app/api/fact_check.py

@@ -1,13 +1,17 @@
 from fastapi import APIRouter, HTTPException
 import httpx
+from typing import Union
 from app.config import GOOGLE_API_KEY, GOOGLE_FACT_CHECK_BASE_URL, OPENAI_API_KEY
 from app.api.scrap_websites import search_websites, SearchRequest
 from app.services.openai_client import OpenAIClient
 from app.models.fact_check_models import (
     FactCheckRequest,
     FactCheckResponse,
+    UnverifiedFactCheckResponse,
     ErrorResponse,
     Source,
+    VerdictEnum,
+    ConfidenceEnum
 )
 from app.websites.fact_checker_website import get_all_sources
 
@@ -15,7 +19,7 @@ fact_check_router = APIRouter()
 openai_client = OpenAIClient(OPENAI_API_KEY)
 
 
-async def generate_fact_report(query: str, fact_check_data: dict) -> FactCheckResponse:
+async def generate_fact_report(query: str, fact_check_data: dict) -> Union[FactCheckResponse, UnverifiedFactCheckResponse]:
     """Generate a fact check report using OpenAI based on the fact check results."""
     try:
         base_system_prompt = """You are a professional fact-checking reporter. Your task is to create a detailed fact check report based on the provided data. Focus on accuracy, clarity, and proper citation of sources.
@@ -24,7 +28,24 @@ Rules:
 1. Include all source URLs and names in the sources list
 2. Keep the explanation focused on verifiable facts
 3. Include dates when available
-4. Maintain objectivity in the report"""
+4. Maintain objectivity in the report
+5. If no reliable sources are found, provide a clear explanation why"""
+
+        # If no sources were found, return an unverified response
+        if not fact_check_data.get("claims") and (
+            not fact_check_data.get("urls_found") or 
+            fact_check_data.get("status") == "no_results" or 
+            fact_check_data.get("verification_result", {}).get("no_sources_found")
+        ):
+            return UnverifiedFactCheckResponse(
+                claim=query,
+                verdict=VerdictEnum.UNVERIFIED,
+                confidence=ConfidenceEnum.LOW,
+                sources=[],
+                evidence="No fact-checking sources have verified this claim yet.",
+                explanation="Our search across reputable fact-checking websites did not find any formal verification of this claim. This doesn't mean the claim is false - just that it hasn't been formally fact-checked yet.",
+                additional_context="The claim may be too recent for fact-checkers to have investigated, or it may not have been widely circulated enough to warrant formal fact-checking."
+            )
 
         base_user_prompt = """Generate a comprehensive fact check report in this exact JSON format:
 {
@@ -40,9 +61,7 @@ Rules:
     "evidence": "A concise summary of the key evidence (1-2 sentences)",
     "explanation": "A detailed explanation including who verified it, when it was verified, and the key findings (2-3 sentences)",
     "additional_context": "Important context about the verification process, limitations, or broader implications (1-2 sentences)"
-}
-
-Ensure all URLs in sources are complete (including https:// if missing) and each source has both a URL and name."""
+}"""
 
         if "claims" in fact_check_data:
             system_prompt = base_system_prompt
@@ -71,75 +90,74 @@ Ensure all URLs in sources are complete (including https:// if missing) and each
             4. Note any conflicting information between sources"""
 
         response = await openai_client.generate_text_response(
-            system_prompt=system_prompt, user_prompt=user_prompt, max_tokens=1000
+            system_prompt=system_prompt,
+            user_prompt=user_prompt,
+            max_tokens=1000
         )
 
         try:
-            # First try to parse the response directly
             response_data = response["response"]
 
-            # Clean up sources before validation
             if isinstance(response_data.get("sources"), list):
                 cleaned_sources = []
                 for source in response_data["sources"]:
                     if isinstance(source, str):
-                        # Convert string sources to Source objects
-                        url = (
-                            source if source.startswith("http") else f"https://{source}"
-                        )
+                        url = source if source.startswith("http") else f"https://{source}"
                         cleaned_sources.append({"url": url, "name": source})
                     elif isinstance(source, dict):
-                        # Ensure URL has proper scheme
                         url = source.get("url", "")
                         if url and not url.startswith("http"):
                             source["url"] = f"https://{url}"
                         cleaned_sources.append(source)
                 response_data["sources"] = cleaned_sources
 
-            fact_check_response = FactCheckResponse(**response_data)
-            return fact_check_response
+            if response_data["verdict"] == "Unverified" or not response_data.get("sources"):
+                return UnverifiedFactCheckResponse(**response_data)
+            return FactCheckResponse(**response_data)
 
         except Exception as validation_error:
             print(f"Response validation error: {str(validation_error)}")
-            raise HTTPException(
-                status_code=422,
-                detail=ErrorResponse(
-                    detail=f"Invalid response format: {str(validation_error)}",
-                    error_code="VALIDATION_ERROR",
-                    path="/check-facts",
-                ).dict(),
+            return UnverifiedFactCheckResponse(
+                claim=query,
+                verdict=VerdictEnum.UNVERIFIED,
+                confidence=ConfidenceEnum.LOW,
+                sources=[],
+                evidence="An error occurred while processing the fact check results.",
+                explanation="The system encountered an error while validating the fact check results.",
+                additional_context="This is a technical error and does not reflect on the truthfulness of the claim."
             )
 
     except Exception as e:
         print(f"Error generating fact report: {str(e)}")
-        raise HTTPException(
-            status_code=500,
-            detail=ErrorResponse(
-                detail="Error generating fact report",
-                error_code="FACT_CHECK_ERROR",
-                path="/check-facts",
-            ).dict(),
+        return UnverifiedFactCheckResponse(
+            claim=query,
+            verdict=VerdictEnum.UNVERIFIED,
+            confidence=ConfidenceEnum.LOW,
+            sources=[],
+            evidence="An error occurred while generating the fact check report.",
+            explanation="The system encountered an unexpected error while processing the fact check request.",
+            additional_context="This is a technical error and does not reflect on the truthfulness of the claim."
         )
 
