feat: gradio ui chat integration with LangGraph `invoke`

gradio_ui_langgraph.py

@@ -0,0 +1,336 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+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,
+):
+    """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 = 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)))
+            else:
+                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 = 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
+
+            # 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"```bash\n{log_content}\n",
+                        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",
+                    content=str(step_log.error),
+                    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:,}"
+            )
+            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="-----", metadata={"status": "done"})
+
+
+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
+
+    # todo: change run for invoke and create AgentText out of final answer
+
+    response = agent.invoke({"messages": task})
+
+    # Show the messages
+    for m in response['messages']:
+        m.pretty_print()
+
+    final_answer = response['messages'][-1].content
+    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:
+    """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]'`"
+            )
+        self.agent = agent
+        self.file_upload_folder = file_upload_folder
+        self.name = getattr(agent, "name") or "Agent interface"
+        self.description = getattr(agent, "description", None)
+        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, session_state):
+        import gradio as gr
+
+        # Get the agent type from the template agent
+        if "agent" not in session_state:
+            session_state["agent"] = self.agent
+
+        try:
+            messages.append(gr.ChatMessage(role="user", content=prompt))
+            yield messages
+
+            for msg in stream_to_gradio(session_state["agent"], task=prompt, reset_agent_memory=False):
+                messages.append(msg)
+                yield messages
+
+            yield messages
+        except Exception as e:
+            print(f"Error in interaction: {str(e)}")
+            messages.append(gr.ChatMessage(role="assistant", content=f"Error: {str(e)}"))
+            yield messages
+
+    def upload_file(self, file, file_uploads_log, allowed_file_types=None):
+        """
+        Handle file uploads, default allowed types are .pdf, .docx, and .txt
+        """
+        import gradio as gr
+
+        if file is None:
+            return gr.Textbox(value="No file uploaded", visible=True), file_uploads_log
+
+        if allowed_file_types is None:
+            allowed_file_types = [".pdf", ".docx", ".txt"]
+
+        file_ext = os.path.splitext(file.name)[1].lower()
+        if file_ext 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
+
+        # 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):
+        import gradio as gr
+
+        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 ""
+            ),
+            "",
+            gr.Button(interactive=False),
+        )
+
+    def launch(self, share: bool = True, **kwargs):
+        import gradio as gr
+
+        with gr.Blocks(theme="ocean", fill_height=True) as demo:
+            # Add session state to store session-specific data
+            session_state = gr.State({})
+            stored_messages = gr.State([])
+            file_uploads_log = gr.State([])
+
+            with gr.Sidebar():
+                gr.Markdown(
+                    f"# {self.name.replace('_', ' ').capitalize()}"
+                    "\n> This web ui allows you to interact with a `smolagents` agent that can use tools and execute steps to complete tasks."
+                    + (f"\n\n**Agent description:**\n{self.description}" if self.description else "")
+                )
+
+                with gr.Group():
+                    gr.Markdown("**Your request**", container=True)
+                    text_input = gr.Textbox(
+                        lines=3,
+                        label="Chat Message",
+                        container=False,
+                        placeholder="Enter your prompt here and press Shift+Enter or press the button",
+                    )
+                    submit_btn = gr.Button("Submit", variant="primary")
+
+                # 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)
+                    upload_file.change(
+                        self.upload_file,
+                        [upload_file, file_uploads_log],
+                        [upload_status, file_uploads_log],
+                    )
+
+                gr.HTML("<br><br><h4><center>Powered by:</center></h4>")
+                with gr.Row():
+                    gr.HTML("""<div style="display: flex; align-items: center; gap: 8px; font-family: system-ui, -apple-system, sans-serif;">
+            <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolagents/mascot_smol.png" style="width: 32px; height: 32px; object-fit: contain;" alt="logo">
+            <a target="_blank" href="https://github.com/huggingface/smolagents"><b>huggingface/smolagents</b></a>
+            </div>""")
+
+            # Main chat interface
+            chatbot = gr.Chatbot(
+                label="Agent",
+                type="messages",
+                avatar_images=(
+                    None,
+                    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolagents/mascot_smol.png",
+                ),
+                resizeable=True,
+                scale=1,
+            )
+
+            # Set up event handlers
+            text_input.submit(
+                self.log_user_message,
+                [text_input, file_uploads_log],
+                [stored_messages, text_input, submit_btn],
+            ).then(self.interact_with_agent, [stored_messages, chatbot, session_state], [chatbot]).then(
+                lambda: (
+                    gr.Textbox(
+                        interactive=True, placeholder="Enter your prompt here and press Shift+Enter or the button"
+                    ),
+                    gr.Button(interactive=True),
+                ),
+                None,
+                [text_input, submit_btn],
+            )
+
+            submit_btn.click(
+                self.log_user_message,
+                [text_input, file_uploads_log],
+                [stored_messages, text_input, submit_btn],
+            ).then(self.interact_with_agent, [stored_messages, chatbot, session_state], [chatbot]).then(
+                lambda: (
+                    gr.Textbox(
+                        interactive=True, placeholder="Enter your prompt here and press Shift+Enter or the button"
+                    ),
+                    gr.Button(interactive=True),
+                ),
+                None,
+                [text_input, submit_btn],
+            )
+
+        demo.launch(debug=True, share=share, **kwargs)
+
+
+__all__ = ["stream_to_gradio", "GradioUI"]