Update app.py

app.py

@@ -9,14 +9,12 @@ from langchain.prompts import PromptTemplate
 from langchain.chains import RetrievalQA
 from langchain_community.llms import HuggingFacePipeline
 
-# System prompts
 DEFAULT_SYSTEM_PROMPT = """
 You are a ROS2 expert assistant. Based on the context provided, give direct and concise answers.
 If the information is not in the context, respond with "I don't find that information in the available documentation."
 Keep responses to 1-2 lines maximum.
 """.strip()
 
-# Expanded pre-populated questions
 PREDEFINED_QUESTIONS = [
     "Select a question...",
     "Tell me how can I navigate to a specific pose - include replanning aspects in your answer.",
@@ -26,13 +24,6 @@ PREDEFINED_QUESTIONS = [
     "How do I integrate custom recovery behaviors?"
 ]
 
-# Helper text for tooltip
-DROPDOWN_TOOLTIP = """
-You can either:
-• Select a predefined question from this dropdown
-• Type your own question in the text box below
-"""
-
 def generate_prompt(context: str, question: str, system_prompt: str = DEFAULT_SYSTEM_PROMPT) -> str:
     return f"""
 [INST] <<SYS>>
@@ -43,7 +34,6 @@ Question: {question}
 Answer: [/INST]
 """.strip()
 
-# Initialize embeddings and database
 embeddings = HuggingFaceInstructEmbeddings(
     model_name="hkunlp/instructor-base",
     model_kwargs={"device": "cpu"}
@@ -75,7 +65,6 @@ def question_selected(question):
 @spaces.GPU
 def respond(message, history, system_message, max_tokens, temperature, top_p):
     try:
-        # Initialize chat history if None
         history = history or []
         
         if not message.strip():
@@ -84,15 +73,12 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
             
         model, tokenizer = initialize_model()
         
-        # Get context from database
         retriever = db.as_retriever(search_kwargs={"k": 2})
         docs = retriever.get_relevant_documents(message)
         context = "\n".join([doc.page_content for doc in docs])
         
-        # Generate prompt
         prompt = generate_prompt(context=context, question=message, system_prompt=system_message)
         
-        # Set up the pipeline
         text_pipeline = pipeline(
             "text-generation",
             model=model,
@@ -103,56 +89,40 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
             repetition_penalty=1.15
         )
         
-        # Generate response
         output = text_pipeline(
             prompt,
             return_full_text=False,
             max_new_tokens=max_tokens
         )[0]['generated_text']
         
-        # Add the new exchange to history
         history.append((message, output.strip()))
-        
         return history
         
     except Exception as e:
         history.append((message, f"An error occurred: {str(e)}"))
         return history
 
-def clear_input():
-    return gr.Textbox.update(value="")
+def submit_and_clear(message, history, system_message, max_tokens, temperature, top_p):
+    new_history = respond(message, history, system_message, max_tokens, temperature, top_p)
+    return new_history, gr.Textbox.update(value="")
 
-# Create the Gradio interface
 with gr.Blocks(title="ROS2 Expert Assistant") as demo:
     gr.Markdown("# ROS2 Expert Assistant")
     gr.Markdown("Ask questions about ROS2, navigation, and robotics. I'll provide concise answers based on the available documentation.")
     
-    with gr.Row():
-        with gr.Column(scale=8):
-            # Dropdown for predefined questions
-            question_dropdown = gr.Dropdown(
-                choices=PREDEFINED_QUESTIONS,
-                value="Select a question...",
-                label="Pre-defined Questions"
-            )
-        with gr.Column(scale=1):
-            # Info icon with tooltip
-            gr.Markdown(
-                """<div title="{}">ℹ️</div>""".format(DROPDOWN_TOOLTIP),
-                elem_classes=["tooltip"]
-            )
+    question_dropdown = gr.Dropdown(
+        choices=PREDEFINED_QUESTIONS,
+        value="Select a question...",
+        label="Pre-defined Questions"
+    )
         
-    with gr.Row():
-        # Chat interface
-        chatbot = gr.Chatbot()
+    chatbot = gr.Chatbot()
     
-    with gr.Row():
-        # Message input
-        msg = gr.Textbox(
-            label="Your Question",
-            placeholder="Type your question here or select one from the dropdown above...",
-            lines=2
-        )
+    msg = gr.Textbox(
+        label="Your Question",
+        placeholder="Type your question here or select one from the dropdown above...",
+        lines=2
+    )
         
     with gr.Row():
         submit = gr.Button("Submit")
@@ -186,55 +156,24 @@ with gr.Blocks(title="ROS2 Expert Assistant") as demo:
             label="Top-p"
         )
     
-    # Add custom CSS for tooltip
-    gr.Markdown("""
-        <style>
-        .tooltip {
-            cursor: help;
-            font-size: 1.2em;
-        }
-        </style>
-    """)
-    
-    # Event handlers
     question_dropdown.change(
         question_selected,
         inputs=[question_dropdown],
         outputs=[msg]
     )
     
-    def submit_and_clear(message, history, system_message, max_tokens, temperature, top_p):
-        # First get the response
-        new_history = respond(message, history, system_message, max_tokens, temperature, top_p)
-        # Then clear the input
-        return new_history, gr.Textbox.update(value="")
-    
     submit.click(
         submit_and_clear,
-        inputs=[
-            msg,
-            chatbot,
-            system_message,
-            max_tokens,
-            temperature,
-            top_p
-        ],
+        inputs=[msg, chatbot, system_message, max_tokens, temperature, top_p],
         outputs=[chatbot, msg]
     )
     
     clear.click(lambda: (None, ""), None, [chatbot, msg], queue=False)
     msg.submit(
         submit_and_clear,
-        inputs=[
-            msg,
-            chatbot,
-            system_message,
-            max_tokens,
-            temperature,
-            top_p
-        ],
+        inputs=[msg, chatbot, system_message, max_tokens, temperature, top_p],
         outputs=[chatbot, msg]
     )
 
 if __name__ == "__main__":
-    demo.launch(share)
+    demo.launch()