Update Gradio_UI.py

Gradio_UI.py

@@ -3,43 +3,32 @@ import os
 import re
 import shutil
 from typing import Optional
-
 from smolagents.agent_types import AgentAudio, AgentImage, AgentText, handle_agent_output_types
 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()
-            # 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 = 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)
             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
             if isinstance(args, dict):
                 content = str(args.get("answer", str(args)))
@@ -47,9 +36,8 @@ def pull_messages_from_step(
                 content = str(args).strip()
 
             if used_code:
-                # 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 = re.sub(r"```.*?\n", "", content)
+                content = re.sub(r"\s*<end_code>\s*", "", content)
                 content = content.strip()
                 if not content.startswith("```python"):
                     content = f"```python\n{content}\n```"
@@ -65,10 +53,7 @@ def pull_messages_from_step(
             )
             yield parent_message_tool
 
-            # 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
+            if hasattr(step_log, "observations") and (step_log.observations is not None and step_log.observations.strip()):
                 log_content = step_log.observations.strip()
                 if log_content:
                     log_content = re.sub(r"^Execution logs:\s*", "", log_content)
@@ -78,7 +63,6 @@ def pull_messages_from_step(
                         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",
@@ -86,19 +70,14 @@ def pull_messages_from_step(
                     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
@@ -107,25 +86,16 @@ def pull_messages_from_step(
         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,
-):
-    """Runs an agent with the given task and streams the messages from the agent as gradio ChatMessages."""
+def stream_to_gradio(agent, task: str, reset_agent_memory: bool = False, additional_args: Optional[dict] = None):
+    """Stream agent responses to Gradio interface"""
     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]'`")
     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
@@ -133,12 +103,10 @@ def stream_to_gradio(
                 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
 
-    final_answer = step_log  # Last log is the run's final_answer
+    final_answer = step_log
     final_answer = handle_agent_output_types(final_answer)
 
     if isinstance(final_answer, AgentText):
@@ -159,15 +127,12 @@ def stream_to_gradio(
     else:
         yield gr.ChatMessage(role="assistant", content=f"**Final answer:** {str(final_answer)}")
 
-
 class GradioUI:
-    """A one-line interface to launch your agent in Gradio"""
-
+    """Custom Gradio interface for the agent with specialized tools"""
+    
     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 is not None:
@@ -176,172 +141,6 @@ class GradioUI:
 
     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):
-            messages.append(msg)
-            yield messages
-        yield messages
-
-    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 file is None:
-            return gr.Textbox("No file uploaded", visible=True), file_uploads_log
-
-        try:
-            mime_type, _ = mimetypes.guess_type(file.name)
-        except Exception as e:
-            return gr.Textbox(f"Error: {e}", visible=True), file_uploads_log
-
-        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
-        )  # 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)
-
-        return gr.Textbox(f"File uploaded: {file_path}", visible=True), file_uploads_log + [file_path]
-
-    def log_user_message(self, text_input, file_uploads_log):
-        return (
-            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 ""
-            ),
-            "",
-        )
-
-    def launch(self, **kwargs):
-        import gradio as gr
-
-        with gr.Blocks(title="AI Assistant", fill_height=True) as demo:
-            # Welcome message and description
-            gr.Markdown("""
-            # 🤖 Welcome to Your AI Assistant
-            
-            This intelligent agent can help you with various tasks by:
-            - Answering questions
-            - Processing text and documents
-            - Performing calculations and analysis
-            - And much more!
-            
-            Simply type your request below and the AI will assist you.
-            """)
-            
-            # File upload section
-            with gr.Accordion("ℹ️ How to use", open=False):
-                gr.Markdown("""
-                **Getting started:**
-                1. Type your question or request in the chat box
-                2. Optionally upload files (PDF, DOCX, TXT) for the AI to reference
-                3. Press Enter or click Send to get your answer
-                
-                **Examples to try:**
-                - "Explain quantum computing in simple terms"
-                - "Summarize the key points from this document" (after uploading)
-                - "Write a poem about artificial intelligence"
-                """)
-
-            stored_messages = gr.State([])
-            file_uploads_log = gr.State([])
-            
-            # Chat interface
-            chatbot = gr.Chatbot(
-                label="Chat with AI Assistant",
-                type="messages",
-                avatar_images=(
-                    None,
-                    "https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/communication/Alfred.png",
-                ),
-                height=500,
-                render=True,
-            )
-            
-            # File upload functionality
-            if self.file_upload_folder is not None:
-                with gr.Row():
-                    upload_file = gr.File(
-                        label="Upload documents (PDF, DOCX, TXT)",
-                        file_types=[".pdf", ".docx", ".txt"]
-                    )
-                    upload_status = gr.Textbox(
-                        label="Upload Status",
-                        interactive=False,
-                        visible=False
-                    )
-                
-                upload_file.change(
-                    self.upload_file,
-                    [upload_file, file_uploads_log],
-                    [upload_status, file_uploads_log],
-                )
-            
-            # Input area
-            with gr.Row():
-                text_input = gr.Textbox(
-                    placeholder="Type your message here...",
-                    label="Your message",
-                    lines=2,
-                    max_lines=5,
-                    container=False,
-                    scale=7
-                )
-                submit_btn = gr.Button("Send", variant="primary", scale=1)
-            
-            # Interaction handlers
-            text_input.submit(
-                self.log_user_message,
-                [text_input, file_uploads_log],
-                [stored_messages, text_input],
-            ).then(
-                self.interact_with_agent,
-                [stored_messages, chatbot],
-                [chatbot]
-            )
-            
-            submit_btn.click(
-                self.log_user_message,
-                [text_input, file_uploads_log],
-                [stored_messages, text_input],
-            ).then(
-                self.interact_with_agent,
-                [stored_messages, chatbot],
-                [chatbot]
-            )
-
-        demo.launch(debug=True, share=True, **kwargs)
-
-
-__all__ = ["stream_to_gradio", "GradioUI"]
+