Update Gradio_UI.py

Gradio_UI.py

@@ -24,32 +24,47 @@ from smolagents.agents import ActionStep, MultiStepAgent
 from smolagents.memory import MemoryStep
 from smolagents.utils import _is_package_available
 
-def pull_messages_from_step(step_log: MemoryStep):
+
+def pull_messages_from_step(
+    step_log: MemoryStep,
+):
+    """Extract ChatMessage objects from agent steps with proper nesting"""
     import gradio as gr
 
     if isinstance(step_log, ActionStep):
+        # Output the step number
         step_number = f"Step {step_log.step_number}" if step_log.step_number is not None else ""
         yield gr.ChatMessage(role="assistant", content=f"**{step_number}**")
 
+        # First yield the thought/reasoning from the LLM
         if hasattr(step_log, "model_output") and step_log.model_output is not None:
+            # Clean up the LLM output
             model_output = step_log.model_output.strip()
-            model_output = re.sub(r"```\s*<end_code>", "```", model_output)
-            model_output = re.sub(r"<end_code>\s*```", "```", model_output)
-            model_output = re.sub(r"```\s*\n\s*<end_code>", "```", model_output)
+            # Remove any trailing <end_code> and extra backticks, handling multiple possible formats
+            model_output = re.sub(r"```\s*<end_code>", "```", model_output)  # handles ```<end_code>
+            model_output = re.sub(r"<end_code>\s*```", "```", model_output)  # handles <end_code>```
+            model_output = re.sub(r"```\s*\n\s*<end_code>", "```", model_output)  # handles ```\n<end_code>
             model_output = model_output.strip()
             yield gr.ChatMessage(role="assistant", content=model_output)
 
+        # For tool calls, create a parent message
         if hasattr(step_log, "tool_calls") and step_log.tool_calls is not None:
             first_tool_call = step_log.tool_calls[0]
             used_code = first_tool_call.name == "python_interpreter"
             parent_id = f"call_{len(step_log.tool_calls)}"
 
+            # Tool call becomes the parent message with timing info
+            # First we will handle arguments based on type
             args = first_tool_call.arguments
-            content = str(args.get("answer", str(args))) if isinstance(args, dict) else str(args).strip()
+            if isinstance(args, dict):
+                content = str(args.get("answer", str(args)))
+            else:
+                content = str(args).strip()
 
             if used_code:
-                content = re.sub(r"```.*?\n", "", content)
-                content = re.sub(r"\s*<end_code>\s*", "", content)
+                # Clean up the content by removing any end code tags
+                content = re.sub(r"```.*?\n", "", content)  # Remove existing code blocks
+                content = re.sub(r"\s*<end_code>\s*", "", content)  # Remove end_code tags
                 content = content.strip()
                 if not content.startswith("```python"):
                     content = f"```python\n{content}\n```"
@@ -57,18 +72,28 @@ def pull_messages_from_step(step_log: MemoryStep):
             parent_message_tool = gr.ChatMessage(
                 role="assistant",
                 content=content,
-                metadata={"title": f"🛠️ Used tool {first_tool_call.name}", "id": parent_id, "status": "pending"},
+                metadata={
+                    "title": f"🛠️ Used tool {first_tool_call.name}",
+                    "id": parent_id,
+                    "status": "pending",
+                },
             )
             yield parent_message_tool
 
-            if hasattr(step_log, "observations") and step_log.observations and step_log.observations.strip():
-                log_content = re.sub(r"^Execution logs:\s*", "", step_log.observations.strip())
-                yield gr.ChatMessage(
-                    role="assistant",
-                    content=log_content,
-                    metadata={"title": "📝 Execution Logs", "parent_id": parent_id, "status": "done"},
-                )
-
+            # Nesting execution logs under the tool call if they exist
+            if hasattr(step_log, "observations") and (
+                step_log.observations is not None and step_log.observations.strip()
+            ):  # Only yield execution logs if there's actual content
+                log_content = step_log.observations.strip()
+                if log_content:
+                    log_content = re.sub(r"^Execution logs:\s*", "", log_content)
+                    yield gr.ChatMessage(
+                        role="assistant",
+                        content=f"{log_content}",
+                        metadata={"title": "📝 Execution Logs", "parent_id": parent_id, "status": "done"},
+                    )
+
+            # Nesting any errors under the tool call
             if hasattr(step_log, "error") and step_log.error is not None:
                 yield gr.ChatMessage(
                     role="assistant",
@@ -76,31 +101,46 @@ def pull_messages_from_step(step_log: MemoryStep):
                     metadata={"title": "💥 Error", "parent_id": parent_id, "status": "done"},
                 )
 
+            # Update parent message metadata to done status without yielding a new message
             parent_message_tool.metadata["status"] = "done"
 
