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- test
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before_script:
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@ -29,14 +28,6 @@ setup:
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- venv/
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expire_in: 1 hour
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lint:
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stage: lint
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needs:
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- setup
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script:
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- black --check app/ main.py tests/
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- flake8 app/ main.py tests/ --max-line-length=100
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test:
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stage: test
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needs:
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@ -47,7 +38,7 @@ test:
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# Start FastAPI server
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- uvicorn main:app --host 0.0.0.0 --port 8000 &
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# Wait for server to start
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- sleep 10
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- sleep 15
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# Test health endpoint
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- |
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RESPONSE=$(curl -s -o /dev/null -w "%{http_code}" http://localhost:8000/health)
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@ -6,7 +6,7 @@ from app.models.ai_fact_check_models import (
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AIFactCheckResponse,
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VerificationResult,
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TokenUsage,
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ErrorResponse
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ErrorResponse,
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)
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from urllib.parse import urlparse
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import asyncio
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@ -16,13 +16,11 @@ aifact_check_router = APIRouter()
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openai_client = OpenAIClient(api_key=OPENAI_API_KEY)
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fact_checker = AIFactChecker(openai_client=openai_client)
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@aifact_check_router.post(
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"/aicheck-facts",
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response_model=AIFactCheckResponse,
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responses={
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400: {"model": ErrorResponse},
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500: {"model": ErrorResponse}
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}
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responses={400: {"model": ErrorResponse}, 500: {"model": ErrorResponse}},
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)
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async def ai_fact_check(request: AIFactCheckRequest):
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"""
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@ -40,14 +38,14 @@ async def ai_fact_check(request: AIFactCheckRequest):
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total_prompt_tokens = 0
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total_completion_tokens = 0
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total_tokens = 0
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# Process all URLs concurrently
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tasks = [
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fact_checker.check_fact(url=url, query=request.content)
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for url in request.urls
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]
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fact_check_results = await asyncio.gather(*tasks, return_exceptions=True)
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# Process results
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for url, result in zip(request.urls, fact_check_results):
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if isinstance(result, Exception):
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@ -57,21 +55,21 @@ async def ai_fact_check(request: AIFactCheckRequest):
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confidence="Low",
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evidence=f"Error checking URL: {str(result)}",
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reasoning="URL processing failed",
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missing_info="Could not access or process the URL"
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missing_info="Could not access or process the URL",
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)
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continue
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verification_result = VerificationResult(
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verdict=result["verification_result"]["verdict"],
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confidence=result["verification_result"]["confidence"],
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evidence=result["verification_result"]["evidence"],
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reasoning=result["verification_result"]["reasoning"],
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missing_info=result["verification_result"].get("missing_info", None)
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missing_info=result["verification_result"].get("missing_info", None),
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)
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results[url] = verification_result
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all_sources.update(result["sources"])
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# Accumulate token usage
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total_prompt_tokens += result["token_usage"]["prompt_tokens"]
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total_completion_tokens += result["token_usage"]["completion_tokens"]
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@ -80,24 +78,22 @@ async def ai_fact_check(request: AIFactCheckRequest):
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token_usage = TokenUsage(
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prompt_tokens=total_prompt_tokens,
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completion_tokens=total_completion_tokens,
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total_tokens=total_tokens
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total_tokens=total_tokens,
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)
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return AIFactCheckResponse(
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query=request.content,
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verification_result=results,
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sources=list(all_sources),
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token_usage=token_usage
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token_usage=token_usage,
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)
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except ValueError as e:
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raise HTTPException(
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status_code=400,
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detail=ErrorResponse(
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detail=str(e),
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error_code="INVALID_URL",
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path="/aicheck-facts"
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).dict()
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detail=str(e), error_code="INVALID_URL", path="/aicheck-facts"
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).dict(),
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)
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except Exception as e:
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raise HTTPException(
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@ -105,6 +101,6 @@ async def ai_fact_check(request: AIFactCheckRequest):
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detail=ErrorResponse(
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detail=f"Error processing fact-check request: {str(e)}",
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error_code="PROCESSING_ERROR",
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path="/aicheck-facts"
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).dict()
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)
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path="/aicheck-facts",
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).dict(),
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)
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@ -4,16 +4,17 @@ from app.config import GOOGLE_API_KEY, GOOGLE_FACT_CHECK_BASE_URL, OPENAI_API_KE
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from app.api.scrap_websites import search_websites, SearchRequest
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from app.services.openai_client import OpenAIClient
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from app.models.fact_check_models import (
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FactCheckRequest,
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FactCheckResponse,
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FactCheckRequest,
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FactCheckResponse,
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ErrorResponse,
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Source
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Source,
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)
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from app.websites.fact_checker_website import get_all_sources
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fact_check_router = APIRouter()
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openai_client = OpenAIClient(OPENAI_API_KEY)
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async def generate_fact_report(query: str, fact_check_data: dict) -> FactCheckResponse:
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"""Generate a fact check report using OpenAI based on the fact check results."""
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try:
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@ -55,7 +56,7 @@ Ensure all URLs in sources are complete (including https:// if missing) and each
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2. Specify verification dates when available
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3. Name the fact-checking organizations involved
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4. Describe the verification process"""
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else:
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system_prompt = base_system_prompt
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user_prompt = f"""Query: {query}
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@ -70,37 +71,34 @@ Ensure all URLs in sources are complete (including https:// if missing) and each
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4. Note any conflicting information between sources"""
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response = await openai_client.generate_text_response(
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system_prompt=system_prompt,
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user_prompt=user_prompt,
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max_tokens=1000
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system_prompt=system_prompt, user_prompt=user_prompt, max_tokens=1000
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)
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try:
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# First try to parse the response directly
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response_data = response["response"]
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# Clean up sources before validation
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if isinstance(response_data.get('sources'), list):
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if isinstance(response_data.get("sources"), list):
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cleaned_sources = []
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for source in response_data['sources']:
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for source in response_data["sources"]:
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if isinstance(source, str):
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# Convert string sources to Source objects
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url = source if source.startswith('http') else f"https://{source}"
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cleaned_sources.append({
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"url": url,
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"name": source
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})
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url = (
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source if source.startswith("http") else f"https://{source}"
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)
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cleaned_sources.append({"url": url, "name": source})
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elif isinstance(source, dict):
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# Ensure URL has proper scheme
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url = source.get('url', '')
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if url and not url.startswith('http'):
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source['url'] = f"https://{url}"
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url = source.get("url", "")
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if url and not url.startswith("http"):
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source["url"] = f"https://{url}"
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cleaned_sources.append(source)
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response_data['sources'] = cleaned_sources
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response_data["sources"] = cleaned_sources
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fact_check_response = FactCheckResponse(**response_data)
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return fact_check_response
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except Exception as validation_error:
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print(f"Response validation error: {str(validation_error)}")
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raise HTTPException(
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@ -108,10 +106,10 @@ Ensure all URLs in sources are complete (including https:// if missing) and each
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detail=ErrorResponse(
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detail=f"Invalid response format: {str(validation_error)}",
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error_code="VALIDATION_ERROR",
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path="/check-facts"
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).dict()
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path="/check-facts",
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).dict(),
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)
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except Exception as e:
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print(f"Error generating fact report: {str(e)}")
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raise HTTPException(
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@ -119,10 +117,11 @@ Ensure all URLs in sources are complete (including https:// if missing) and each
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detail=ErrorResponse(
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detail="Error generating fact report",
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error_code="FACT_CHECK_ERROR",
