app.py

import streamlit as st
import pandas as pd
import faiss
import numpy as np
from sentence_transformers import SentenceTransformer
from transformers import T5ForConditionalGeneration, T5Tokenizer

# Load the Sentence Transformer and T5 model
@st.cache(allow_output_mutation=True)
def load_models():
    embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
    qa_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small")
    tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")
    return embedding_model, qa_model, tokenizer

embedding_model, qa_model, tokenizer = load_models()

# Upload and load the CSV file
st.title("Economics & Population Advisor")
uploaded_file = st.file_uploader("Upload your CSV file with economic documents", type=["csv"])

if uploaded_file is not None:
    # Load CSV
    df = pd.read_csv(uploaded_file, error_bad_lines=False, engine='python')
    st.write("Dataset Preview:", df.head())

    # Assume 'text' column contains the document text; replace with actual column name
    documents = df['Country Name'].tolist() if 'text' in df.columns else st.text_input("Specify the text column name:")

    # Create embeddings for FAISS indexing
    st.write("Indexing documents...")
    embeddings = embedding_model.encode(documents)
    dimension = embeddings.shape[1]
    index = faiss.IndexFlatL2(dimension)
    index.add(np.array(embeddings))
    st.write("Indexing complete.")

    # Function to generate response
    def generate_summary(context):
        inputs = tokenizer("summarize: " + context, return_tensors="pt", max_length=512, truncation=True)
        outputs = qa_model.generate(inputs["input_ids"], max_length=150, min_length=50, length_penalty=2.0)
        return tokenizer.decode(outputs[0], skip_special_tokens=True)

    # RAG functionality: Ask a question, retrieve documents, and generate an answer
    st.subheader("Ask a Question about Economic Data")
    question = st.text_input("Enter your question:")

    if st.button("Get Answer") and question:
        question_embedding = embedding_model.encode([question])
        D, I = index.search(np.array(question_embedding), k=3)
        retrieved_docs = [documents[i] for i in I[0]]
        context = " ".join(retrieved_docs)
        answer = generate_summary(context)
        
        st.write("Answer:", answer)