Create app.py

app.py

@@ -0,0 +1,75 @@
+def main():
+    load_dotenv()
+
+    st.set_page_config(
+        page_title="PDF Insights AI", 
+        page_icon=":books:", 
+        layout="wide"
+    )
+    st.write(css, unsafe_allow_html=True)
+    
+
+    # Welcome section
+    st.title("📚 PDF Insights AI")
+    st.markdown("""
+    ### Unlock the Knowledge in Your PDFs
+    - 🤖 AI-powered document analysis
+    - 💬 Ask questions about your uploaded documents
+    - 📄 Support for multiple PDF files
+    """)
+
+    if "conversation" not in st.session_state:
+        st.session_state.conversation = None
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = []
+
+    # File upload section
+    with st.sidebar:
+        st.header("📤 Upload Documents")
+        pdf_docs = st.file_uploader(
+            "Upload your PDFs here", 
+            type=['pdf'], 
+            accept_multiple_files=True,
+            help="Upload PDF files to analyze. Max file size: 200MB"
+        )
+
+        # File validation
+        if pdf_docs:
+            for doc in pdf_docs:
+                if doc.size > 200 * 1024 * 1024:  # 200 MB
+                    st.error(f"File {doc.name} is too large. Maximum file size is 200MB.")
+                    pdf_docs.remove(doc)
+
+        if st.button("Process Documents", type="primary"):
+            if not pdf_docs:
+                st.warning("Please upload at least one PDF file.")
+            else:
+                with st.spinner("Processing your documents..."):
+                    try:
+                        # get pdf text
+                        content, metadata = prepare_docs(pdf_docs)
+
+                        # get the text chunks
+                        split_docs = get_text_chunks(content, metadata)
+
+                        # create vector store
+                        vectorstore = ingest_into_vectordb(split_docs)
+
+                        # create conversation chain
+                        st.session_state.conversation = get_conversation_chain(vectorstore)
+                        
+                        st.success("Documents processed successfully! You can now ask questions.")
+                    except Exception as e:
+                        st.error(f"An error occurred while processing documents: {str(e)}")
+
+    # Question input section
+    user_question = st.text_input(
+        "📝 Ask a question about your documents", 
+        placeholder="What insights can you provide from these documents?"
+    )
+
+    if user_question:
+        if st.session_state.conversation is None:
+            st.warning("Please upload and process documents first.")
+        else:
+            handle_userinput(user_question)