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path="/check-facts"
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).dict()
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path="/check-facts",
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).dict(),
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)
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@fact_check_router.post("/check-facts", response_model=FactCheckResponse)
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async def check_facts(request: FactCheckRequest):
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"""
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@ -134,52 +133,49 @@ async def check_facts(request: FactCheckRequest):
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detail=ErrorResponse(
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detail="Google API key or base URL is not configured",
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error_code="CONFIGURATION_ERROR",
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path="/check-facts"
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).dict()
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path="/check-facts",
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).dict(),
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)
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headers = {"Content-Type": "application/json"}
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async with httpx.AsyncClient() as client:
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# Get fact checker sources from the centralized configuration
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fact_checker_sources = get_all_sources()
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for source in fact_checker_sources:
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params = {
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"key": GOOGLE_API_KEY,
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"query": request.query,
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"languageCode": "en-US",
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"reviewPublisherSiteFilter": source.domain,
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"pageSize": 10
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"pageSize": 10,
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}
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try:
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response = await client.get(
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GOOGLE_FACT_CHECK_BASE_URL,
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params=params,
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headers=headers
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GOOGLE_FACT_CHECK_BASE_URL, params=params, headers=headers
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)
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response.raise_for_status()
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json_response = response.json()
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if json_response.get("claims"):
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return await generate_fact_report(request.query, json_response)
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except httpx.RequestError as e:
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print(f"Error fetching results for site {source.domain}: {str(e)}")
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continue
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except Exception as e:
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print(f"Unexpected error for site {source.domain}: {str(e)}")
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continue
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try:
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search_request = SearchRequest(
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search_text=request.query,
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source_types=["fact_checkers"]
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search_text=request.query, source_types=["fact_checkers"]
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)
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ai_response = await search_websites(search_request)
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return await generate_fact_report(request.query, ai_response)
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except Exception as e:
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print(f"Error in AI fact check: {str(e)}")
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raise HTTPException(
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@ -187,6 +183,6 @@ async def check_facts(request: FactCheckRequest):
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detail=ErrorResponse(
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detail="No fact check results found",
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error_code="NOT_FOUND",
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path="/check-facts"
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).dict()
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)
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path="/check-facts",
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).dict(),
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)
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@ -7,7 +7,7 @@ from pydantic import BaseModel
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from app.models.ai_fact_check_models import (
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AIFactCheckRequest,
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FactCheckSource,
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SourceType
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SourceType,
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)
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from app.websites.fact_checker_website import SOURCES, get_all_sources
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from app.api.ai_fact_check import ai_fact_check
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@ -18,10 +18,10 @@ class SearchRequest(BaseModel):
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search_text: str
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source_types: List[str] = ["fact_checkers"]
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# Configure logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
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)
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logger = logging.getLogger(__name__)
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@ -38,51 +38,58 @@ def get_domain_from_url(url: str) -> str:
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try:
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parsed = urlparse(url)
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domain = parsed.netloc.lower()
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if domain.startswith('www.'):
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if domain.startswith("www."):
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domain = domain[4:]
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return domain
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except Exception as e:
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logger.error(f"Error extracting domain from URL {url}: {str(e)}")
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return ""
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def is_valid_source_domain(domain: str, sources: List[FactCheckSource]) -> bool:
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"""Check if domain matches any source with improved matching logic."""
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if not domain:
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return False
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domain = domain.lower()
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if domain.startswith('www.'):
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if domain.startswith("www."):
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domain = domain[4:]
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for source in sources:
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source_domain = source.domain.lower()
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if source_domain.startswith('www.'):
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if source_domain.startswith("www."):
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source_domain = source_domain[4:]
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if domain == source_domain or domain.endswith('.' + source_domain):
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if domain == source_domain or domain.endswith("." + source_domain):
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return True
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return False
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async def build_enhanced_search_query(query: str, sources: List[FactCheckSource]) -> str:
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async def build_enhanced_search_query(
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query: str, sources: List[FactCheckSource]
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) -> str:
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"""Build search query with site restrictions."""
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site_queries = [f"site:{source.domain}" for source in sources]
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site_restriction = " OR ".join(site_queries)
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return f"({query}) ({site_restriction})"
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async def google_custom_search(query: str, sources: List[FactCheckSource], page: int = 1) -> Optional[Dict]:
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async def google_custom_search(
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query: str, sources: List[FactCheckSource], page: int = 1
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) -> Optional[Dict]:
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"""Perform Google Custom Search with enhanced query."""
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enhanced_query = await build_enhanced_search_query(query, sources)
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start_index = ((page - 1) * RESULTS_PER_PAGE) + 1
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params = {
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"key": GOOGLE_API_KEY,
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"cx": GOOGLE_ENGINE_ID,
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"q": enhanced_query,
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"num": RESULTS_PER_PAGE,
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"start": start_index
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"start": start_index,
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}
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async with httpx.AsyncClient(timeout=30.0) as client:
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try:
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response = await client.get(GOOGLE_SEARCH_URL, params=params)
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@ -92,69 +99,70 @@ async def google_custom_search(query: str, sources: List[FactCheckSource], page:
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logger.error(f"Search error: {str(e)}")
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raise HTTPException(status_code=500, detail=f"Search error: {str(e)}")
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@scrap_websites_router.post("/search")
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async def search_websites(request: SearchRequest):
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# Get the source types from the request
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source_types = request.source_types if request.source_types else ["fact_checkers"]
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# Get sources based on requested types
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selected_sources = []
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for source_type in source_types:
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if source_type in SOURCES:
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selected_sources.extend(SOURCES[source_type])
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# If no valid sources found, use fact checkers as default
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if not selected_sources:
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selected_sources = SOURCES["fact_checkers"]
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all_urls = []
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domain_results = {}
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try:
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for page in range(1, MAX_PAGES + 1):
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if len(all_urls) >= 50:
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break
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search_response = await google_custom_search(request.search_text, selected_sources, page)
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search_response = await google_custom_search(
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request.search_text, selected_sources, page
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)
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if not search_response or not search_response.get("items"):
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break
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for item in search_response.get("items", []):
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url = item.get("link")
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if not url:
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continue
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domain = get_domain_from_url(url)
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if is_valid_source_domain(domain, selected_sources):
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if domain not in domain_results:
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domain_results[domain] = []
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if len(domain_results[domain]) < MAX_URLS_PER_DOMAIN:
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domain_results[domain].append({
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"url": url,
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"title": item.get("title", ""),
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"snippet": item.get("snippet", "")
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})
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domain_results[domain].append(
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{
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"url": url,
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"title": item.get("title", ""),
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"snippet": item.get("snippet", ""),
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}
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)
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all_urls.append(url)
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if len(all_urls) >= 50:
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break
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if not all_urls:
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return {
|
||||
"status": "no_results",
|
||||
"urls_found": 0
|
||||
}
|
||||
|
||||
return {"status": "no_results", "urls_found": 0}
|
||||
|
||||
fact_check_request = AIFactCheckRequest(
|
||||
content=request.search_text,
|
||||
urls=all_urls[:5]
|
||||
content=request.search_text, urls=all_urls[:5]
|
||||
)
|
||||
|
||||
|
||||
return await ai_fact_check(fact_check_request)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error during search/fact-check process: {str(e)}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
|
|
|||
|
|
@ -4,9 +4,9 @@ from dotenv import load_dotenv
|
|||
load_dotenv()
|
||||
|
||||
GOOGLE_API_KEY = os.environ["GOOGLE_API_KEY"]
|
||||
GOOGLE_FACT_CHECK_BASE_URL= os.environ["GOOGLE_FACT_CHECK_BASE_URL"]
|
||||
GOOGLE_FACT_CHECK_BASE_URL = os.environ["GOOGLE_FACT_CHECK_BASE_URL"]
|
||||
GOOGLE_ENGINE_ID = os.environ["GOOGLE_ENGINE_ID"]
|
||||
GOOGLE_SEARCH_URL = os.environ["GOOGLE_SEARCH_URL"]
|
||||
|
||||
OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
|
||||
FRONTEND_URL = os.environ["FRONTEND_URL"]
|
||||
FRONTEND_URL = os.environ["FRONTEND_URL"]
|
||||
|
|
|
|||
Binary file not shown.
