main.py

# main.py
from fastapi import FastAPI
from pydantic import BaseModel, Field, HttpUrl
from typing import List

# -- 1. Define Pydantic Models for Data Validation --

# This model defines the structure of the incoming request JSON
class RequestPacket(BaseModel):
    text: str
    url: HttpUrl  # Pydantic validates this is a valid URL
    image: str    # Expecting a base64 encoded string

# These models define the structure of the outgoing response JSON
class Analysis(BaseModel):
    isMisinformation: bool
    reasoning: str
    confidenceScore: float = Field(
        ..., 
        ge=0,  # Must be greater than or equal to 0
        le=1   # Must be less than or equal to 1
    )

class Source(BaseModel):
    name: str
    description: str

class ResponsePacket(BaseModel):
    summary: str
    analysis: Analysis
    sources: List[Source]

# -- 2. Create the FastAPI Application --
app = FastAPI(
    title="Backend Checker API",
    description="A mock API to validate requests and send simulated responses."
)

# -- 3. Define the API Endpoint --
@app.post("/check", response_model=ResponsePacket)
async def check_request_format(request: RequestPacket):
    """
    This endpoint receives a request packet, validates its structure,
    and returns a mock analysis response.
    """
    # For your debugging: print a confirmation to the server console
    print("✅ Request received and successfully validated!")
    print(f"Received URL: {request.url}")
    print(f"Text length: {len(request.text)}")
    print(f"Image string starts with: {request.image[:30]}...")

    # -- 4. Create and Return the Mock Response --
    mock_response = ResponsePacket(
        summary="This is a simulated summary based on the provided text. The analysis suggests the content requires further verification due to conflicting reports from primary sources.",
        analysis=Analysis(
            isMisinformation=True,
            reasoning="The claim contradicts established facts from reputable news organizations and scientific consensus. The provided image appears to be digitally altered.",
            confidenceScore=0.88
        ),
        sources=[
            Source(name="Verified Source A", description="Provides data that conflicts with the user's text."),
            Source(name="FactCheck Organization B", description="Previously debunked a similar claim.")
        ]
    )
    
    return mock_response