Update app.py

app.py

@@ -1,5 +1,4 @@
 from smolagents import CodeAgent, DuckDuckGoSearchTool, OpenAIServerModel, tool, FinalAnswerTool, VisitWebpageTool, GradioUI, LiteLLMModel
-from Gradio_UI import GradioUI
 import requests
 import pytz
 from typing import Optional, Tuple, Union, Any # Added Any
@@ -201,24 +200,122 @@ class HeightComparisonAgent(CodeAgent):
 # --- Instantiate the Subclassed Agent ---
 # IMPORTANT: Use the HeightComparisonAgent class, not CodeAgent directly.
 # Set verbosity_level=3 so the parent's run method (super().run) generates the verbose output.
+# --- Instantiate the Agent ---
+height_agent = None
+initialization_error_message = None # <<< Make sure this line is BEFORE the if
+
 if llm_model is not None:
-    height_agent = HeightComparisonAgent(
-        tools=[DuckDuckGoSearchTool(), VisitWebpageTool(), parse_height_from_text, create_comparison_statement, FinalAnswerTool()],
-        model=llm_model,
-        verbosity_level=3, # <<< Crucial for GradioUI to see the steps from the parent run
-        max_steps=20, # Increased slightly just in case
-        # planning_interval=3, # Optional
-    )
+    try:
+        height_agent = HeightComparisonAgent(
+            tools=[DuckDuckGoSearchTool(), VisitWebpageTool(), parse_height_from_text, create_comparison_statement, FinalAnswerTool()],
+            model=llm_model,
+            verbosity_level=3, # <<< ESSENTIAL for capturing reasoning steps
+            max_steps=20,
+        )
+        print("--- HeightComparisonAgent initialized successfully. ---")
+    except Exception as e:
+        # Store the error if agent creation fails even with a model
+        initialization_error_message = f"ERROR: Failed to initialize HeightComparisonAgent: {e}\n{traceback.format_exc()}"
+        print(initialization_error_message)
+        height_agent = None # Ensure agent is None on error
 else:
-    print('Failed to Load LiteLLM Model')
+    # Store the error if the LLM model itself failed to initialize
+    initialization_error_message = (
+        "ERROR: Could not initialize any Language Model backend.\n\n"
+        f"Please check the Space logs (check the 'Logs' tab above the app).\n"
+        f"Verify that at least one of these secrets is correctly set in Space Settings -> Secrets:\n"
+        f"Also ensure necessary libraries are in requirements.txt."
+    )
+    print(initialization_error_message)
+    # height_agent is already None
 
-# --- Launch Gradio using GradioUI and the custom agent ---
-print("--- Starting Gradio Interface with GradioUI and HeightComparisonAgent ---")
+# --- Wrapper Function to Run Agent and Capture Output ---
+def run_agent_wrapper(query: str) -> Tuple[str, str]:
+    """
+    Runs the height_agent and captures its stdout (reasoning steps).
+    Returns (reasoning_log, final_answer).
+    """
+    # Access the global variables
+    global height_agent, initialization_error_message
+
+    if height_agent is None:
+        # If agent initialization failed, return the stored error message
+        return (initialization_error_message or "Agent not initialized (unknown error).",
+                "Agent failed to initialize. See reasoning log for details.")
+
+    print(f"\n--- Running agent for query: '{query}' ---") # Log to console
+    log_stream = io.StringIO()
+    final_answer = "Agent execution did not complete." # Default message
+
+    try:
+        # Redirect stdout to capture prints from agent.run() (due to verbosity=3)
+        with contextlib.redirect_stdout(log_stream):
+            # Make sure to call the run method of the specific agent instance
+            final_answer = height_agent.run(query) # Pass the raw query
+            print("\n--- Agent execution finished successfully. ---") # Add marker to log
+    except Exception as e:
+        print(f"\n--- Error during agent execution wrapper: {e} ---") # Log to console
+        # Print exception details *into the captured log*
+        print("\n\n******** ERROR DURING EXECUTION ********\n", file=log_stream)
+        traceback.print_exc(file=log_stream)
+        final_answer = f"An error occurred during processing. See reasoning log. Error: {e}"
+    finally:
+        reasoning_log = log_stream.getvalue()
+        log_stream.close()
+        print("--- Finished capturing stdout. ---") # Log to console
+
+    return reasoning_log, final_answer
+# --- Build Gradio Interface Manually with gr.Blocks ---
+print("--- Building Gradio Interface with gr.Blocks ---")
+
+# Make sure theme is applied correctly if desired
+# theme = gr.themes.Default() # Or another theme
+# with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as demo:
+with gr.Blocks(css="footer {visibility: hidden}") as demo: # Hides the default footer
+    gr.Markdown("# Height Comparison Agent")
+    gr.Markdown("Enter your height (e.g., '180 cm', '5ft 11in') to find characters/figures of similar height.")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            query_input = gr.Textbox(
+                label="Your Query (including height)",
+                placeholder="e.g., I am 175cm tall",
+                lines=2 # Allow slightly more room for input
+            )
+            submit_button = gr.Button("Compare Heights", variant="primary")
+        with gr.Column(scale=2):
+            final_answer_output = gr.Textbox(
+                label="Final Answer",
+                interactive=False,
+                lines=5
+            )
+
+    gr.Markdown("## Agent Reasoning Steps")
+    # Use gr.Code for better formatting of logs, especially if they contain code blocks
+    reasoning_output = gr.Code(
+        label="Reasoning Log",
+        language="text", # Use 'markdown' if logs might contain markdown
+        interactive=False,
+        lines=20
+    )
 
-# GradioUI will call height_agent.run(user_input)
-# Our overridden run method will preprocess the input and call super().run(detailed_task)
-# GradioUI should then display the thinking steps from super().run()
-ui = GradioUI(agent=height_agent)
+    # Link components: When button is clicked, call wrapper, update outputs
+    submit_button.click(
+        fn=run_agent_wrapper,          # Function to call
+        inputs=[query_input],          # Component(s) providing input
+        outputs=[reasoning_output, final_answer_output] # Components to update
+        # Ensure the order matches the return tuple from run_agent_wrapper: (log, answer)
+    )
 
-# Launch the UI
-ui.launch() # Use debug=True for Gradio logs
+    # Add an example input
+    gr.Examples(
+        examples=[
+            "I am 188cm tall",
+            "How tall is someone who is 5 foot 8 inches?",
+            "My height is 1.65m",
+        ],
+        inputs=query_input
+    )
+    # --- Launch Gradio ---
+print("--- Launching Gradio demo ---")
+demo.launch(ssr=False) # ssr=False recommended, share=True not needed for Spaces