|
|
@ -4,38 +4,46 @@ from enum import Enum
|
|||
from datetime import datetime
|
||||
from urllib.parse import urlparse
|
||||
|
||||
|
||||
# Common Models
|
||||
class TokenUsage(BaseModel):
|
||||
prompt_tokens: Optional[int] = 0
|
||||
completion_tokens: Optional[int] = 0
|
||||
total_tokens: Optional[int] = 0
|
||||
|
||||
|
||||
class ErrorResponse(BaseModel):
|
||||
detail: str
|
||||
error_code: str = Field(..., description="Unique error code for this type of error")
|
||||
timestamp: str = Field(default_factory=lambda: datetime.now().isoformat())
|
||||
path: Optional[str] = Field(None, description="The endpoint path where error occurred")
|
||||
path: Optional[str] = Field(
|
||||
None, description="The endpoint path where error occurred"
|
||||
)
|
||||
|
||||
model_config = ConfigDict(json_schema_extra={
|
||||
"example": {
|
||||
"detail": "Error description",
|
||||
"error_code": "ERROR_CODE",
|
||||
"timestamp": "2024-12-09T16:49:30.905765",
|
||||
"path": "/check-facts"
|
||||
model_config = ConfigDict(
|
||||
json_schema_extra={
|
||||
"example": {
|
||||
"detail": "Error description",
|
||||
"error_code": "ERROR_CODE",
|
||||
"timestamp": "2024-12-09T16:49:30.905765",
|
||||
"path": "/check-facts",
|
||||
}
|
||||
}
|
||||
})
|
||||
)
|
||||
|
||||
|
||||
# Fact Check Models
|
||||
class Publisher(BaseModel):
|
||||
name: str
|
||||
site: Optional[str] = Field(None, description="Publisher's website")
|
||||
|
||||
@validator('site')
|
||||
|
||||
@validator("site")
|
||||
def validate_site(cls, v):
|
||||
if v and not (v.startswith('http://') or v.startswith('https://')):
|
||||
if v and not (v.startswith("http://") or v.startswith("https://")):
|
||||
return f"https://{v}"
|
||||
return v
|
||||
|
||||
|
||||
class ClaimReview(BaseModel):
|
||||
publisher: Publisher
|
||||
url: Optional[HttpUrl] = None
|
||||
|
|
@ -44,21 +52,25 @@ class ClaimReview(BaseModel):
|
|||
textualRating: Optional[str] = None
|
||||
languageCode: str = Field(default="en-US")
|
||||
|
||||
|
||||
class Claim(BaseModel):
|
||||
text: str
|
||||
claimant: Optional[str] = None
|
||||
claimDate: Optional[str] = None
|
||||
claimReview: List[ClaimReview]
|
||||
|
||||
|
||||
class SourceType(str, Enum):
|
||||
FACT_CHECKER = "fact_checker"
|
||||
NEWS_SITE = "news_site"
|
||||
|
||||
|
||||
class FactCheckSource(BaseModel):
|
||||
domain: str
|
||||
type: SourceType
|
||||
priority: int = Field(default=1, ge=1, le=10)
|
||||
|
||||
|
||||
# Verification Models
|
||||
class VerificationResult(BaseModel):
|
||||
verdict: str = Field(..., description="True/False/Insufficient Information")
|
||||
|
|
@ -67,54 +79,56 @@ class VerificationResult(BaseModel):
|
|||
reasoning: str
|
||||
missing_info: Optional[str] = None
|
||||
|
||||
model_config = ConfigDict(json_schema_extra={
|
||||
"example": {
|
||||
"verdict": "True",
|
||||
"confidence": "High",
|
||||
"evidence": ["Direct quote from source supporting the claim"],
|
||||
"reasoning": "Detailed analysis of why the claim is considered true",
|
||||
"missing_info": "Any caveats or limitations of the verification"
|
||||
model_config = ConfigDict(
|
||||
json_schema_extra={
|
||||
"example": {
|
||||
"verdict": "True",
|
||||
"confidence": "High",
|
||||
"evidence": ["Direct quote from source supporting the claim"],
|
||||
"reasoning": "Detailed analysis of why the claim is considered true",
|
||||
"missing_info": "Any caveats or limitations of the verification",
|
||||
}
|
||||
}
|
||||
})
|
||||
)
|
||||
|
||||
|
||||
# Request Models
|
||||
class BaseFactCheckRequest(BaseModel):
|
||||
content: str = Field(
|
||||
...,
|
||||
min_length=10,
|
||||
max_length=1000,
|
||||
description="The claim to be fact-checked"
|
||||
..., min_length=10, max_length=1000, description="The claim to be fact-checked"
|
||||
)
|
||||
|
||||
@validator('content')
|
||||
|
||||
@validator("content")
|
||||
def validate_content(cls, v):
|
||||
if not v.strip():
|
||||
raise ValueError("Content cannot be empty or just whitespace")
|
||||
return v.strip()
|
||||
|
||||
|
||||
class GoogleFactCheckRequest(BaseFactCheckRequest):
|
||||
language: str = Field(default="en-US", pattern="^[a-z]{2}-[A-Z]{2}$")
|
||||
max_results_per_source: int = Field(default=10, ge=1, le=50)
|
||||
|
||||
|
||||
class AIFactCheckRequest(BaseFactCheckRequest):
|
||||
urls: List[str] = Field(
|
||||
...,
|
||||
min_items=1,
|
||||
max_items=5,
|
||||
description="List of URLs to check the content against. URLs will be prefixed with https:// if protocol is missing"
|
||||
description="List of URLs to check the content against. URLs will be prefixed with https:// if protocol is missing",
|
||||
)
|
||||
|
||||
@validator('urls')
|
||||
|
||||
@validator("urls")
|
||||
def validate_urls(cls, urls):
|
||||
validated_urls = []
|
||||
for url in urls:
|
||||
if not url.strip():
|
||||
raise ValueError("URL cannot be empty")
|
||||
|
||||
|
||||
# Add https:// if no protocol specified
|
||||
if not url.startswith(('http://', 'https://')):
|
||||
url = f'https://{url}'
|
||||
|
||||
if not url.startswith(("http://", "https://")):
|
||||
url = f"https://{url}"
|
||||
|
||||
try:
|
||||
result = urlparse(url)
|
||||
if not result.netloc:
|
||||
|
|
@ -122,18 +136,21 @@ class AIFactCheckRequest(BaseFactCheckRequest):
|
|||
validated_urls.append(url)
|
||||
except Exception as e:
|
||||
raise ValueError(f"Invalid URL {url}: {str(e)}")
|
||||
|
||||
|
||||
return validated_urls
|
||||
|
||||
model_config = ConfigDict(json_schema_extra={
|
||||
"example": {
|
||||
"content": "Indian flag was drawn in BUET campus",
|
||||
"urls": [
|
||||
"www.altnews.in/article-about-flag",
|
||||
"www.another-source.com/related-news"
|
||||
]
|
||||
model_config = ConfigDict(
|
||||
json_schema_extra={
|
||||
"example": {
|
||||
"content": "Indian flag was drawn in BUET campus",
|
||||
"urls": [
|
||||
"www.altnews.in/article-about-flag",
|
