gradio_app.py

import gradio as gr
from src.agents.mask_generation_agent import mask_generation_agent, ImageEditDeps
import os
from src.hopter.client import Hopter, Environment
from src.services.generate_mask import GenerateMaskService
from dotenv import load_dotenv
from src.utils import image_path_to_uri
from pydantic_ai.messages import (
    ToolCallPart,
    ToolReturnPart
)
from src.agents.mask_generation_agent import EditImageResult
from pydantic_ai.agent import Agent 
from pydantic_ai.models.openai import OpenAIModel
model = OpenAIModel(
    "gpt-4o",
    api_key=os.environ.get("OPENAI_API_KEY"),
)

simple_agent = Agent(
    model,
    system_prompt="You are a helpful assistant that can answer questions and help with tasks.",
    deps_type=ImageEditDeps
)

load_dotenv()

def build_user_message(chat_input):
    text = chat_input["text"]
    images = chat_input["files"]
    messages = [
        {
            "role": "user",
            "content": text
        }
    ]
    if images:
        messages.extend([
            {
                "role": "user",
                "content": {"path": image}
            }
            for image in images
        ])
    return messages

def build_messages_for_agent(chat_input, past_messages):
    # filter out image messages from past messages to save on tokens
    messages = [msg for msg in past_messages 
                if not (isinstance(msg, dict) 
                        and msg.get("type") == "image_url")]
    
    # add the user's text message
    if chat_input["text"]:
        messages.append({
            "type": "text",
            "text": chat_input["text"]
        })

    # add the user's image message
    files = chat_input.get("files", [])
    image_url = image_path_to_uri(files[0]) if files else None
    if image_url:
        messages.append({
            "type": "image_url",
            "image_url": {"url": image_url}
        })

    return messages
    

async def stream_from_agent(chat_input, chatbot, past_messages, current_image):
    # Prepare messages for the UI
    chatbot.extend(build_user_message(chat_input))
    yield {"text": "", "files": []}, chatbot, gr.skip, gr.skip()

    # Prepare messages for the agent
    text = chat_input["text"]
    files = chat_input.get("files", [])
    image_url = image_path_to_uri(files[0]) if files else None
    messages = [
        {
            "type": "text",
            "text": text
        },
    ]
    if image_url:
        messages.append(
            {"type": "image_url", "image_url": {"url": image_url}}
        )
        current_image = image_url

    # Dependencies
    hopter = Hopter(os.environ.get("HOPTER_API_KEY"), environment=Environment.STAGING)
    mask_service = GenerateMaskService(hopter=hopter)
    deps = ImageEditDeps(
        edit_instruction=text,
        image_url=current_image,
        hopter_client=hopter,
        mask_service=mask_service
    )
    # Run the agent
    async with mask_generation_agent.run_stream(
        messages,
        deps=deps,
    ) as result:
        for message in result.new_messages():
            for call in message.parts:
                if isinstance(call, ToolCallPart):
                    call_args = (
                        call.args.args_json
                        if hasattr(call.args, 'args_json')
                        else call.args
                    )
                    metadata = {
                        'title': f'🛠️ Using {call.tool_name}',
                    }
                    # set the tool call id so that when the tool returns
                    # we can find this message and update with the result
                    if call.tool_call_id is not None:
                        metadata['id'] = call.tool_call_id

                    # Create a tool call message to show on the UI
                    gr_message = {
                        'role': 'assistant',
                        'content': 'Parameters: ' + call_args,
                        'metadata': metadata,
                    }
                    chatbot.append(gr_message)
                if isinstance(call, ToolReturnPart):
                    for gr_message in chatbot:
                        # Skip messages without metadata
                        if not gr_message.get('metadata'):
                            continue

                        if gr_message['metadata'].get('id', '') == call.tool_call_id:
                            if isinstance(call.content, EditImageResult):
                                chatbot.append({
                                    "role": "assistant",
                                    "content": gr.Image(call.content.edited_image_url),
                                    "files": [call.content.edited_image_url]
                                })
                            else:
                                gr_message['content'] += (
                                    f'\nOutput: {call.content}'
                                )
                yield gr.skip(), chatbot, gr.skip(), gr.skip()

        chatbot.append({'role': 'assistant', 'content': ''})
        async for message in result.stream_text():
            chatbot[-1]['content'] = message
            yield gr.skip(), chatbot, gr.skip(), gr.skip()
        past_messages = result.all_messages()

        yield gr.Textbox(interactive=True), gr.skip(), past_messages, current_image

with gr.Blocks() as demo:
    gr.HTML(
        """
<div style="display: flex; justify-content: center; align-items: center; gap: 2rem; padding: 1rem; width: 100%">
    <img src="https://ai.pydantic.dev/img/logo-white.svg" style="max-width: 200px; height: auto">
    <div>
        <h1 style="margin: 0 0 1rem 0">Image Editing Assistant</h1>
        <h3 style="margin: 0 0 0.5rem 0">
            This assistant edits images according to your instructions.
        </h3>
    </div>
</div>
"""
    )

    current_image = gr.State(None)
    past_messages = gr.State([])
    chatbot = gr.Chatbot(
        label='Image Editing Assistant',
        type='messages',
        avatar_images=(None, 'https://ai.pydantic.dev/img/logo-white.svg'),
    )
    with gr.Row():
        chat_input = gr.MultimodalTextbox(
            interactive=True,
            file_count="single",
            show_label=False,
            placeholder='How would you like to edit this image?',
            sources=["upload"]
        )
    generation = chat_input.submit(
        stream_from_agent,
        inputs=[chat_input, chatbot, past_messages, current_image],
        outputs=[chat_input, chatbot, past_messages, current_image],
    )

if __name__ == '__main__':
    demo.launch()