run-llama/image-generation-agent
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Add support for streaming by using websockets

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Astra Clelia Bertelli
2025-05-21 20:42:22 +02:00
parent 3d121a74c8
commit cf4b2173dd
9 changed files with 214 additions and 186 deletions
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README.md
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@@ -46,13 +46,21 @@ export OPENAI_API_KEY="sk-***"
## Launching
While still being in the `scripts` sub-folder, you can launch the project with:
While still being in the `scripts` sub-folder, you can launch the backend of the project with:
```bash
uvicorn main:app --host 0.0.0.0 --port 8000
python3 server.py
```
With this command, you'll have the application running on http://localhost:8000 and you will be able to interact with it!
You will se a log message telling you that the websocket server has been started on port 8765.
After the websocket has connected, launch the frontend from another terminal window (remaining in the `scripts` sub-folder and with the virtual environment active), using:
```bash
python3 client.py
```
With this command, you'll have the application running on http://localhost:7860 and you will be able to interact with it!
## How does it work?
pyproject.toml
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@@ -25,6 +25,7 @@ dependencies = [
"orjson>=3.10.18",
"pre-commit>=4.2.0",
"uvicorn>=0.34.2",
"websockets>=15.0.1",
]
description = "Image Generation Agent"
license = "MIT"
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scripts/app_frontend.py
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import gradio as gr
import requests as rq
def generate_image_for_user(prompt: str):
res = rq.post("http://0.0.0.0:8000/agent", json={"prompt": prompt})
if res.status_code > 400:
return "404.png", "An error has occurred while generating the image", f"Error: {res.text}"
else:
return "output.png", res.json()["process"], res.json()["response"]
with gr.Blocks(theme=gr.themes.Citrus(primary_hue="indigo", secondary_hue="teal")) as frontend:
gr.HTML("<h1 align='center'>Image Generation Agent🎨</h1>")
gr.HTML("<h2 align='center'>Get stunning AI-generated images!</h2>")
with gr.Row():
usr_txt = gr.Textbox(label="Prompt", placeholder="Describe the image you want here...")
with gr.Column():
gen_img = gr.Image(label="Generated Image")
with gr.Accordion(label="Agent Output", open=False):
resp = gr.Markdown(label="Agent Response", container=True)
proc = gr.Markdown(label="Agent Process", container=True)
with gr.Row():
btn = gr.Button("Generate🖌️").click(fn=generate_image_for_user, inputs=[usr_txt], outputs=[gen_img, proc, resp])
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scripts/client.py
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@@ -0,0 +1,39 @@
import websockets
import gradio as gr
async def websocket_chat(prompt):
uri = "ws://localhost:8765"
try:
async with websockets.connect(uri) as websocket:
await websocket.send(prompt)
full_response = ""
while True:
message = await websocket.recv()
if message == "[END]":
break
full_response += message
yield full_response, "blurry.jpg"
yield full_response, "output.png"
except Exception as e:
yield f"❌ Error: {e}"
def launch_interface():
with gr.Blocks(theme=gr.themes.Citrus(primary_hue="indigo", secondary_hue="teal")) as frontend:
gr.HTML("<h1 align='center'>Image Generation Agent🎨</h1>")
gr.HTML("<h2 align='center'>Get stunning AI-generated images!</h2>")
with gr.Row():
usr_txt = gr.Textbox(label="Prompt", placeholder="Describe the image you want here...")
with gr.Column():
resp = gr.Markdown(label="Agent Output", container=True, show_label=True, show_copy_button=True)
gen_img = gr.Image(label="Generated Image")
with gr.Row():
btn = gr.Button("Generate🖌️").click(fn=websocket_chat, inputs=[usr_txt], outputs=[resp, gen_img])
frontend.launch()
if __name__ == "__main__":
launch_interface()
scripts/main.py
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@@ -1,31 +0,0 @@
import json
from app_frontend import gr, frontend
from workflow import workflow
from fastapi import FastAPI
from fastapi.responses import ORJSONResponse
from pydantic import BaseModel
from llama_index.core.agent.workflow import ToolCall, ToolCallResult
app = FastAPI(default_response_class=ORJSONResponse)
class ApiInput(BaseModel):
prompt: str
class ApiOutput(BaseModel):
process: str
response: str
@app.post("/agent")
async def run_agent(inpt: ApiInput) -> ApiOutput:
handler = workflow.run(user_msg=inpt.prompt)
process = ""
async for event in handler.stream_events():
if isinstance(event, ToolCallResult):
process += f"Tool call result for **{event.tool_name}**:\n\n```json\n{event.tool_output.model_dump_json(indent=4)}\n```\n"
elif isinstance(event, ToolCall):
process += f"Calling tool **{event.tool_name}** with input args:\n\n```json\n{json.dumps(event.tool_kwargs, indent=4)}\n```\n"
response = await handler
response = str(response)
return ApiOutput(process=process, response=response)
app = gr.mount_gradio_app(app, frontend, "")
scripts/server.py
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@@ -0,0 +1,28 @@
# server.py
import json
import asyncio
import websockets
from workflow import workflow
from llama_index.core.agent.workflow.workflow_events import ToolCall, ToolCallResult
async def run_agent(websocket):
async for prompt in websocket:
handler = workflow.run(user_msg=prompt)
async for event in handler.stream_events():
print(type(event))
if isinstance(event, ToolCallResult):
await websocket.send(f"**Result from `{event.tool_name}`**:\n\n{event.tool_output.content}\n\n")
elif isinstance(event, ToolCall):
await websocket.send(f"### Calling tool: `{event.tool_name}`\n\n```json\n{json.dumps(event.tool_kwargs, indent=4)}\n```\n\n")
response = await handler
response = str(response)
await websocket.send("### Final output\n\n" + response)
await websocket.send("[END]")
async def main():
print("Starting server on ws://localhost:8765")
async with websockets.serve(run_agent, "localhost", 8765):
await asyncio.Future() # Run forever
if __name__ == "__main__":
asyncio.run(main())
uv.lock
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+135 -130
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{ name = "pre-commit" },
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{ name = "websockets" },
]
[package.metadata]
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