||||
"www.another-source.com/related-news",
|
||||
],
|
||||
}
|
||||
}
|
||||
})
|
||||
)
|
||||
|
||||
|
||||
# Response Models
|
||||
class BaseFactCheckResponse(BaseModel):
|
||||
|
|
@ -141,17 +158,20 @@ class BaseFactCheckResponse(BaseModel):
|
|||
token_usage: TokenUsage
|
||||
sources: List[str]
|
||||
|
||||
model_config = ConfigDict(json_schema_extra={
|
||||
"example": {
|
||||
"query": "Example statement to verify",
|
||||
"token_usage": {
|
||||
"prompt_tokens": 100,
|
||||
"completion_tokens": 50,
|
||||
"total_tokens": 150
|
||||
},
|
||||
"sources": ["source1.com", "source2.com"],
|
||||
model_config = ConfigDict(
|
||||
json_schema_extra={
|
||||
"example": {
|
||||
"query": "Example statement to verify",
|
||||
"token_usage": {
|
||||
"prompt_tokens": 100,
|
||||
"completion_tokens": 50,
|
||||
"total_tokens": 150,
|
||||
},
|
||||
"sources": ["source1.com", "source2.com"],
|
||||
}
|
||||
}
|
||||
})
|
||||
)
|
||||
|
||||
|
||||
class GoogleFactCheckResponse(BaseFactCheckResponse):
|
||||
total_claims_found: int
|
||||
|
|
@ -159,71 +179,80 @@ class GoogleFactCheckResponse(BaseFactCheckResponse):
|
|||
verification_result: Dict[str, Any]
|
||||
summary: Dict[str, int]
|
||||
|
||||
model_config = ConfigDict(json_schema_extra={
|
||||
"example": {
|
||||
"query": "Example claim",
|
||||
"total_claims_found": 1,
|
||||
"results": [{
|
||||
"text": "Example claim text",
|
||||
"claimant": "Source name",
|
||||
"claimReview": [{
|
||||
"publisher": {
|
||||
"name": "Fact Checker",
|
||||
"site": "factchecker.com"
|
||||
},
|
||||
"textualRating": "True"
|
||||
}]
|
||||
}],
|
||||
"verification_result": {
|
||||
"verdict": "True",
|
||||
"confidence": "High",
|
||||
"evidence": ["Supporting evidence"],
|
||||
"reasoning": "Detailed analysis"
|
||||
},
|
||||
"sources": ["factchecker.com"],
|
||||
"token_usage": {
|
||||
"prompt_tokens": 100,
|
||||
"completion_tokens": 50,
|
||||
"total_tokens": 150
|
||||
},
|
||||
"summary": {
|
||||
"total_sources": 1,
|
||||
"fact_checking_sites_queried": 10
|
||||
}
|
||||
}
|
||||
})
|
||||
|
||||
class AIFactCheckResponse(BaseFactCheckResponse):
|
||||
verification_result: Dict[str, VerificationResult] # Changed to Dict to store results per URL
|
||||
|
||||
model_config = ConfigDict(json_schema_extra={
|
||||
"example": {
|
||||
"query": "Indian flag was drawn in BUET campus",
|
||||
"verification_result": {
|
||||
"https://www.source1.com": {
|
||||
model_config = ConfigDict(
|
||||
json_schema_extra={
|
||||
"example": {
|
||||
"query": "Example claim",
|
||||
"total_claims_found": 1,
|
||||
"results": [
|
||||
{
|
||||
"text": "Example claim text",
|
||||
"claimant": "Source name",
|
||||
"claimReview": [
|
||||
{
|
||||
"publisher": {
|
||||
"name": "Fact Checker",
|
||||
"site": "factchecker.com",
|
||||
},
|
||||
"textualRating": "True",
|
||||
}
|
||||
],
|
||||
}
|
||||
],
|
||||
"verification_result": {
|
||||
"verdict": "True",
|
||||
"confidence": "High",
|
||||
"evidence": ["Supporting evidence from source 1"],
|
||||
"reasoning": "Detailed analysis from source 1",
|
||||
"missing_info": None
|
||||
"evidence": ["Supporting evidence"],
|
||||
"reasoning": "Detailed analysis",
|
||||
},
|
||||
"https://www.source2.com": {
|
||||
"verdict": "True",
|
||||
"confidence": "Medium",
|
||||
"evidence": ["Supporting evidence from source 2"],
|
||||
"reasoning": "Analysis from source 2",
|
||||
"missing_info": "Additional context needed"
|
||||
}
|
||||
},
|
||||
"sources": ["source1.com", "source2.com"],
|
||||
"token_usage": {
|
||||
"prompt_tokens": 200,
|
||||
"completion_tokens": 100,
|
||||
"total_tokens": 300
|
||||
"sources": ["factchecker.com"],
|
||||
"token_usage": {
|
||||
"prompt_tokens": 100,
|
||||
"completion_tokens": 50,
|
||||
"total_tokens": 150,
|
||||
},
|
||||
"summary": {"total_sources": 1, "fact_checking_sites_queried": 10},
|
||||
}
|
||||
}
|
||||
})
|
||||
)
|
||||
|
||||
|
||||
class AIFactCheckResponse(BaseFactCheckResponse):
|
||||
verification_result: Dict[
|
||||
str, VerificationResult
|
||||
] # Changed to Dict to store results per URL
|
||||
|
||||
model_config = ConfigDict(
|
||||
json_schema_extra={
|
||||
"example": {
|
||||
"query": "Indian flag was drawn in BUET campus",
|
||||
"verification_result": {
|
||||
"https://www.source1.com": {
|
||||
"verdict": "True",
|
||||
"confidence": "High",
|
||||
"evidence": ["Supporting evidence from source 1"],
|
||||
"reasoning": "Detailed analysis from source 1",
|
||||
"missing_info": None,
|
||||
},
|
||||
"https://www.source2.com": {
|
||||
"verdict": "True",
|
||||
"confidence": "Medium",
|
||||
"evidence": ["Supporting evidence from source 2"],
|
||||
"reasoning": "Analysis from source 2",
|
||||
"missing_info": "Additional context needed",
|
||||
},
|
||||
},
|
||||
"sources": ["source1.com", "source2.com"],
|
||||
"token_usage": {
|
||||
"prompt_tokens": 200,
|
||||
"completion_tokens": 100,
|
||||
"total_tokens": 300,
|
||||
},
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
# Backwards compatibility aliases
|
||||
FactCheckRequest = GoogleFactCheckRequest
|
||||
FactCheckResponse = GoogleFactCheckResponse
|
||||
FactCheckResponse = GoogleFactCheckResponse
|
||||
|
|
|
|||
|
|
@ -3,74 +3,73 @@ from typing import List, Literal, Union
|
|||
from datetime import datetime
|
||||
from enum import Enum
|
||||
|
||||
|
||||
class VerdictEnum(str, Enum):
|
||||
TRUE = "True"
|
||||
FALSE = "False"
|
||||
PARTIALLY_TRUE = "Partially True"
|
||||
UNVERIFIED = "Unverified"
|
||||
|
||||
|
||||
class ConfidenceEnum(str, Enum):
|
||||
HIGH = "High"
|
||||
MEDIUM = "Medium"
|
||||
LOW = "Low"
|
||||
|
||||
|
||||
class FactCheckRequest(BaseModel):
|
||||
query: str = Field(
|
||||
...,
|
||||
min_length=3,
|
||||
max_length=500,
|
||||
description="The claim or statement to be fact-checked",
|
||||
example="Did NASA confirm finding alien structures on Mars in 2024?"