+        # Handle standalone errors but not from tool calls
         elif hasattr(step_log, "error") and step_log.error is not None:
             yield gr.ChatMessage(role="assistant", content=str(step_log.error), metadata={"title": "💥 Error"})
 
+        # Calculate duration and token information
         step_footnote = f"{step_number}"
         if hasattr(step_log, "input_token_count") and hasattr(step_log, "output_token_count"):
-            token_str = f" | Input-tokens:{step_log.input_token_count:,} | Output-tokens:{step_log.output_token_count:,}"
+            token_str = (
+                f" | Input-tokens:{step_log.input_token_count:,} | Output-tokens:{step_log.output_token_count:,}"
+            )
             step_footnote += token_str
         if hasattr(step_log, "duration"):
             step_duration = f" | Duration: {round(float(step_log.duration), 2)}" if step_log.duration else None
             step_footnote += step_duration
-        step_footnote = f'<span style="color: #bbbbc2; font-size: 12px;">{step_footnote}</span> '
+        step_footnote = f"""<span style="color: #bbbbc2; font-size: 12px;">{step_footnote}</span> """
         yield gr.ChatMessage(role="assistant", content=f"{step_footnote}")
         yield gr.ChatMessage(role="assistant", content="-----")
 
-def stream_to_gradio(agent, task: str, reset_agent_memory: bool = False, additional_args: Optional[dict] = None):
-    if not _is_package_available("gradio"):
-        raise ModuleNotFoundError("Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`")
 
+def stream_to_gradio(
+    agent,
+    task: str,
+    reset_agent_memory: bool = False,
+    additional_args: Optional[dict] = None,
+):
+    """Runs an agent with the given task and streams the messages from the agent as gradio ChatMessages."""
+    if not _is_package_available("gradio"):
+        raise ModuleNotFoundError(
+            "Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`"
+        )
     import gradio as gr
+
     total_input_tokens = 0
     total_output_tokens = 0
 
     for step_log in agent.run(task, stream=True, reset=reset_agent_memory, additional_args=additional_args):
+        # Track tokens if model provides them
         if hasattr(agent.model, "last_input_token_count"):
             total_input_tokens += agent.model.last_input_token_count
             total_output_tokens += agent.model.last_output_token_count
@@ -108,14 +148,18 @@ def stream_to_gradio(agent, task: str, reset_agent_memory: bool = False, additio
                 step_log.input_token_count = agent.model.last_input_token_count
                 step_log.output_token_count = agent.model.last_output_token_count
 
-        for message in pull_messages_from_step(step_log):
+        for message in pull_messages_from_step(
+            step_log,
+        ):
             yield message
 
     from smolagents.agents import FinalAnswerStep
 
+    # Safely extract the final output from the last step
     if isinstance(step_log, FinalAnswerStep):
         final_output = step_log.final_answer
     else:
+        # If not a FinalAnswerStep, fallback to tool_output or tool_calls
         final_output = getattr(step_log, "tool_output", None)
         if not final_output and hasattr(step_log, "tool_calls"):
             for call in step_log.tool_calls:
@@ -123,28 +167,47 @@ def stream_to_gradio(agent, task: str, reset_agent_memory: bool = False, additio
                     final_output = call.arguments.get("answer")
                     break
 
+
     final_output = handle_agent_output_types(final_output)
 
     if isinstance(final_output, AgentText):
-        yield gr.ChatMessage(role="assistant", content=final_output.to_string().strip())
+        yield gr.ChatMessage(
+            role="assistant",
+            content=final_output.to_string().strip(),
+        )
     elif isinstance(final_output, AgentImage):
-        yield gr.ChatMessage(role="assistant", content={"path": final_output.to_string(), "mime_type": "image/png"})
+        yield gr.ChatMessage(
+            role="assistant",
+            content={"path": final_output.to_string(), "mime_type": "image/png"},
+        )
     elif isinstance(final_output, AgentAudio):
-        yield gr.ChatMessage(role="assistant", content={"path": final_output.to_string(), "mime_type": "audio/wav"})
+        yield gr.ChatMessage(
+            role="assistant",
+            content={"path": final_output.to_string(), "mime_type": "audio/wav"},
+        )
     else:
         yield gr.ChatMessage(role="assistant", content=str(final_output).strip())
 