 
-@fact_check_router.post("/check-facts", response_model=FactCheckResponse)
+@fact_check_router.post("/check-facts", response_model=Union[FactCheckResponse, UnverifiedFactCheckResponse])
 async def check_facts(request: FactCheckRequest):
     """
     Fetch fact check results and generate a comprehensive report.
     """
     if not GOOGLE_API_KEY or not GOOGLE_FACT_CHECK_BASE_URL:
-        raise HTTPException(
-            status_code=500,
-            detail=ErrorResponse(
-                detail="Google API key or base URL is not configured",
-                error_code="CONFIGURATION_ERROR",
-                path="/check-facts",
-            ).dict(),
+        return UnverifiedFactCheckResponse(
+            claim=request.query,
+            verdict=VerdictEnum.UNVERIFIED,
+            confidence=ConfidenceEnum.LOW,
+            sources=[],
+            evidence="The fact-checking service is not properly configured.",
+            explanation="The system is missing required API configuration for fact-checking services.",
+            additional_context="This is a temporary system configuration issue."
         )
 
     headers = {"Content-Type": "application/json"}
     async with httpx.AsyncClient() as client:
-        # Get fact checker sources from the centralized configuration
         fact_checker_sources = get_all_sources()
 
         for source in fact_checker_sources:
@@ -170,7 +188,8 @@ async def check_facts(request: FactCheckRequest):
 
         try:
             search_request = SearchRequest(
-                search_text=request.query, source_types=["fact_checkers"]
+                search_text=request.query,
+                source_types=["fact_checkers"]
             )
 
             ai_response = await search_websites(search_request)
@@ -178,11 +197,10 @@ async def check_facts(request: FactCheckRequest):
 
         except Exception as e:
             print(f"Error in AI fact check: {str(e)}")
-            raise HTTPException(
-                status_code=404,
-                detail=ErrorResponse(
-                    detail="No fact check results found",
-                    error_code="NOT_FOUND",
-                    path="/check-facts",
-                ).dict(),
-            )
+            return await generate_fact_report(request.query, {
+                "status": "no_results",
+                "verification_result": {
+                    "no_sources_found": True,
+                    "reason": str(e)
+                }
+            })

app/models/fact_check_models.py

@@ -33,12 +33,44 @@ class Source(BaseModel):
 
     @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 UnverifiedFactCheckResponse(BaseModel):
+    claim: str = Field(
+        ...,
+        min_length=10,
+        max_length=1000,
+        description="The exact claim being verified",
+    )
+    verdict: VerdictEnum = Field(..., description="The verification verdict")
+    confidence: ConfidenceEnum = Field(..., description="Confidence level in the verdict")
+    sources: List[Source] = Field(
+        default=[],
+        description="List of sources used in verification"
+    )
+    evidence: str = Field(
+        ...,
+        min_length=20,
+        max_length=500,
+        description="Concise summary of key evidence",
+    )
+    explanation: str = Field(
+        ...,
+        min_length=50,
+        max_length=1000,
+        description="Detailed explanation of verification findings",
+    )
+    additional_context: str = Field(
+        ...,
+        min_length=20,
+        max_length=500,
+        description="Important context about the verification",
+    )
+
+
 class FactCheckResponse(BaseModel):
     claim: str = Field(
         ...,
@@ -47,11 +79,11 @@ class FactCheckResponse(BaseModel):
         description="The exact claim being verified",
     )
     verdict: VerdictEnum = Field(..., description="The verification verdict")
-    confidence: ConfidenceEnum = Field(
-        ..., description="Confidence level in the verdict"
-    )
+    confidence: ConfidenceEnum = Field(..., 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(
         ...,
@@ -82,15 +114,11 @@ class FactCheckResponse(BaseModel):
                     {
                         "url": "https://www.nasa.gov/mars-exploration",
                         "name": "NASA Mars Exploration",
-                    },
-                    {
-                        "url": "https://factcheck.org/2024/mars-claims",
-                        "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.",
+                "explanation": "Multiple fact-checking organizations investigated this claim. NASA's official communications and Mars mission reports from 2024 contain no mention of alien structures.",
+                "additional_context": "Similar false claims about alien structures on Mars have circulated periodically.",
             }
         }
 
@@ -98,4 +126,4 @@ class FactCheckResponse(BaseModel):
 class ErrorResponse(BaseModel):
     detail: str
     error_code: str = Field(..., example="VALIDATION_ERROR")
-    path: str = Field(..., example="/check-facts")
+    path: str = Field(..., example="/check-facts")

main.py

@@ -17,6 +17,7 @@ origins = [
     "http://localhost:5173",
     "http://0.0.0.0",
     "http://0.0.0.0:5173",
+    "*"
 ]