|
||||
example="Did NASA confirm finding alien structures on Mars in 2024?",
|
||||
)
|
||||
|
||||
|
||||
class Source(BaseModel):
|
||||
url: str
|
||||
name: str = ""
|
||||
|
||||
@validator('url')
|
||||
@validator("url")
|
||||
def validate_url(cls, v):
|
||||
# Basic URL validation without requiring HTTP/HTTPS
|
||||
if not v or len(v) < 3:
|
||||
raise ValueError("URL must not be empty and must be at least 3 characters")
|
||||
return v
|
||||
|
||||
|
||||
class FactCheckResponse(BaseModel):
|
||||
claim: str = Field(
|
||||
...,
|
||||
min_length=10,
|
||||
max_length=1000,
|
||||
description="The exact claim being verified"
|
||||
)
|
||||
verdict: VerdictEnum = Field(
|
||||
...,
|
||||
description="The verification verdict"
|
||||
description="The exact claim being verified",
|
||||
)
|
||||
verdict: VerdictEnum = Field(..., description="The verification verdict")
|
||||
confidence: ConfidenceEnum = Field(
|
||||
...,
|
||||
description="Confidence level in the verdict"
|
||||
..., description="Confidence level in the verdict"
|
||||
)
|
||||
sources: List[Source] = Field(
|
||||
...,
|
||||
min_items=1,
|
||||
description="List of sources used in verification"
|
||||
..., min_items=1, description="List of sources used in verification"
|
||||
)
|
||||
evidence: str = Field(
|
||||
...,
|
||||
min_length=20,
|
||||
max_length=500,
|
||||
description="Concise summary of key evidence"
|
||||
description="Concise summary of key evidence",
|
||||
)
|
||||
explanation: str = Field(
|
||||
...,
|
||||
min_length=50,
|
||||
max_length=1000,
|
||||
description="Detailed explanation of verification findings"
|
||||
description="Detailed explanation of verification findings",
|
||||
)
|
||||
additional_context: str = Field(
|
||||
...,
|
||||
min_length=20,
|
||||
max_length=500,
|
||||
description="Important context about the verification"
|
||||
description="Important context about the verification",
|
||||
)
|
||||
|
||||
class Config:
|
||||
|
|
@ -82,20 +81,21 @@ class FactCheckResponse(BaseModel):
|
|||
"sources": [
|
||||
{
|
||||
"url": "https://www.nasa.gov/mars-exploration",
|
||||
"name": "NASA Mars Exploration"
|
||||
"name": "NASA Mars Exploration",
|
||||
},
|
||||
{
|
||||
"url": "https://factcheck.org/2024/mars-claims",
|
||||
"name": "FactCheck.org"
|
||||
}
|
||||
"name": "FactCheck.org",
|
||||
},
|
||||
],
|
||||
"evidence": "NASA has made no such announcement. Recent Mars rover images show natural rock formations.",
|
||||
"explanation": "Multiple fact-checking organizations investigated this claim. NASA's official communications and Mars mission reports from 2024 contain no mention of alien structures. The viral images being shared are misidentified natural geological formations.",
|
||||
"additional_context": "Similar false claims about alien structures on Mars have circulated periodically since the first Mars rovers began sending back images."
|
||||
"additional_context": "Similar false claims about alien structures on Mars have circulated periodically since the first Mars rovers began sending back images.",
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
class ErrorResponse(BaseModel):
|
||||
detail: str
|
||||
error_code: str = Field(..., example="VALIDATION_ERROR")
|
||||
path: str = Field(..., example="/check-facts")
|
||||
path: str = Field(..., example="/check-facts")
|
||||
|
|
|
|||
|
|
@ -1,38 +1,46 @@
|
|||
from pydantic import BaseModel
|
||||
from typing import List, Dict
|
||||
|
||||
|
||||
class SearchRequest(BaseModel):
|
||||
search_text: str
|
||||
source_types: List[str] = ["fact_checkers"]
|
||||
|
||||
|
||||
class Publisher(BaseModel):
|
||||
name: str
|
||||
site: str
|
||||
|
||||
|
||||
class ClaimReview(BaseModel):
|
||||
publisher: Publisher
|
||||
textualRating: str
|
||||
|
||||
|
||||
class Claim(BaseModel):
|
||||
claimReview: List[ClaimReview]
|
||||
claimant: str
|
||||
text: str
|
||||
|
||||
|
||||
class Summary(BaseModel):
|
||||
fact_checking_sites_queried: int
|
||||
total_sources: int
|
||||
|
||||
|
||||
class TokenUsage(BaseModel):
|
||||
prompt_tokens: int
|
||||
completion_tokens: int
|
||||
total_tokens: int
|
||||
|
||||
|
||||
class VerificationResult(BaseModel):
|
||||
verdict: str
|
||||
confidence: str
|
||||
evidence: List[str]
|
||||
reasoning: str
|
||||
|
||||
|
||||
class EnhancedFactCheckResponse(BaseModel):
|
||||
query: str
|
||||
results: List[Claim]
|
||||
|
|
@ -40,4 +48,4 @@ class EnhancedFactCheckResponse(BaseModel):
|
|||
summary: Summary
|
||||
token_usage: Dict[str, int]
|
||||
total_claims_found: int
|
||||
verification_result: VerificationResult
|
||||
verification_result: VerificationResult
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ import json
|
|||
import aiohttp
|
||||
from bs4 import BeautifulSoup
|
||||
|
||||
|
||||
class OpenAIClient:
|
||||
def __init__(self, api_key: str):
|
||||
"""
|
||||
|
|
@ -16,7 +17,9 @@ class OpenAIClient:
|
|||
"""
|
||||
openai.api_key = api_key
|
||||
|
||||
async def generate_text_response(self, system_prompt: str, user_prompt: str, max_tokens: int) -> dict:
|
||||
async def generate_text_response(
|
||||
self, system_prompt: str, user_prompt: str, max_tokens: int
|
||||
) -> dict:
|
||||
"""
|
||||
Generate a response using OpenAI's chat completion API.
