SwarajPatil/Python_code_fixer

1#!/usr/bin/env python2# coding=utf-83# Copyright 2024 The HuggingFace Inc. team. All rights reserved.4#5# Licensed under the Apache License, Version 2.0 (the "License");6# you may not use this file except in compliance with the License.7# You may obtain a copy of the License at8#9#     http://www.apache.org/licenses/LICENSE-2.010#11# Unless required by applicable law or agreed to in writing, software12# distributed under the License is distributed on an "AS IS" BASIS,13# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.14# See the License for the specific language governing permissions and15# limitations under the License.16import mimetypes17import os18import re19import shutil20from typing import Optional21 22from smolagents.agent_types import AgentAudio, AgentImage, AgentText, handle_agent_output_types23from smolagents.agents import ActionStep, MultiStepAgent24from smolagents.memory import MemoryStep25from smolagents.utils import _is_package_available26 27 28def pull_messages_from_step(29    step_log: MemoryStep,30):31    """Extract ChatMessage objects from agent steps with proper nesting"""32    import gradio as gr33 34    if isinstance(step_log, ActionStep):35        # Output the step number36        step_number = f"Step {step_log.step_number}" if step_log.step_number is not None else ""37        yield gr.ChatMessage(role="assistant", content=f"**{step_number}**")38 39        # First yield the thought/reasoning from the LLM40        if hasattr(step_log, "model_output") and step_log.model_output is not None:41            # Clean up the LLM output42            model_output = step_log.model_output.strip()43            # Remove any trailing <end_code> and extra backticks, handling multiple possible formats44            model_output = re.sub(r"```\s*<end_code>", "```", model_output)  # handles ```<end_code>45            model_output = re.sub(r"<end_code>\s*```", "```", model_output)  # handles <end_code>```46            model_output = re.sub(r"```\s*\n\s*<end_code>", "```", model_output)  # handles ```\n<end_code>47            model_output = model_output.strip()48            yield gr.ChatMessage(role="assistant", content=model_output)49 50        # For tool calls, create a parent message51        if hasattr(step_log, "tool_calls") and step_log.tool_calls is not None:52            first_tool_call = step_log.tool_calls[0]53            used_code = first_tool_call.name == "python_interpreter"54            parent_id = f"call_{len(step_log.tool_calls)}"55 56            # Tool call becomes the parent message with timing info57            # First we will handle arguments based on type58            args = first_tool_call.arguments59            if isinstance(args, dict):60                content = str(args.get("answer", str(args)))61            else:62                content = str(args).strip()63 64            if used_code:65                # Clean up the content by removing any end code tags66                content = re.sub(r"```.*?\n", "", content)  # Remove existing code blocks67                content = re.sub(r"\s*<end_code>\s*", "", content)  # Remove end_code tags68                content = content.strip()69                if not content.startswith("```python"):70                    content = f"```python\n{content}\n```"71 72            parent_message_tool = gr.ChatMessage(73                role="assistant",74                content=content,75                metadata={76                    "title": f"🛠️ Used tool {first_tool_call.name}",77                    "id": parent_id,78                    "status": "pending",79                },80            )81            yield parent_message_tool82 83            # Nesting execution logs under the tool call if they exist84            if hasattr(step_log, "observations") and (85                step_log.observations is not None and step_log.observations.strip()86            ):  # Only yield execution logs if there's actual content87                log_content = step_log.observations.strip()88                if log_content:89                    log_content = re.sub(r"^Execution logs:\s*", "", log_content)90                    yield gr.ChatMessage(91                        role="assistant",92                        content=f"{log_content}",93                        metadata={"title": "📝 Execution Logs", "parent_id": parent_id, "status": "done"},94                    )95 96            # Nesting any errors under the tool call97            if hasattr(step_log, "error") and step_log.error is not None:98                yield gr.ChatMessage(99                    role="assistant",100                    content=str(step_log.error),101                    metadata={"title": "💥 Error", "parent_id": parent_id, "status": "done"},102                )103 104            # Update parent message metadata to done status without yielding a new message105            parent_message_tool.metadata["status"] = "done"106 107        # Handle standalone errors but not from tool calls108        elif hasattr(step_log, "error") and step_log.error is not None:109            yield gr.ChatMessage(role="assistant", content=str(step_log.error), metadata={"title": "💥 Error"})110 111        # Calculate duration and token information112        step_footnote = f"{step_number}"113        if hasattr(step_log, "input_token_count") and hasattr(step_log, "output_token_count"):114            token_str = (115                f" | Input-tokens:{step_log.input_token_count:,} | Output-tokens:{step_log.output_token_count:,}"116            )117            step_footnote += token_str118        if hasattr(step_log, "duration"):119            step_duration = f" | Duration: {round(float(step_log.duration), 2)}" if step_log.duration else None120            step_footnote += step_duration121        step_footnote = f"""<span style="color: #bbbbc2; font-size: 12px;">{step_footnote}</span> """122        yield gr.ChatMessage(role="assistant", content=f"{step_footnote}")123        yield gr.ChatMessage(role="assistant", content="-----")124 125 126def stream_to_gradio(127    agent,128    task: str,129    reset_agent_memory: bool = False,130    additional_args: Optional[dict] = None,131):132    """Runs an agent with the given task and streams the messages from the agent as gradio ChatMessages."""133    if not _is_package_available("gradio"):134        raise ModuleNotFoundError(135            "Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`"136        )137    import gradio as gr138 139    total_input_tokens = 0140    total_output_tokens = 0141 142    for step_log in agent.run(task, stream=True, reset=reset_agent_memory, additional_args=additional_args):143        # Track tokens if model provides them144        if hasattr(agent.model, "last_input_token_count"):145            total_input_tokens += agent.model.last_input_token_count146            total_output_tokens += agent.model.last_output_token_count147            if isinstance(step_log, ActionStep):148                step_log.input_token_count = agent.model.last_input_token_count149                step_log.output_token_count = agent.model.last_output_token_count150 151        for message in pull_messages_from_step(152            step_log,153        ):154            yield message155 156    final_answer = step_log  # Last log is the run's final_answer157    final_answer = handle_agent_output_types(final_answer)158 159    if isinstance(final_answer, AgentText):160        yield gr.ChatMessage(161            role="assistant",162            content=f"**Final answer:**\n{final_answer.to_string()}\n",163        )164    elif isinstance(final_answer, AgentImage):165        yield gr.ChatMessage(166            role="assistant",167            content={"path": final_answer.to_string(), "mime_type": "image/png"},168        )169    elif isinstance(final_answer, AgentAudio):170        yield gr.ChatMessage(171            role="assistant",172            content={"path": final_answer.to_string(), "mime_type": "audio/wav"},173        )174    else:175        yield gr.ChatMessage(role="assistant", content=f"**Final answer:** {str(final_answer)}")176 177 178class GradioUI:179    """A one-line interface to launch your agent in Gradio"""180 181    def __init__(self, agent: MultiStepAgent, file_upload_folder: str | None = None):182        if not _is_package_available("gradio"):183            raise ModuleNotFoundError(184                "Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`"185            )186        self.agent = agent187        self.file_upload_folder = file_upload_folder188        if self.file_upload_folder is not None:189            if not os.path.exists(file_upload_folder):190                os.mkdir(file_upload_folder)191 192    def interact_with_agent(self, prompt, messages):193        import gradio as gr194 195        messages.append(gr.ChatMessage(role="user", content=prompt))196        yield messages197        for msg in stream_to_gradio(self.agent, task=prompt, reset_agent_memory=False):198            messages.append(msg)199            yield messages200        yield messages201 202    def upload_file(203        self,204        file,205        file_uploads_log,206        allowed_file_types=[207            "application/pdf",208            "application/vnd.openxmlformats-officedocument.wordprocessingml.document",209            "text/plain",210        ],211    ):212        """213        Handle file uploads, default allowed types are .pdf, .docx, and .txt214        """215        import gradio as gr216 217        if file is None:218            return gr.Textbox("No file uploaded", visible=True), file_uploads_log219 220        try:221            mime_type, _ = mimetypes.guess_type(file.name)222        except Exception as e:223            return gr.Textbox(f"Error: {e}", visible=True), file_uploads_log224 225        if mime_type not in allowed_file_types:226            return gr.Textbox("File type disallowed", visible=True), file_uploads_log227 228        # Sanitize file name229        original_name = os.path.basename(file.name)230        sanitized_name = re.sub(231            r"[^\w\-.]", "_", original_name232        )  # Replace any non-alphanumeric, non-dash, or non-dot characters with underscores233 234        type_to_ext = {}235        for ext, t in mimetypes.types_map.items():236            if t not in type_to_ext:237                type_to_ext[t] = ext238 239        # Ensure the extension correlates to the mime type240        sanitized_name = sanitized_name.split(".")[:-1]241        sanitized_name.append("" + type_to_ext[mime_type])242        sanitized_name = "".join(sanitized_name)243 244        # Save the uploaded file to the specified folder245        file_path = os.path.join(self.file_upload_folder, os.path.basename(sanitized_name))246        shutil.copy(file.name, file_path)247 248        return gr.Textbox(f"File uploaded: {file_path}", visible=True), file_uploads_log + [file_path]249 250    def log_user_message(self, text_input, file_uploads_log):251        return (252            text_input253            + (254                f"\nYou have been provided with these files, which might be helpful or not: {file_uploads_log}"255                if len(file_uploads_log) > 0256                else ""257            ),258            "",259        )260 261    def launch(self, **kwargs):262        import gradio as gr263 264        with gr.Blocks(fill_height=True) as demo:265            stored_messages = gr.State([])266            file_uploads_log = gr.State([])267            chatbot = gr.Chatbot(268                label="Agent",269                type="messages",270                avatar_images=(271                    None,272                    "https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/communication/Alfred.png",273                ),274                resizeable=True,275                scale=1,276            )277            # If an upload folder is provided, enable the upload feature278            if self.file_upload_folder is not None:279                upload_file = gr.File(label="Upload a file")280                upload_status = gr.Textbox(label="Upload Status", interactive=False, visible=False)281                upload_file.change(282                    self.upload_file,283                    [upload_file, file_uploads_log],284                    [upload_status, file_uploads_log],285                )286            text_input = gr.Textbox(lines=1, label="Chat Message")287            text_input.submit(288                self.log_user_message,289                [text_input, file_uploads_log],290                [stored_messages, text_input],291            ).then(self.interact_with_agent, [stored_messages, chatbot], [chatbot])292 293        demo.launch(debug=True, share=True, **kwargs)294 295 296__all__ = ["stream_to_gradio", "GradioUI"]