Creating app.py

app.py

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+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
+
+# Hardcoded OpenAI API Key
+OPENAI_API_KEY = "your-openai-api-key"  # Replace with your actual API key
+os.environ['OPENAI_API_KEY'] = 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)