app_2.py

import streamlit as st
import tempfile
import os
import shutil
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain_community.document_loaders import WebBaseLoader
from langchain.chains.question_answering import load_qa_chain
from langchain_openai import ChatOpenAI
import os

# Hardcoded OpenAI API Key
os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')

# Streamlit UI
st.title("🔍 AI Benefits Analysis for Any Company")

# User input: Only Website URL (with placeholder)
website_url = st.text_input("Enter Website URL", placeholder="e.g., https://www.companywebsite.com")

# Fixed question for AI analysis
fixed_question = (
    "Analyze how Artificial Intelligence (AI) can benefit this company based on its industry, "
    "key operations, and challenges. Provide insights on AI-driven improvements in customer experience, "
    "automation, sales, risk management, decision-making, and innovation. Include an AI implementation roadmap, "
    "challenges, solutions, and future opportunities with real-world examples."
)

# Temporary directory to store FAISS index
temp_dir = tempfile.gettempdir()
faiss_db_path = os.path.join(temp_dir, "faiss_index_dir")

# Function to fetch and process website data
def build_embeddings(url):
    st.info("Fetching and processing website data...")
    
    # Load website data
    loader = WebBaseLoader(url)
    raw_text = loader.load()
    
    # Chunking the fetched text
    text_splitter = CharacterTextSplitter(separator='\n', chunk_size=500, chunk_overlap=50)
    docs = text_splitter.split_documents(raw_text)
    
    # Creating embeddings
    embeddings = OpenAIEmbeddings()
    docsearch = FAISS.from_documents(docs, embeddings)
    
    # Save FAISS index
    if os.path.exists(faiss_db_path):
        shutil.rmtree(faiss_db_path)
    os.makedirs(faiss_db_path)
    docsearch.save_local(faiss_db_path)
    
    return docsearch

# Run everything in one click
if st.button("Get AI Insights") and website_url:
    docsearch = build_embeddings(website_url)
    
    # AI Benefits Analysis
    st.subheader("💬 AI Benefits Analysis")
    
    chain = load_qa_chain(ChatOpenAI(model="gpt-4o"), chain_type="stuff")
    docs = docsearch.similarity_search(fixed_question)
    response = chain.run(input_documents=docs, question=fixed_question)
    
    st.write("**AI Insights:**", response)