Update Gradio_UI.py

Gradio_UI.py

@@ -1,288 +1,58 @@
-import mimetypes
-import os
-import re
-import shutil
+import gradio as gr
 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):
-    """Extract ChatMessage objects from agent steps with proper nesting"""
-    import gradio as gr
-
-    if isinstance(step_log, ActionStep):
-        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}**")
-
-        if hasattr(step_log, "model_output") and step_log.model_output is not None:
-            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)
-            model_output = model_output.strip()
-            yield gr.ChatMessage(role="assistant", content=model_output)
-
-        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)}"
-
-            args = first_tool_call.arguments
-            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)
-                content = content.strip()
-                if not content.startswith("```python"):
-                    content = f"```python\n{content}\n```"
-
-            parent_message_tool = gr.ChatMessage(
-                role="assistant",
-                content=content,
-                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 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)
-                    yield gr.ChatMessage(
-                        role="assistant",
-                        content=f"{log_content}",
-                        metadata={"title": "📝 Execution Logs", "parent_id": parent_id, "status": "done"},
-                    )
-
-            if hasattr(step_log, "error") and step_log.error is not None:
-                yield gr.ChatMessage(
-                    role="assistant",
-                    content=str(step_log.error),
-                    metadata={"title": "💥 Error", "parent_id": parent_id, "status": "done"},
-                )
-
-            parent_message_tool.metadata["status"] = "done"
-
-        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"})
-
-        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:,}"
-            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> """
-        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):
-    """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]'`")
-    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):
-        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
-            if isinstance(step_log, ActionStep):
-                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):
-            yield message
-
-    final_answer = step_log
-    final_answer = handle_agent_output_types(final_answer)
-
-    if isinstance(final_answer, AgentText):
-        yield gr.ChatMessage(
-            role="assistant",
-            content=f"**Final answer:**\n{final_answer.to_string()}\n",
-        )
-    elif isinstance(final_answer, AgentImage):
-        yield gr.ChatMessage(
-            role="assistant",
-            content={"path": final_answer.to_string(), "mime_type": "image/png"},
-        )
-    elif isinstance(final_answer, AgentAudio):
-        yield gr.ChatMessage(
-            role="assistant",
-            content={"path": final_answer.to_string(), "mime_type": "audio/wav"},
-        )
-    else:
-        yield gr.ChatMessage(role="assistant", content=f"**Final answer:** {str(final_answer)}")
 
 class GradioUI:
-    """Custom Gradio interface for the agent with specialized tools"""
+    """Simplified Gradio interface for Hugging Face Spaces"""
     
-    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]'`")
+    def __init__(self, agent: MultiStepAgent):
         self.agent = agent
-        self.file_upload_folder = file_upload_folder
-        if self.file_upload_folder is not None:
-            if not os.path.exists(file_upload_folder):
-                os.makedirs(file_upload_folder, exist_ok=True)
 
-    def launch(self, **kwargs):
-        import gradio as gr
-        
-        with gr.Blocks(title="Multi-Tool AI Assistant", theme=gr.themes.Soft(), fill_height=True) as demo:
-            # Header with capabilities overview
+    def launch(self):
+        with gr.Blocks(title="AI Assistant", theme=gr.themes.Soft()) as demo:
             gr.Markdown("""
-            # 🛠️ Multi-Tool AI Assistant
-            
-            This assistant specializes in:
-            - **Time zone conversions** (e.g., "What time is 3pm EST in Tokyo?")
-            - **Weather lookups** (e.g., "What's the weather in Paris?")
-            - **Unit conversions** (e.g., "Convert 50 miles to kilometers")
-            - **Web search** (e.g., "Find recent news about AI")
-            - **Image generation** (e.g., "Create an image of a futuristic city")
-            - **Code execution** (e.g., "Calculate factorial of 5")
+            # 🤖 AI Assistant
+            **Capabilities:**
+            - Time zone conversions
+            - Weather lookups
+            - Unit conversions
+            - Web search
+            - Image generation
+            - Code execution
             """)
             
-            # State management
-            stored_messages = gr.State([])
-            file_uploads_log = gr.State([])
-            
-            # Chat interface
-            with gr.Row():
-                chatbot = gr.Chatbot(
-                    label="Conversation",
-                    avatar_images=(
-                        None,
-                        "https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/communication/Alfred.png",
-                    ),
-                    height=500,
-                    render=True,
-                    bubble_full_width=False
+            chatbot = gr.Chatbot(
+                height=500,
+                avatar_images=(
+                    None,
+                    "https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/communication/Alfred.png",
                 )
+            )
             
-            # File upload and input section
             with gr.Row():
-                if self.file_upload_folder is not None:
-                    with gr.Column(scale=1):
-                        upload_file = gr.File(
-                            label="Upload documents (PDF, DOCX, TXT)",
-                            file_types=[".pdf", ".docx", ".txt"],
-                            height=100
-                        )
-                        upload_status = gr.Textbox(
-                            label="Upload Status",
-                            interactive=False,
-                            visible=False
-                        )
+                msg = gr.Textbox(
+                    placeholder="Ask me anything...",
+                    container=False,
+                    scale=7
+                )
+                submit = gr.Button("Send", scale=1)
+            
+            def respond(message, chat_history):
+                chat_history.append((message, ""))
+                full_response = ""
+                for step_log in self.agent.run(message, stream=True):
+                    if isinstance(step_log, ActionStep):
+                        if hasattr(step_log, 'model_output') and step_log.model_output:
+                            full_response += step_log.model_output + "\n"
+                        if hasattr(step_log, 'observations') and step_log.observations:
+                            full_response += step_log.observations + "\n"
                 
-                with gr.Column(scale=4):
-                    text_input = gr.Textbox(
-                        placeholder="Type your question or request here...",
-                        label="Your message",
-                        lines=2,
-                        max_lines=5,
-                        container=False
-                    )
-            
-            # Control buttons
-            with gr.Row():
-                submit_btn = gr.Button("Send", variant="primary")
-                clear_btn = gr.Button("Clear Chat")
-            
-            # Event handlers
-            upload_file.change(
-                self.upload_file,
-                [upload_file, file_uploads_log],
-                [upload_status, file_uploads_log],
-            )
-            
-            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]
-            )
+                chat_history[-1] = (message, full_response)
+                return "", chat_history
             
-            clear_btn.click(
-                lambda: (None, [], []),
-                outputs=[chatbot, stored_messages, file_uploads_log]
-            )
-
-        demo.launch(**kwargs)
-
-    def upload_file(self, file, file_uploads_log, allowed_file_types=[
-            "application/pdf", 
-            "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
-            "text/plain"]):
-        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
-
-        original_name = os.path.basename(file.name)
-        sanitized_name = re.sub(r"[^\w\-.]", "_", original_name)
-
-        type_to_ext = {}
-        for ext, t in mimetypes.types_map.items():
-            if t not in type_to_ext:
-                type_to_ext[t] = ext
-
-        sanitized_name = sanitized_name.split(".")[:-1]
-        sanitized_name.append("" + type_to_ext[mime_type])
-        sanitized_name = "".join(sanitized_name)
-
-        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 ""
-            ),
-            "",
-        )
+            msg.submit(respond, [msg, chatbot], [msg, chatbot])
+            submit.click(respond, [msg, chatbot], [msg, chatbot])
 
-    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
+        demo.launch()