+
+
+
 class GradioUI:
+    """A one-line interface to launch your agent in Gradio"""
+
     def __init__(self, agent: MultiStepAgent, file_upload_folder: str | None = None):
         if not _is_package_available("gradio"):
-            raise ModuleNotFoundError("Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`")
+            raise ModuleNotFoundError(
+                "Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`"
+            )
         self.agent = agent
         self.file_upload_folder = file_upload_folder
-        if self.file_upload_folder and not os.path.exists(file_upload_folder):
-            os.mkdir(file_upload_folder)
+        if self.file_upload_folder is not None:
+            if not os.path.exists(file_upload_folder):
+                os.mkdir(file_upload_folder)
 
     def interact_with_agent(self, prompt, messages):
         import gradio as gr
+
         messages.append(gr.ChatMessage(role="user", content=prompt))
         yield messages
         for msg in stream_to_gradio(self.agent, task=prompt, reset_agent_memory=False):
@@ -152,14 +215,21 @@ class GradioUI:
             yield messages
         yield messages
 
-    def upload_file(self, file, file_uploads_log, allowed_file_types=None):
+    def upload_file(
+        self,
+        file,
+        file_uploads_log,
+        allowed_file_types=[
+            "application/pdf",
+            "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+            "text/plain",
+        ],
+    ):
+        """
+        Handle file uploads, default allowed types are .pdf, .docx, and .txt
+        """
         import gradio as gr
-        if allowed_file_types is None:
-            allowed_file_types = [
-                "application/pdf",
-                "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
-                "text/plain",
-            ]
+
         if file is None:
             return gr.Textbox("No file uploaded", visible=True), file_uploads_log
 
@@ -171,18 +241,23 @@ class GradioUI:
         if mime_type not in allowed_file_types:
             return gr.Textbox("File type disallowed", visible=True), file_uploads_log
 
+        # Sanitize file name
         original_name = os.path.basename(file.name)
-        sanitized_name = re.sub(r"[^\w\-.]", "_", original_name)
+        sanitized_name = re.sub(
+            r"[^\w\-.]", "_", original_name
+        )  # Replace any non-alphanumeric, non-dash, or non-dot characters with underscores
 
         type_to_ext = {}
         for ext, t in mimetypes.types_map.items():
             if t not in type_to_ext:
                 type_to_ext[t] = ext
 
+        # Ensure the extension correlates to the mime type
         sanitized_name = sanitized_name.split(".")[:-1]
         sanitized_name.append("" + type_to_ext[mime_type])
         sanitized_name = "".join(sanitized_name)
 
+        # Save the uploaded file to the specified folder
         file_path = os.path.join(self.file_upload_folder, os.path.basename(sanitized_name))
         shutil.copy(file.name, file_path)
 
@@ -190,16 +265,17 @@ class GradioUI:
 
     def log_user_message(self, text_input, file_uploads_log):
         return (
-            text_input + (
+            text_input
+            + (
                 f"\nYou have been provided with these files, which might be helpful or not: {file_uploads_log}"
-                if len(file_uploads_log) > 0 else ""
+                if len(file_uploads_log) > 0
+                else ""
             ),
             "",
         )
 
     def launch(self, **kwargs):
         import gradio as gr
-        from submit import main as submit_answers
 
         with gr.Blocks(fill_height=True) as demo:
             stored_messages = gr.State([])
@@ -214,7 +290,7 @@ class GradioUI:
                 resizeable=True,
                 scale=1,
             )
-
+            # If an upload folder is provided, enable the upload feature
             if self.file_upload_folder is not None:
                 upload_file = gr.File(label="Upload a file")
                 upload_status = gr.Textbox(label="Upload Status", interactive=False, visible=False)
@@ -223,7 +299,6 @@ class GradioUI:
                     [upload_file, file_uploads_log],
                     [upload_status, file_uploads_log],
                 )
-
             text_input = gr.Textbox(lines=1, label="Chat Message")
             text_input.submit(
                 self.log_user_message,
@@ -231,20 +306,21 @@ class GradioUI:
                 [stored_messages, text_input],
             ).then(self.interact_with_agent, [stored_messages, chatbot], [chatbot])
 
-            with gr.Row():
-                submit_btn = gr.Button("📤 Submit All GAIA Answers")
-                submission_status = gr.Textbox(label="Submission Status", interactive=False)
-
-                def trigger_submission():
-                    submit_answers()
-                    return "✅ Submitted to GAIA scoring API"
-
-                submit_btn.click(
-                    fn=trigger_submission,
-                    inputs=[],
-                    outputs=[submission_status]
-                )
-
+            submit_btn = gr.Button("📤 Submit All GAIA Answers")
+            submission_status = gr.Textbox(label="Submission Status", interactive=False)
+
+            def trigger_submission():
+                submit_answers()
+                return "✅ Submitted to GAIA scoring API"
+            
+            submit_btn = gr.Button("📤 Submit All GAIA Answers")
+            submit_btn.click(
+                fn=trigger_submission,
+                inputs=[],
+                outputs=[submission_status]
+        )
+        
         demo.launch(debug=True, share=True, show_error=True, **kwargs)
 
-__all__ = ["stream_to_gradio", "GradioUI"]
+
+__all__ = ["stream_to_gradio", "GradioUI"]