|
||||
"""
|
||||
|
|
@ -25,19 +28,19 @@ class OpenAIClient:
|
|||
model="gpt-4",
|
||||
messages=[
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": user_prompt}
|
||||
{"role": "user", "content": user_prompt},
|
||||
],
|
||||
max_tokens=max_tokens
|
||||
max_tokens=max_tokens,
|
||||
)
|
||||
content = response['choices'][0]['message']['content']
|
||||
content = response["choices"][0]["message"]["content"]
|
||||
# Parse the JSON string into a dictionary
|
||||
parsed_content = json.loads(content)
|
||||
|
||||
|
||||
return {
|
||||
"response": parsed_content, # Now returns a dictionary instead of string
|
||||
"prompt_tokens": response['usage']['prompt_tokens'],
|
||||
"completion_tokens": response['usage']['completion_tokens'],
|
||||
"total_tokens": response['usage']['total_tokens']
|
||||
"prompt_tokens": response["usage"]["prompt_tokens"],
|
||||
"completion_tokens": response["usage"]["completion_tokens"],
|
||||
"total_tokens": response["usage"]["total_tokens"],
|
||||
}
|
||||
except json.JSONDecodeError as e:
|
||||
raise Exception(f"Failed to parse OpenAI response as JSON: {str(e)}")
|
||||
|
|
@ -50,14 +53,14 @@ class OpenAIClient:
|
|||
"""
|
||||
try:
|
||||
response = openai.Embedding.create(
|
||||
input=texts,
|
||||
model="text-embedding-ada-002"
|
||||
input=texts, model="text-embedding-ada-002"
|
||||
)
|
||||
embeddings = [data['embedding'] for data in response['data']]
|
||||
embeddings = [data["embedding"] for data in response["data"]]
|
||||
return embeddings
|
||||
except Exception as e:
|
||||
raise Exception(f"OpenAI embedding error: {str(e)}")
|
||||
|
||||
|
||||
class AIFactChecker:
|
||||
def __init__(self, openai_client: OpenAIClient):
|
||||
"""Initialize the fact checker with OpenAI client."""
|
||||
|
|
@ -66,65 +69,71 @@ class AIFactChecker:
|
|||
chunk_size=1000,
|
||||
chunk_overlap=200,
|
||||
length_function=len,
|
||||
separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""]
|
||||
separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""],
|
||||
)
|
||||
|
||||
|
||||
async def scrape_webpage(self, url: str) -> List[Document]:
|
||||
"""Scrape webpage content without saving HTML files."""
|
||||
try:
|
||||
async with aiohttp.ClientSession() as session:
|
||||
async with session.get(url) as response:
|
||||
if response.status != 200:
|
||||
raise Exception(f"Failed to fetch URL: {url}, status: {response.status}")
|
||||
|
||||
raise Exception(
|
||||
f"Failed to fetch URL: {url}, status: {response.status}"
|
||||
)
|
||||
|
||||
html_content = await response.text()
|
||||
|
||||
|
||||
# Parse HTML with BeautifulSoup
|
||||
soup = BeautifulSoup(html_content, 'html.parser')
|
||||
|
||||
soup = BeautifulSoup(html_content, "html.parser")
|
||||
|
||||
# Create a Document with the parsed content
|
||||
doc = Document(
|
||||
page_content=soup.get_text(separator='\n', strip=True),
|
||||
metadata={"source": url}
|
||||
page_content=soup.get_text(separator="\n", strip=True),
|
||||
metadata={"source": url},
|
||||
)
|
||||
|
||||
|
||||
# Split into chunks
|
||||
docs_chunks = self.text_splitter.split_documents([doc])
|
||||
|
||||
logger.info(f"Successfully scraped webpage | chunks={len(docs_chunks)}")
|
||||
|
||||
logger.info(
|
||||
f"Successfully scraped webpage | chunks={len(docs_chunks)}"
|
||||
)
|
||||
return docs_chunks
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error scraping webpage | url={url} | error={str(e)}")
|
||||
raise
|
||||
|
||||
def find_relevant_chunks(
|
||||
self,
|
||||
query_embedding: List[float],
|
||||
doc_embeddings: List[List[float]],
|
||||
docs: List[Document]
|
||||
self,
|
||||
query_embedding: List[float],
|
||||
doc_embeddings: List[List[float]],
|
||||
docs: List[Document],
|
||||
) -> List[Document]:
|
||||
"""Find most relevant document chunks using cosine similarity."""
|
||||
try:
|
||||
query_array = np.array(query_embedding)
|
||||
chunks_array = np.array(doc_embeddings)
|
||||
|
||||
|
||||
similarities = np.dot(chunks_array, query_array) / (
|
||||
np.linalg.norm(chunks_array, axis=1) * np.linalg.norm(query_array)
|
||||
)
|
||||
|
||||
|
||||
top_indices = np.argsort(similarities)[-5:][::-1]
|
||||
return [docs[i] for i in top_indices]
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error finding relevant chunks | error={str(e)}")
|
||||
raise
|
||||
|
||||
async def verify_fact(self, query: str, relevant_docs: List[Document]) -> Dict[str, Any]:
|
||||
async def verify_fact(
|
||||
self, query: str, relevant_docs: List[Document]
|
||||
) -> Dict[str, Any]:
|
||||
"""Verify fact using OpenAI's API with context from relevant documents."""
|
||||
try:
|
||||
context = "\n\n".join([doc.page_content for doc in relevant_docs])
|
||||
|
||||
|
||||
system_prompt = """You are a professional fact-checking assistant. Analyze the provided context
|
||||
and determine if the given statement is true, false, or if there isn't enough information.
|
||||
|
||||
|
|
@ -136,32 +145,37 @@ class AIFactChecker:
|
|||
"reasoning": "Your detailed analysis and reasoning",
|
||||
"missing_info": "Any important missing information (if applicable)"
|
||||
}"""
|
||||
|
||||
|
||||
user_prompt = f"""Context:
|
||||
{context}
|
||||
|
||||
Statement to verify: "{query}"
|
||||
|
||||
Analyze the statement based on the provided context and return your response in the specified JSON format."""
|
||||
|
||||
|
||||
response = await self.openai_client.generate_text_response(
|
||||
system_prompt=system_prompt,
|
||||
user_prompt=user_prompt,
|
||||
max_tokens=800
|
||||
system_prompt=system_prompt, user_prompt=user_prompt, max_tokens=800
|
||||
)
|
||||
|
||||
sources = list(set([doc.metadata.get('source', 'Unknown source') for doc in relevant_docs]))
|
||||
|
||||
|
||||
sources = list(
|
||||
set(
|
||||
[
|
||||
doc.metadata.get("source", "Unknown source")
|
||||
for doc in relevant_docs
|
||||
]
|
||||
)
|
||||
)
|
||||
|
||||
return {
|
||||
"verification_result": response["response"], # This is now a dictionary
|
||||
"sources": sources,
|
||||
"token_usage": {
|
||||
"prompt_tokens": response["prompt_tokens"],
|
||||
"completion_tokens": response["completion_tokens"],
|
||||
"total_tokens": response["total_tokens"]
|
||||
}
|
||||
"total_tokens": response["total_tokens"],
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error verifying fact | error={str(e)}")
|
||||
raise
|
||||
|
|
@ -170,16 +184,18 @@ class AIFactChecker:
|
|||
"""Main method to check a fact against a webpage."""
