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Dockerfile

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+# Dockerfile
+
+# Use an official Python runtime as a parent image
+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+# 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

requirements.txt

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+# requirements.txt
+fastapi
+uvicorn[standard]
+pydantic