Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import faiss
+from sentence_transformers import SentenceTransformer
+from transformers import pipeline
+
+# Load abstracts
+df = pd.read_csv("abstracts.csv")
+abstracts = df["abstract"].tolist()
+
+# Load embedding model
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+abstract_embeddings = embedder.encode(abstracts, show_progress_bar=True)
+
+# Build FAISS index
+index = faiss.IndexFlatL2(abstract_embeddings.shape[1])
+index.add(abstract_embeddings)
+
+# Load LLM from Hugging Face Hub
+llm = pipeline("text-generation", model="tiiuae/falcon-7b-instruct", max_new_tokens=300)
+
+def verify_claim(claim):
+    query_vec = embedder.encode([claim])
+    D, I = index.search(query_vec, 3)
+
+    top_abstracts = df.iloc[I[0]]["abstract"].tolist()
+    context = "\n".join(top_abstracts)
+
+    prompt = f"Claim: {claim}\n\nEvidence:\n{context}\n\nAnswer True, False, or Uncertain. Then explain why:\n"
+    output = llm(prompt)[0]["generated_text"]
+
+    return f"🔍 **Top Abstracts:**\n{context}\n\n🧠 **LLM Response:**\n{output}"
+
+# Gradio UI
+gr.Interface(
+    fn=verify_claim,
+    inputs=gr.Textbox(label="Enter a scientific claim"),
+    outputs=gr.Markdown(),
+    title="🔬 Scientific Claim Verifier",
+    description="Checks the validity of a scientific claim using PubMed abstracts + LLM"
+).launch()