app.py

import streamlit as st
import sympy as sp
import chromadb
from transformers import pipeline
from langchain.chains import RetrievalQA
from langchain_community.vectorstores import FAISS
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.llms import OpenAI
import os

# Initialize ChromaDB for Retrieval-Augmented Generation (RAG)
chroma_client = chromadb.PersistentClient(path="./chroma_db")
embedding_model = SentenceTransformerEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")

# Check if FAISS index exists
faiss_path = "faiss_index"

if not os.path.exists(faiss_path):
    st.error("⚠️ FAISS index not found. Please upload or generate the FAISS index first.")
else:
    # Load the RAG-based Retrieval System
    vectorstore = FAISS.load_local(faiss_path, embedding_model, allow_dangerous_deserialization=True)
    retriever = vectorstore.as_retriever()
    qa_chain = RetrievalQA.from_chain_type(llm=OpenAI(), retriever=retriever)

    # Load NLP Model
    model = pipeline("text2text-generation", model="google/flan-t5-small")

    st.title("🤖 AI-Driven Mathematical Model Generator")
    st.write("Enter a problem statement in natural language to get a mathematical model.")

    user_input = st.text_area("✍️ Enter your problem:")

    if st.button("🚀 Generate Model"):
        retrieved_context = qa_chain.run(user_input)  # RAG retrieval
        response = model(f"Generate a mathematical model for: {user_input}\nContext: {retrieved_context}", max_length=200)
        
        try:
            equation = sp.sympify(response[0]['generated_text'])
        except:
            equation = response[0]['generated_text']  # If parsing fails, return text
        
        st.subheader("📌 Mathematical Model:")
        st.latex(sp.latex(equation))
        st.code(str(equation), language='python')