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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()
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