Update app.py

app.py

@@ -1,71 +1,53 @@
-# Image-to-Claim Verifier v2 (with Verdict using BLIP + DuckDuckGo + Flan-T5)
+# Text-Only RAG Claim Verifier for Historical & Political Facts
 
 import gradio as gr
-from transformers import BlipProcessor, BlipForConditionalGeneration, pipeline
-from PIL import Image
 from duckduckgo_search import DDGS
-import torch
+from transformers import pipeline
 
-# Load BLIP for image captioning
-blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
-blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+# Load lightweight LLM for verdict generation
+llm_pipeline = pipeline("text2text-generation", model="google/flan-t5-base", device=-1)
 
-# Load Flan-T5 for final verdict generation
-verdict_pipeline = pipeline("text2text-generation", model="google/flan-t5-base", device=-1)
-
-# DuckDuckGo search
-
-def retrieve_passages(query, num_results=3):
+# Function to retrieve supporting info
+def retrieve_context(claim, num_results=3):
     docs = []
     with DDGS() as ddgs:
-        for r in ddgs.text(query, region='wt-wt', safesearch='Moderate', max_results=num_results):
-            docs.append(r['body'])
+        for r in ddgs.text(claim, region='wt-wt', safesearch='Moderate', max_results=num_results):
+            docs.append(f"- {r['title']}: {r['body']}")
     return docs or ["No relevant documents found."]
 
-# Main function
-def verify_claim(image, claim):
+# Function to verify claim
+def verify_claim_text_only(claim):
     try:
-        # Step 1: Get caption from BLIP
-        inputs = blip_processor(image, return_tensors="pt")
-        out = blip_model.generate(**inputs)
-        caption = blip_processor.decode(out[0], skip_special_tokens=True)
-
-        # Step 2: Retrieve info
-        combined_query = f"{caption} {claim}"
-        passages = retrieve_passages(combined_query)
+        # Step 1: Retrieve supporting info
+        retrieved_docs = retrieve_context(claim)
 
-        # Step 3: Build prompt for LLM
+        # Step 2: Compose prompt for RAG+LLM
+        context_block = "\n".join(retrieved_docs)
         prompt = f"""
-        The image shows: {caption}
-        The user claims: "{claim}"
+Claim: "{claim}"
 
-        The following information was found online:
-        1. {passages[0] if len(passages) > 0 else ''}
-        2. {passages[1] if len(passages) > 1 else ''}
-        3. {passages[2] if len(passages) > 2 else ''}
+The following information was retrieved from reliable sources:
+{context_block}
 
-        Based on this, is the claim TRUE, FALSE, or UNCERTAIN? Justify your answer in 2-3 lines.
-        """
+Based on the above context, is the claim TRUE, FALSE, or UNCERTAIN? Justify your answer clearly. Also provide the correct version of the claim if it's false.
+"""
 
-        # Step 4: Get final judgment
-        answer = verdict_pipeline(prompt, max_new_tokens=150)[0]['generated_text']
+        # Step 3: Run LLM
+        response = llm_pipeline(prompt, max_new_tokens=250)[0]['generated_text']
 
-        return f"🖼 **Image Caption:** {caption}\n\n📚 **Search Results:**\n- {passages[0]}\n- {passages[1] if len(passages) > 1 else ''}\n- {passages[2] if len(passages) > 2 else ''}\n\n✅ **Verdict:**\n{answer}"
+        return f"📜 **Claim**: {claim}\n\n🔍 **Retrieved Info**:\n{context_block}\n\n✅ **Verdict & Justification**:\n{response}"
 
     except Exception as e:
         return f"❌ Error: {str(e)}"
 
-# Gradio App
+# Gradio Interface
 demo = gr.Interface(
-    fn=verify_claim,
-    inputs=[
-        gr.Image(type="pil", label="Upload Image"),
-        gr.Textbox(lines=2, label="Enter a claim about the image")
-    ],
+    fn=verify_claim_text_only,
+    inputs=gr.Textbox(lines=2, label="Enter a historical or political claim"),
     outputs=gr.Markdown(),
-    title="🧠 Image-to-Claim Verifier (with Final Verdict)",
-    description="Upload an image and enter a claim. The app uses BLIP to caption the image, searches the web for context, and uses Flan-T5 to verify the claim as TRUE, FALSE, or UNCERTAIN with justification."
+    title="🧠 FactCheckGPT – Historical & Political Claim Verifier",
+    description="Enter a factual claim (e.g., 'Alexander the Great died in 1971') and the app will verify if it's TRUE, FALSE, or UNCERTAIN using live web retrieval and LLM justification."
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()