|
||||
try:
|
||||
docs = await self.scrape_webpage(url)
|
||||
|
||||
|
||||
doc_texts = [doc.page_content for doc in docs]
|
||||
doc_embeddings = self.openai_client.get_embeddings(doc_texts)
|
||||
query_embedding = self.openai_client.get_embeddings([query])
|
||||
|
||||
relevant_docs = self.find_relevant_chunks(query_embedding[0], doc_embeddings, docs)
|
||||
|
||||
relevant_docs = self.find_relevant_chunks(
|
||||
query_embedding[0], doc_embeddings, docs
|
||||
)
|
||||
verification_result = await self.verify_fact(query, relevant_docs)
|
||||
|
||||
|
||||
return verification_result
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error checking fact | error={str(e)}")
|
||||
raise
|
||||
raise
|
||||
|
|
|
|||
Binary file not shown.
|
|
@ -1,120 +1,125 @@
|
|||
from typing import Dict, List
|
||||
import requests
|
||||
from fastapi import HTTPException
|
||||
from app.models.ai_fact_check_models import FactCheckSource, ErrorResponse, FactCheckRequest, SourceType
|
||||
from app.models.ai_fact_check_models import (
|
||||
FactCheckSource,
|
||||
ErrorResponse,
|
||||
FactCheckRequest,
|
||||
SourceType,
|
||||
)
|
||||
|
||||
# Sources configuration with validation
|
||||
SOURCES = {
|
||||
"fact_checkers": [
|
||||
FactCheckSource(domain=domain, type=SourceType.FACT_CHECKER, priority=1)
|
||||
for domain in [
|
||||
"snopes.com",
|
||||
"politifact.com",
|
||||
"factcheck.org",
|
||||
"reuters.com/fact-check",
|
||||
"apnews.com/hub/ap-fact-check",
|
||||
"bbc.com/news/reality_check",
|
||||
"fullfact.org",
|
||||
"afp.com/fact-check",
|
||||
"truthorfiction.com",
|
||||
"leadstories.com",
|
||||
"checkyourfact.com",
|
||||
"washingtonpost.com/news/fact-checker",
|
||||
"factcheck.kz",
|
||||
"poynter.org/ifcn",
|
||||
"factcheckeu.info",
|
||||
"africacheck.org",
|
||||
"thequint.com/webqoof",
|
||||
"altnews.in",
|
||||
"facta.news",
|
||||
"factcheckni.org",
|
||||
"mythdetector.ge",
|
||||
"verificado.mx",
|
||||
"euvsdisinfo.eu",
|
||||
"factcheck.afp.com",
|
||||
"newtral.es",
|
||||
"maldita.es",
|
||||
"faktograf.hr",
|
||||
"demagog.org.pl",
|
||||
"factnameh.com",
|
||||
"faktiskt.se",
|
||||
"teyit.org",
|
||||
"factly.in",
|
||||
"boom.live",
|
||||
"stopfake.org",
|
||||
"factcheck.ge",
|
||||
"factcheck.kg",
|
||||
"factcheck.uz",
|
||||
"factcheck.tj",
|
||||
"factcheck.az",
|
||||
"factcheck.am",
|
||||
"factcheck.md",
|
||||
"verafiles.org",
|
||||
"rappler.com/fact-check",
|
||||
"vera.com.gt",
|
||||
"chequeado.com",
|
||||
"aosfatos.org",
|
||||
"lasillavacia.com/detector-mentiras",
|
||||
"colombiacheck.com",
|
||||
"ecuadorchequea.com",
|
||||
"elsurti.com/checado",
|
||||
"verificat.cat",
|
||||
"mafindo.or.id",
|
||||
"tempo.co/cek-fakta",
|
||||
"factcheck.mk",
|
||||
"raskrinkavanje.ba",
|
||||
"faktograf.hr",
|
||||
"demagog.cz",
|
||||
"faktabaari.fi",
|
||||
"correctiv.org",
|
||||
"mimikama.at",
|
||||
"factcheck.vlaanderen",
|
||||
"factuel.afp.com",
|
||||
"nieuwscheckers.nl",
|
||||
"faktisk.no",
|
||||
"tjekdet.dk",
|
||||
"ellinikahoaxes.gr",
|
||||
"faktograf.id",
|
||||
"stopfake.kz",
|
||||
"pesacheck.org",
|
||||
"dubawa.org",
|
||||
"namibiafactcheck.org.na",
|
||||
"zimfact.org",
|
||||
"ghanafact.com",
|
||||
"factspace.africa",
|
||||
"factcrescendo.com",
|
||||
"vishvasnews.com",
|
||||
"factcheck.lk",
|
||||
"newschecker.in",
|
||||
"boomlive.in",
|
||||
"digiteye.in",
|
||||
"indiatoday.in/fact-check",
|
||||
"factcrescendo.com",
|
||||
"piyasa.com/fact-check",
|
||||
"taiwanese.facts.news",
|
||||
"taiwanfactcheck.com",
|
||||
"mygopen.com",
|
||||
"tfc-taiwan.org.tw",
|
||||
"cofacts.tw",
|
||||
"rumor.taipei",
|
||||
"fact.qq.com",
|
||||
"factcheck.afp.com/list",
|
||||
"acfta.org",
|
||||
"crosscheck.firstdraftnews.org",
|
||||
"healthfeedback.org",
|
||||
"climatefeedback.org",
|
||||
"sciencefeedback.co",
|
||||
"factcheck.aap.com.au",
|
||||
"emergent.info",
|
||||
"hoax-slayer.net",
|
||||
"truthorfiction.com",
|
||||
"factcheck.media",
|
||||
"mediawise.org",
|
||||
"thejournal.ie/factcheck",
|
||||
"journalistsresource.org",
|
||||
"metafact.io",
|
||||
"reporterslab.org/fact-checking"
|
||||
]
|
||||
"snopes.com",
|
||||
"politifact.com",
|
||||
"factcheck.org",
|
||||
"reuters.com/fact-check",
|
||||
"apnews.com/hub/ap-fact-check",
|
||||
"bbc.com/news/reality_check",
|
||||
"fullfact.org",
|
||||
"afp.com/fact-check",
|
||||
"truthorfiction.com",
|
||||
"leadstories.com",
|
||||
"checkyourfact.com",
|
||||
"washingtonpost.com/news/fact-checker",
|
||||
"factcheck.kz",
|
||||
"poynter.org/ifcn",
|
||||
"factcheckeu.info",
|
||||
"africacheck.org",
|
||||
"thequint.com/webqoof",
|
||||
"altnews.in",
|
||||
"facta.news",
|
||||
"factcheckni.org",
|
||||
"mythdetector.ge",
|
||||
"verificado.mx",
|
||||
"euvsdisinfo.eu",
|
||||
"factcheck.afp.com",
|
||||
"newtral.es",
|
||||
"maldita.es",
|
||||
"faktograf.hr",
|
||||
"demagog.org.pl",
|
||||
"factnameh.com",
|
||||
"faktiskt.se",
|
||||
"teyit.org",
|
||||
"factly.in",
|
||||
"boom.live",
|
||||
"stopfake.org",
|
||||
"factcheck.ge",
|
||||
"factcheck.kg",
|
||||
"factcheck.uz",
|
||||
"factcheck.tj",
|
||||
"factcheck.az",
|
||||
"factcheck.am",
|
||||
"factcheck.md",
|
||||
"verafiles.org",
|
||||
"rappler.com/fact-check",
|
||||
"vera.com.gt",
|
||||
"chequeado.com",
|
||||
"aosfatos.org",
|
||||
"lasillavacia.com/detector-mentiras",
|
||||
"colombiacheck.com",
|
||||
"ecuadorchequea.com",
|
||||
"elsurti.com/checado",
|
||||
"verificat.cat",
|
||||
"mafindo.or.id",
|
||||
