app.py

import streamlit as st
import pandas as pd
import torch
import faiss
from sentence_transformers import SentenceTransformer
from transformers import pipeline

# Load models for embeddings and generation
embedder = SentenceTransformer('all-MiniLM-L6-v2')
generator = pipeline("text2text-generation", model="facebook/bart-large")

# Load and combine train + test datasets
@st.cache_data
def load_data():
    train_df = pd.read_csv("train.csv")
    test_df = pd.read_csv("test.csv")
    df = pd.concat([train_df, test_df], ignore_index=True)
    df["text"] = df["title"].fillna('') + ". " + df["description"].fillna('')
    return df

# Load the data
df = load_data()
corpus = df["text"].tolist()
corpus_embeddings = embedder.encode(corpus, convert_to_tensor=True)

# Build FAISS index for similarity search
index = faiss.IndexFlatL2(corpus_embeddings.shape[1])
index.add(corpus_embeddings.cpu().detach().numpy())

# App UI
st.title("🧠 Climate News Fact Checker")
st.markdown("Enter a **claim** to check if it's supported or refuted by recent climate-related news.")

# User input
user_input = st.text_input("🔎 Enter a claim or statement:")

if user_input:
    # Embed the input claim
    query_embedding = embedder.encode([user_input])

    # Retrieve top-k similar news snippets
    top_k = 3
    D, I = index.search(query_embedding, top_k)
    results = [corpus[i] for i in I[0]]

    # Show retrieved snippets
    st.subheader("📄 Retrieved News Snippets")
    for idx, res in enumerate(results):
        st.write(f"**Snippet {idx+1}:** {res}")

    # Generate a response based on context
    context = " ".join(results)
    prompt = f"Claim: {user_input}\nContext: {context}\nAnswer:"

    st.subheader("✅ Fact Check Result")
    response = generator(prompt, max_length=100, do_sample=False)[0]['generated_text']
    st.write(response)