Create app.py

app.py

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+import logging
+import gradio as gr
+from utils.document_utils import initialize_logging
+from globals import app_config 
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+initialize_logging()
+
+def load_sample_question(question):
+    return question
+
+def clear_selection():
+    return gr.update(value=[]), "", "", gr.update(value=[])  # Reset doc_selector to empty list
+
+def process_uploaded_file(file, current_selection):
+    """Process uploaded file using DocumentManager and update UI."""
+    try:
+        if file is None:
+            # When file input is cleared, preserve current selection and choices
+            uploaded_docs = app_config.doc_manager.get_uploaded_documents()
+            return (
+                "",
+                gr.update(choices=uploaded_docs, value=current_selection or []),
+                False,
+                ""
+            )
+        
+        status, filename, doc_id = app_config.doc_manager.process_document(file.name if file else None)
+
+        updated_selection = current_selection if current_selection else []
+        if filename and filename not in updated_selection:
+            updated_selection.append(filename)
+        trigger_summary = bool(filename)
+        logging.info(f"Processed file: {filename}, Trigger summary: {trigger_summary}")
+
+        return (
+            status,
+            gr.update(choices=app_config.doc_manager.get_uploaded_documents(), value=updated_selection),
+            trigger_summary,
+            filename  
+        )
+    except Exception as e:
+        logging.error(f"Error in process_uploaded_file: {e}")
+        return "Error processing file", gr.update(choices=[]), False, ''
+
+def update_doc_selector(selected_docs):
+    """Keep selected documents in sync."""
+    return selected_docs
+
+# UI Configuration
+models = [ "gemma2-9b-it", "llama3-70b-8192"]
+
+example_questions = [
+    "What is the architecture of the Communication Server?",
+    "Show me an example of a configuration file.",
+    "How to create Protected File Directories ?",
+    "What functionalities are available in the Communication Server setups?",
+    "What is Mediator help?",
+    "Why AzureBlobStorage port is used?"
+]
+
+with gr.Blocks(css="""
+        .chatbot .user {
+            position: relative;
+            background-color: #cfdcfd;
+            padding: 12px 16px;
+            border-radius: 20px;
+            border-bottom-right-radius: 6px;
+            display: inline-block;
+            max-width: 80%;
+            margin: 8px 0;
+        }
+
+        /* Tail effect */
+        .chatbot .user::after {
+            content: "";
+            position: absolute;
+            right: -10px;
+            bottom: 10px;
+            width: 0;
+            height: 0;
+            border: 10px solid transparent;
+            border-left-color: #cfdcfd;
+            border-right: 0;
+            border-top: 0;
+            margin-top: -5px;
+        }
+        .chatbot .bot { background-color: #f1f8e9; padding: 8px; border-radius: 10px; }   /* Light green for bot responses */
+    """) as interface:
+    interface.title = "🤖 IntelliDoc: AI Document Explorer"
+    gr.Markdown("""
+        # 🤖 IntelliDoc: AI Document Explorer
+        **AI Document Explorer** allows you to upload PDF documents and interact with them using AI-powered analysis and summarization. Ask questions, extract key insights, and gain a deeper understanding of your documents effortlessly.
