first

chatbot.py

@@ -0,0 +1,7 @@
+from smolagents import VisitWebpageTool, InferenceClientModel, CodeAgent, ToolCallingAgent, WebSearchTool
+from gradio_ui import GradioUI
+
+model = InferenceClientModel()
+agent = CodeAgent(tools=[VisitWebpageTool(), WebSearchTool()], model=model, additional_authorized_imports=["xml.etree.ElementTree"])
+agent.prompt_templates["system_prompt"] = agent.prompt_templates["system_prompt"] + "\nTake the final result and format it as a sms text message to a customer. You are a helpful customer support assistant. Keep your answers to a 30 word limit. You are a sms text box so keep it informal. Dont show your thinking in the final message. When the user mentions a time that works for them for a scheduled appointment, reply with `Great! We can schedule your visit for that time. Please call https://www.ohiocars.com at +1 513-712-8086 to confirm your appointment. Looking forward to seeing you then!'"
+GradioUI(agent).launch()

gradio_ui.py

@@ -0,0 +1,408 @@
+import os
+import re
+import shutil
+from pathlib import Path
+
+from smolagents.agent_types import AgentAudio, AgentImage, AgentText
+from smolagents.agents import MultiStepAgent, PlanningStep
+from smolagents.memory import ActionStep, FinalAnswerStep, MemoryStep
+from smolagents.models import ChatMessageStreamDelta
+from smolagents.utils import _is_package_available
+import xml.etree.ElementTree as ET
+
+
+def get_step_footnote_content(step_log: MemoryStep, step_name: str) -> str:
+    """Get a footnote string for a step log with duration and token information"""
+    step_footnote = f"**{step_name}**"
+    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_content = f"""<span style="color: #bbbbc2; font-size: 12px;">{step_footnote}</span> """
+    return step_footnote_content
+
+
+def pull_messages_from_step(step_log: MemoryStep, skip_model_outputs: bool = False):
+    """Extract ChatMessage objects from agent steps with proper nesting.
+
+    Args:
+        step_log: The step log to display as gr.ChatMessage objects.
+        skip_model_outputs: If True, skip the model outputs when creating the gr.ChatMessage objects:
+            This is used for instance when streaming model outputs have already been displayed.
+    """
+    if not _is_package_available("gradio"):
+        raise ModuleNotFoundError(
+            "Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`"
+        )
+    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 "Step"
+
+        # First yield the thought/reasoning from the LLM
+        if not skip_model_outputs:
+            yield gr.ChatMessage(role="assistant", content=f"**{step_number}**", metadata={"status": "done"})
+        elif skip_model_outputs and hasattr(step_log, "model_output") and step_log.model_output is not None:
+            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, metadata={"status": "done"})
+
+        # 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"
+
+            # 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}",
+                    "status": "done",
+                },
+            )
+            yield parent_message_tool
+
+        # Display execution logs 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", "status": "done"},
+                )
+
+        # Display any errors
+        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", "status": "done"},
+            )
+
+        # Update parent message metadata to done status without yielding a new message
+        if getattr(step_log, "observations_images", []):
+            for image in step_log.observations_images:
+                path_image = AgentImage(image).to_string()
+                yield gr.ChatMessage(
+                    role="assistant",
+                    content={"path": path_image, "mime_type": f"image/{path_image.split('.')[-1]}"},
+                    metadata={"title": "🖼️ Output Image", "status": "done"},
+                )
+
+        # Handle standalone errors but not from tool calls
+        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", "status": "done"}
+            )
+
+        yield gr.ChatMessage(
+            role="assistant", content=get_step_footnote_content(step_log, step_number), metadata={"status": "done"}
+        )
+        yield gr.ChatMessage(role="assistant", content="-----", metadata={"status": "done"})
+
+    elif isinstance(step_log, PlanningStep):