"tempo.co/cek-fakta",
|
||||
"factcheck.mk",
|
||||
"raskrinkavanje.ba",
|
||||
"faktograf.hr",
|
||||
"demagog.cz",
|
||||
"faktabaari.fi",
|
||||
"correctiv.org",
|
||||
"mimikama.at",
|
||||
"factcheck.vlaanderen",
|
||||
"factuel.afp.com",
|
||||
"nieuwscheckers.nl",
|
||||
"faktisk.no",
|
||||
"tjekdet.dk",
|
||||
"ellinikahoaxes.gr",
|
||||
"faktograf.id",
|
||||
"stopfake.kz",
|
||||
"pesacheck.org",
|
||||
"dubawa.org",
|
||||
"namibiafactcheck.org.na",
|
||||
"zimfact.org",
|
||||
"ghanafact.com",
|
||||
"factspace.africa",
|
||||
"factcrescendo.com",
|
||||
"vishvasnews.com",
|
||||
"factcheck.lk",
|
||||
"newschecker.in",
|
||||
"boomlive.in",
|
||||
"digiteye.in",
|
||||
"indiatoday.in/fact-check",
|
||||
"factcrescendo.com",
|
||||
"piyasa.com/fact-check",
|
||||
"taiwanese.facts.news",
|
||||
"taiwanfactcheck.com",
|
||||
"mygopen.com",
|
||||
"tfc-taiwan.org.tw",
|
||||
"cofacts.tw",
|
||||
"rumor.taipei",
|
||||
"fact.qq.com",
|
||||
"factcheck.afp.com/list",
|
||||
"acfta.org",
|
||||
"crosscheck.firstdraftnews.org",
|
||||
"healthfeedback.org",
|
||||
"climatefeedback.org",
|
||||
"sciencefeedback.co",
|
||||
"factcheck.aap.com.au",
|
||||
"emergent.info",
|
||||
"hoax-slayer.net",
|
||||
"truthorfiction.com",
|
||||
"factcheck.media",
|
||||
"mediawise.org",
|
||||
"thejournal.ie/factcheck",
|
||||
"journalistsresource.org",
|
||||
"metafact.io",
|
||||
"reporterslab.org/fact-checking",
|
||||
]
|
||||
],
|
||||
"news_sites": [
|
||||
FactCheckSource(domain=domain, type=SourceType.NEWS_SITE, priority=2)
|
||||
|
|
@ -133,16 +138,14 @@ SOURCES = {
|
|||
"www.risingbd.com/english",
|
||||
"www.dailyindustry.news",
|
||||
"www.bangladeshpost.net",
|
||||
"www.daily-bangladesh.com/english"
|
||||
"www.daily-bangladesh.com/english",
|
||||
]
|
||||
]
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
async def fetch_fact_checks(
|
||||
api_key: str,
|
||||
base_url: str,
|
||||
query: str,
|
||||
site: FactCheckSource
|
||||
api_key: str, base_url: str, query: str, site: FactCheckSource
|
||||
) -> Dict:
|
||||
"""
|
||||
Fetch fact checks from a specific site using the Google Fact Check API
|
||||
|
|
@ -156,9 +159,9 @@ async def fetch_fact_checks(
|
|||
"query": query,
|
||||
"languageCode": "en-US",
|
||||
"reviewPublisherSiteFilter": site.domain,
|
||||
"pageSize": 10
|
||||
"pageSize": 10,
|
||||
}
|
||||
|
||||
|
||||
response = requests.get(base_url, params=params)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
|
|
@ -168,23 +171,22 @@ async def fetch_fact_checks(
|
|||
detail=ErrorResponse(
|
||||
detail=f"Error fetching from {site.domain}: {str(e)}",
|
||||
error_code="FACT_CHECK_SERVICE_ERROR",
|
||||
path="/check-facts"
|
||||
).dict()
|
||||
path="/check-facts",
|
||||
).dict(),
|
||||
)
|
||||
except ValueError as e:
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=ErrorResponse(
|
||||
detail=str(e),
|
||||
error_code="CONFIGURATION_ERROR",
|
||||
path="/check-facts"
|
||||
).dict()
|
||||
detail=str(e), error_code="CONFIGURATION_ERROR", path="/check-facts"
|
||||
).dict(),
|
||||
)
|
||||
|
||||
|
||||
def get_all_sources() -> List[FactCheckSource]:
|
||||
"""
|
||||
Get all sources sorted by priority
|
||||
"""
|
||||
# all_sources = SOURCES["fact_checkers"] + SOURCES["news_sites"]
|
||||
all_sources = SOURCES["fact_checkers"]
|
||||
return sorted(all_sources, key=lambda x: x.priority)
|
||||
all_sources = SOURCES["fact_checkers"]
|
||||
return sorted(all_sources, key=lambda x: x.priority)
|
||||
|
|
|
|||
10
main.py
10
main.py
|
|
@ -7,9 +7,7 @@ from app.config import FRONTEND_URL
|
|||
|
||||
# Initialize FastAPI app
|
||||
app = FastAPI(
|
||||
title="Your API Title",
|
||||
description="Your API Description",
|
||||
version="1.0.0"
|
||||
title="Your API Title", description="Your API Description", version="1.0.0"
|
||||
)
|
||||
|
||||
# CORS configuration
|
||||
|
|
@ -30,16 +28,19 @@ app.add_middleware(
|
|||
allow_headers=["*"],
|
||||
)
|
||||
|
||||
|
||||
# Basic root endpoint
|
||||
@app.get("/")
|
||||
async def root():
|
||||
return {"message": "Welcome to your FastAPI application"}
|
||||
|
||||
|
||||
# Health check endpoint
|
||||
@app.get("/health")
|
||||
async def health_check():
|
||||
return {"status": "healthy"}
|
||||
|
||||
|
||||
app.include_router(fact_check_router, prefix="")
|
||||
app.include_router(aifact_check_router, prefix="")
|
||||
app.include_router(scrap_websites_router, prefix="")
|
||||
|
|
@ -50,4 +51,5 @@ app.include_router(scrap_websites_router, prefix="")
|
|||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)
|
||||
|
||||
uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)
|
||||
|
|
|
|||
|
|
@ -3,16 +3,19 @@ from main import app
|
|||
|
||||
client = TestClient(app)
|
||||
|
||||
|
||||
def test_root_endpoint():
|
||||
response = client.get("/")
|
||||
assert response.status_code == 200
|
||||
assert response.json() == {"message": "Welcome to your FastAPI application"}
|
||||
|
||||
|
||||
def test_health_endpoint():
|
||||
response = client.get("/health")
|
||||
assert response.status_code == 200
|
||||
assert response.json() == {"status": "healthy"}
|
||||
|
||||
|
||||
def test_cors_headers():
|
||||
response = client.get("/", headers={"Origin": "http://localhost:5173"})
|
||||
assert response.headers["access-control-allow-origin"] == "http://localhost:5173"
|
||||
assert response.headers["access-control-allow-origin"] == "http://localhost:5173"
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue