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
+import os
+from reportlab.lib.pagesizes import letter
+from reportlab.pdfgen import canvas
+
+# 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
+
+# Function to generate PDF
+def generate_pdf(text, file_path):
+    c = canvas.Canvas(file_path, pagesize=letter)
+    c.setFont("Helvetica", 12)
+    width, height = letter
+    text_object = c.beginText(40, height - 40)
+    text_object.textLines(text)
+    c.drawText(text_object)
+    c.showPage()
+    c.save()
+
+# 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)
+
+    # Generate PDF from the AI insights
+    pdf_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
+    generate_pdf(response, pdf_file.name)
+
+    # Provide download link for the generated PDF
+    with open(pdf_file.name, "rb") as f:
+        st.download_button(
+            label="Download AI Insights as PDF",
+            data=f,
+            file_name="ai_benefits_analysis.pdf",
+            mime="application/pdf"
+        )