+        yield gr.ChatMessage(role="assistant", content="**Planning step**", metadata={"status": "done"})
+        yield gr.ChatMessage(role="assistant", content=step_log.plan, metadata={"status": "done"})
+        yield gr.ChatMessage(
+            role="assistant", content=get_step_footnote_content(step_log, "Planning step"), metadata={"status": "done"}
+        )
+        yield gr.ChatMessage(role="assistant", content="-----", metadata={"status": "done"})
+
+    elif isinstance(step_log, FinalAnswerStep):
+        final_answer = step_log.final_answer
+        if isinstance(final_answer, AgentText):
+            yield gr.ChatMessage(
+                role="assistant",
+                content=f"**Final answer:**\n{final_answer.to_string()}\n",
+                metadata={"status": "done"},
+            )
+        elif isinstance(final_answer, AgentImage):
+            yield gr.ChatMessage(
+                role="assistant",
+                content={"path": final_answer.to_string(), "mime_type": "image/png"},
+                metadata={"status": "done"},
+            )
+        elif isinstance(final_answer, AgentAudio):
+            yield gr.ChatMessage(
+                role="assistant",
+                content={"path": final_answer.to_string(), "mime_type": "audio/wav"},
+                metadata={"status": "done"},
+            )
+        else:
+            yield gr.ChatMessage(
+                role="assistant", content=f"**Final answer:** {str(final_answer)}", metadata={"status": "done"}
+            )
+
+    else:
+        raise ValueError(f"Unsupported step type: {type(step_log)}")
+
+
+def stream_to_gradio(
+    agent,
+    task: str,
+    task_images: list | None = None,
+    reset_agent_memory: bool = False,
+    additional_args: dict | None = None,
+):
+    """Runs an agent with the given task and streams the messages from the agent as gradio ChatMessages."""
+    total_input_tokens = 0
+    total_output_tokens = 0
+
+    if not _is_package_available("gradio"):
+        raise ModuleNotFoundError(
+            "Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`"
+        )
+
+    intermediate_text = ""
+
+    for step_log in agent.run(
+        task, images=task_images, stream=True, reset=reset_agent_memory, additional_args=additional_args
+    ):
+        # Track tokens if model provides them
+        if getattr(agent.model, "last_input_token_count", None) is not None:
+            total_input_tokens += agent.model.last_input_token_count
+            total_output_tokens += agent.model.last_output_token_count
+            if isinstance(step_log, (ActionStep, PlanningStep)):
+                step_log.input_token_count = agent.model.last_input_token_count
+                step_log.output_token_count = agent.model.last_output_token_count
+
+        if isinstance(step_log, MemoryStep):
+            intermediate_text = ""
+            for message in pull_messages_from_step(
+                step_log,
+                # If we're streaming model outputs, no need to display them twice
+                skip_model_outputs=getattr(agent, "stream_outputs", False),
+            ):
+                yield message
+        elif isinstance(step_log, ChatMessageStreamDelta):
+            intermediate_text += step_log.content or ""
+            yield intermediate_text
+
+def extract_vehicle_info_as_string(adf_xml):
+    root = ET.fromstring(adf_xml)
+    
+    # Find the vehicle element
+    vehicle = root.find('.//vehicle')
+    
+    if vehicle is not None:
+        year = vehicle.find('year').text if vehicle.find('year') is not None else ""
+        make = vehicle.find('make').text if vehicle.find('make') is not None else ""
+        model = vehicle.find('model').text if vehicle.find('model') is not None else ""
+        vehicle_info = f"{year} {make} {model}".strip()
+    
+    # Extract first name
+    first_name = ""
+    name_element = root.find('.//name[@part="first"]')
+    if name_element is not None:
+        first_name = name_element.text.strip() if name_element.text else ""
+    return first_name, vehicle_info
+
+
+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 = Path(file_upload_folder) if file_upload_folder is not None else None
+        self.name = getattr(agent, "name") or "OTTO: The Car Sales Agent"
+        self.description = getattr(agent, "description", None)
+        if self.file_upload_folder is not None:
+            if not self.file_upload_folder.exists():
+                self.file_upload_folder.mkdir(parents=True, exist_ok=True)
+
+    def interact_with_agent(self, prompt, messages, session_state, car_site, adf_lead):
+        import gradio as gr
+        self.agent.prompt_templates["system_prompt"] += f"\n\nWhen answering a customer's question about the dealership or other cars, use the following site to find the information:\n\nDealership Site: {car_site}\n\nWhen answering a customer's question about the specific car use the following ADF Lead:\n\nADF Lead: {adf_lead}"
+        # 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, metadata={"status": "done"}))
+            yield messages
+
+            for msg in stream_to_gradio(session_state["agent"], task=prompt, reset_agent_memory=False):
+                if isinstance(msg, gr.ChatMessage):
+                    messages.append(msg)
+                elif isinstance(msg, str):  # Then it's only a completion delta
+                    try:
+                        if messages[-1].metadata["status"] == "pending":
+                            messages[-1].content = msg
+                        else:
+                            messages.append(
+                                gr.ChatMessage(role="assistant", content=msg, metadata={"status": "pending"})
+                            )
+                    except Exception as e:
+                        raise e
+                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):
+        self.create_app().launch(debug=True, share=share, **kwargs)
+
+    def create_app(self):
+        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('_', ' ')}"
+                    "\n> Test the OTTO Agent by asking it questions."
+                    + (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")
+                    with gr.Accordion("Dealership Info", open=False):
+                        car_site = gr.Textbox(label="Car Gurus Dealership Site", lines=2, value="https://www.cargurus.com/Cars/m-Ohio-Cars-sp458596", interactive=True)
+                        adf_lead = gr.Textbox(label="ADF Lead", lines=4, value="<?xml version=\"1.0\"?><?ADF version=\"1.0\"?><adf><prospect><requestdate>2025-05-12T13:59:30</requestdate><vehicle status=\"used\"><id source=\"CarsForSale.com\">16f3114e-825f-4eb0-8165-ce43fe5143b6</id><year>2016</year><make>Toyota</make><model>Corolla</model><vin>5YFBURHE4GP511115</vin><stock></stock><comments>DP</comments><colorcombination><exteriorcolor>Super White</exteriorcolor></colorcombination><miles>131024.0</miles><price type=\"asking\">9950</price></vehicle><customer><contact><name part=\"first\">Test</name><name part=\"last\">Lead</name><name part=\"full\">Test Lead</name><email>123@gmail.com</email><phone>2582584568</phone><address><city></city><state></state><postalcode></postalcode></address></contact><comments><![CDATA[I'm interested and want to know more about the 2016 Toyota Corolla S Plus you have listed for $9,950 on Cars For Sale.]]></comments><timeframe><description></description></timeframe></customer><provider><id>19971</id><name part=\"full\">Carsforsale.com</name><service>Carsforsale.com</service><phone>866-388-9778</phone></provider><vendor><id>114483</id><vendorname>Ohio Cars</vendorname></vendor></prospect></adf>", interactive=False)
+                # 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],
+                    )
+
+            first_name, vehicle_info = extract_vehicle_info_as_string(adf_lead.value)
+            message = gr.ChatMessage(role="assistant", content=f"Hi {first_name}! The {vehicle_info} you're interested in is available at [OhioCars.com](https://www.ohiocars.com). Would you like to schedule a visit to check it out? We have appointment slots at 11 AM, 1 PM, or 3 PM. Which time works best for you?", metadata={"status": "done"})
+            # Main chat interface
+            chatbot = gr.Chatbot(
+                label="Agent",
+                type="messages",
+                value=[message],
+                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, car_site, adf_lead], [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, car_site, adf_lead], [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],
+            )
+
+        return demo
+
+
+__all__ = ["stream_to_gradio", "GradioUI"]

requirements.txt

@@ -0,0 +1,3 @@
+smolagents
+gradio
+xml