+    """)
+    summary_query_state = gr.State()  # State to hold the summary query
+    trigger_summary_state = gr.State()  # State to hold trigger flag
+    filename_state = gr.State()  # State to hold file name
+    chunks_state = gr.State()
+    summary_text_state = gr.State()
+    sample_questions_state = gr.State()
+
+    with gr.Row():
+        # Left Sidebar
+        with gr.Column(scale=2):
+            gr.Markdown("## Upload and Select Document")
+            upload_btn = gr.File(label="Upload PDF Document", file_types=[".pdf"])
+            doc_selector = gr.Dropdown(
+                choices=app_config.doc_manager.get_uploaded_documents(),
+                label="Documents",
+                multiselect=True,
+                value=[]  # Initial value as empty list
+            )
+            model_selector = gr.Dropdown(choices=models, label="Models", interactive=True)
+            clear_btn = gr.Button("Clear Selection")
+            upload_status = gr.Textbox(label="Upload Status", interactive=False)
+
+            # Process uploaded file and update UI
+            upload_event = upload_btn.change(
+                process_uploaded_file,
+                inputs=[upload_btn, doc_selector],
+                outputs=[
+                    upload_status,
+                    doc_selector,
+                    trigger_summary_state,  # Store trigger_summary
+                    filename_state
+                ]
+            )
+            
+
+        # Middle Section (Chat & LLM Response)
+        with gr.Column(scale=6):
+            gr.Markdown("## Chat with document(s)")
+            chat_history = gr.Chatbot(label="Chat History", height= 650, bubble_full_width= False, type="messages")
+            with gr.Row():
+                chat_input = gr.Textbox(label="Ask additional questions about the document...", show_label=False, placeholder="Ask additional questions about the document...", elem_id="chat-input", lines=3)
+                chat_btn = gr.Button("🚀 Send", variant="primary", elem_id="send-button", scale=0)
+            chat_btn.click(app_config.chat_manager.generate_chat_response, inputs=[chat_input, doc_selector, chat_history], outputs=chat_history).then(
+                lambda: "",  # Return an empty string to clear the chat_input
+                outputs=chat_input
+            )
+
+        # Right Sidebar (Sample Questions & History)
+        with gr.Column(scale=2):
+            gr.Markdown("## Sample questions for this document:")
+            with gr.Column():
+                sample_questions = gr.Dropdown(
+                    label="Select a sample question",
+                    choices=[],
+                    interactive=True,
+                    allow_custom_value=True  # Allows users to type custom questions if needed
+                )
+            
+            clear_btn.click(
+                clear_selection,
+                outputs=[doc_selector, upload_status, filename_state, sample_questions]
+            )
+            # Reinitialize LLM when the model changes
+            model_selector.change(
+                app_config.gen_llm.reinitialize_llm,
+                inputs=[model_selector],
+                outputs=[upload_status]
+            )
+
+           # After upload, generate "Auto Summary" message only if trigger_summary is True
+            upload_event.then(
+                fn=lambda trigger, filename: "Can you provide summary of the document" if trigger and filename else None,
+                inputs=[trigger_summary_state, filename_state],
+                outputs=[summary_query_state]
+                ).then(
+                    fn=lambda query, history: history + [{"role": "user", "content": ""}, {"role": "assistant", "content": "Generating summary of the document, please wait..."}] if query else history,
+                    inputs=[summary_query_state, chat_history],
+                    outputs=[chat_history]
+                ).then(
+                    fn=lambda trigger, filename: app_config.doc_manager.get_chunks(filename) if trigger and filename else None,
+                    inputs=[trigger_summary_state, filename_state],
+                    outputs=[chunks_state]
+                ).then(
+                    fn=lambda chunks: app_config.chat_manager.generate_summary(chunks) if chunks else None,
+                    inputs=[chunks_state],
+                    outputs=[summary_text_state]
+                ).then(
+                    fn=lambda summary, history: history + [{"role": "assistant", "content": summary}] if summary else history,
+                    inputs=[summary_text_state, chat_history],
+                    outputs=[chat_history]
+                ).then(
+                    fn=lambda chunks: app_config.chat_manager.generate_sample_questions(chunks) if chunks else [],
+                    inputs=[chunks_state],
+                    outputs=[sample_questions_state]
+                ).then(
+                    fn=lambda questions: gr.update(
+                        choices=questions if questions else ["No questions available"],
+                        value=questions[0] if questions else None  # Set the first question as default
+                    ),
+                    inputs=[sample_questions_state],
+                    outputs=[sample_questions]
+                )
+            # Populate chat_input when a question is selected
+            sample_questions.change(
+                fn=lambda question: question,
+                inputs=[sample_questions],
+                outputs=[chat_input]
+            )
+            #gr.Markdown("## Logs")
+            #history = gr.Textbox(label="Previous Queries", interactive=False)
+
+if __name__ == "__main__":
+    interface.launch()