Update app.py

app.py

@@ -1,53 +1,41 @@
-# Text-Only RAG Claim Verifier for Historical & Political Facts
+# Historical Claim Verifier using RAG (Free Tools Only)
+# Works on Hugging Face Spaces with Gradio + Wikipedia
 
 import gradio as gr
-from duckduckgo_search import DDGS
+from langchain.document_loaders import WikipediaLoader
 from transformers import pipeline
+import wikipedia
 
-# Load lightweight LLM for verdict generation
-llm_pipeline = pipeline("text2text-generation", model="google/flan-t5-base", device=-1)
-
-# Function to retrieve supporting info
-def retrieve_context(claim, num_results=3):
-    docs = []
-    with DDGS() as ddgs:
-        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."]
-
-# Function to verify claim
-def verify_claim_text_only(claim):
+# Step 1: Define Wikipedia search and summary function
+def search_wikipedia(query):
     try:
-        # Step 1: Retrieve supporting info
-        retrieved_docs = retrieve_context(claim)
-
-        # Step 2: Compose prompt for RAG+LLM
-        context_block = "\n".join(retrieved_docs)
-        prompt = f"""
-Claim: "{claim}"
-
-The following information was retrieved from reliable sources:
-{context_block}
-
-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.
-"""
+        page_titles = wikipedia.search(query, results=2)
+        summaries = [wikipedia.summary(title, sentences=3) for title in page_titles]
+        return "\n\n".join(summaries)
+    except Exception as e:
+        return f"Wikipedia search error: {str(e)}"
 
-        # Step 3: Run LLM
-        response = llm_pipeline(prompt, max_new_tokens=250)[0]['generated_text']
+# Step 2: Load a free Hugging Face LLM pipeline (e.g., Flan-T5)
+llm_pipeline = pipeline("text2text-generation", model="google/flan-t5-large")
 
-        return f"📜 **Claim**: {claim}\n\n🔍 **Retrieved Info**:\n{context_block}\n\n✅ **Verdict & Justification**:\n{response}"
+# Step 3: Define the claim verification function
+def verify_claim(claim):
+    context = search_wikipedia(claim)
+    if "error" in context.lower() or context.strip() == "":
+        return "Could not retrieve relevant information. Please try a different claim."
 
-    except Exception as e:
-        return f"❌ Error: {str(e)}"
+    prompt = f"Claim: {claim}\n\nContext: {context}\n\nIs this claim true or false? Explain."
+    response = llm_pipeline(prompt, max_length=512, do_sample=False)[0]['generated_text']
+    return response
 
-# Gradio Interface
+# Step 4: Gradio UI setup
 demo = gr.Interface(
-    fn=verify_claim_text_only,
-    inputs=gr.Textbox(lines=2, label="Enter a historical or political claim"),
-    outputs=gr.Markdown(),
-    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."
+    fn=verify_claim,
+    inputs=gr.Textbox(label="Enter a historical claim", placeholder="e.g., Alexander the Great died in 1971."),
+    outputs=gr.Textbox(label="Claim Verification Output"),
+    title="Historical Claim Verifier (RAG-Based)",
+    description="Uses Wikipedia + a free LLM to verify if a historical claim is true or false, and explains why."
 )
 
-if __name__ == "__main__":
-    demo.launch()
+# Step 5: Launch (will auto-run on Hugging Face Spaces)
+demo.launch()