second commit
This commit is contained in:
Carlos Tello
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from fastapi import FastAPI, File, UploadFile, Form, HTTPException
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from fastapi.responses import StreamingResponse, JSONResponse
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from fastapi.staticfiles import StaticFiles
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from fastapi.cors import CORSMiddleware
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import aiofiles
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import base64
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import requests
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import json
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import os
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from pathlib import Path
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from dotenv import load_dotenv
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import asyncio
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import io
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from PIL import Image
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import uuid
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load_dotenv()
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app = FastAPI(title="Code Agent Chat")
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# CORS para que el frontend acceda
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# Config
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LLM_SERVER = os.getenv("LLM_SERVER_URL", "http://localhost:8000")
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UPLOAD_DIR = Path("./uploads")
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WORKSPACE_DIR = Path("./workspace")
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UPLOAD_DIR.mkdir(exist_ok=True)
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WORKSPACE_DIR.mkdir(exist_ok=True)
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# Importar agent
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from agent import CodeAgent, get_agent_tools
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agent = CodeAgent(work_dir=str(WORKSPACE_DIR))
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# ============================================================================
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# ENDPOINTS
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# ============================================================================
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@app.get("/api/health")
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async def health():
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"""Verificar que el servidor está vivo"""
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return {"status": "ok", "workspace": str(WORKSPACE_DIR.absolute())}
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@app.get("/api/files")
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async def list_files():
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"""Listar archivos en workspace"""
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files = []
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for f in WORKSPACE_DIR.rglob("*"):
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if f.is_file():
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files.append({
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"name": str(f.relative_to(WORKSPACE_DIR)),
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"size": f.stat().st_size,
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"type": f.suffix
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})
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return {"files": files}
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@app.get("/api/file/{file_path:path}")
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async def read_file(file_path: str):
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"""Leer contenido de archivo"""
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file = WORKSPACE_DIR / file_path
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if not file.exists():
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raise HTTPException(status_code=404, detail="Archivo no encontrado")
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try:
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with open(file, 'r') as f:
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content = f.read()
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return {"content": content, "name": file_path}
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/api/chat")
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async def chat(
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message: str = Form(...),
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image: UploadFile = File(None),
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model: str = Form("phi")
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):
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"""
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Endpoint principal de chat
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- message: tu prompt
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- image: imagen adjunta (opcional)
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- model: "phi" (rápido) o "deepseek-coder:33b" (potente)
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"""
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# Procesar imagen si hay
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image_data = None
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image_base64 = None
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if image:
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try:
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contents = await image.read()
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image_base64 = base64.b64encode(contents).decode()
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# Guardar imagen
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image_path = UPLOAD_DIR / f"{uuid.uuid4()}.png"
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with open(image_path, 'wb') as f:
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f.write(contents)
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image_data = f"[Imagen adjunta: {image.filename}]"
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print(f"📸 Imagen procesada: {image.filename}")
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except Exception as e:
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raise HTTPException(status_code=400, detail=f"Error procesando imagen: {e}")
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# Construir prompt para el LLM
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full_prompt = f"""
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{message}
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{f"Contexto de imagen: {image_data}" if image_data else ""}
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Tienes acceso a estas herramientas:
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- read_file(file_path): Leer archivo
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- write_file(file_path, content): Escribir archivo
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- execute_python(code): Ejecutar Python
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- execute_node(code): Ejecutar Node.js
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- test_html(file_name): Servir y testear HTML
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- open_browser(url): Abrir navegador
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- list_files(): Listar archivos
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Responde en JSON con tu pensamiento y la acción a ejecutar.
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Ejemplo:
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{{"thought": "Voy a leer el archivo", "action": "read_file", "args": {{"file_path": "app.js"}}}}
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"""
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# Llamar LLM
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try:
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response = requests.post(
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f"{LLM_SERVER}/code",
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json={
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"message": full_prompt,
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"model": model
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},
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timeout=300,
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stream=False
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)
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if response.status_code != 200:
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raise HTTPException(
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status_code=response.status_code,
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detail=response.text
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)
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llm_response = response.json()["response"]
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# Parsear JSON y ejecutar acción
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result = process_agent_response(llm_response)
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return {
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"message": message,
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"image": image.filename if image else None,
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"model": model,
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"llm_response": llm_response,
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"action_result": result,
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"image_base64": image_base64
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}
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except requests.exceptions.ConnectionError:
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raise HTTPException(
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status_code=503,
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detail=f"No se puede conectar a LLM Server: {LLM_SERVER}"
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)
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/api/chat-stream")
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async def chat_stream(
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message: str = Form(...),
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image: UploadFile = File(None),
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model: str = Form("phi")
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):
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"""Versión streaming (respuestas en tiempo real)"""
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image_data = None
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if image:
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contents = await image.read()
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image_path = UPLOAD_DIR / f"{uuid.uuid4()}.png"
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with open(image_path, 'wb') as f:
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f.write(contents)
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image_data = f"[Imagen: {image.filename}]"
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full_prompt = f"{message}\n{image_data if image_data else ''}"
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async def generate():
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try:
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# Streaming desde LLM
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response = requests.post(
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f"{LLM_SERVER}/code",
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json={
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"message": full_prompt,
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"model": model
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},
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timeout=300,
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stream=True
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)
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for line in response.iter_lines():
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if line:
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yield line + b'\n'
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except Exception as e:
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yield json.dumps({"error": str(e)}).encode() + b'\n'
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return StreamingResponse(generate(), media_type="text/event-stream")
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def process_agent_response(response: str) -> dict:
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"""Parsear respuesta del LLM y ejecutar acción"""
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tools = get_agent_tools(agent)
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try:
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# Extraer JSON
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json_start = response.find('{')
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json_end = response.rfind('}') + 1
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json_str = response[json_start:json_end]
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action = json.loads(json_str)
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except:
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return {"error": "No se pudo parsear la respuesta", "raw": response[:200]}
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action_name = action.get("action") or action.get("tool")
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args = action.get("args", {})
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if action_name not in tools:
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return {"error": f"Acción desconocida: {action_name}"}
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try:
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result = tools[action_name](**args)
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return {"success": True, "action": action_name, "result": result}
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except Exception as e:
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return {"error": f"Error ejecutando {action_name}: {str(e)}"}
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# ============================================================================
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# SERVIR STATIC FILES (UI Frontend)
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# ============================================================================
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app.mount("/", StaticFiles(directory="static", html=True), name="static")
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if __name__ == "__main__":
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import uvicorn
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print(f"🚀 Backend en http://localhost:5000")
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print(f"📁 Workspace: {WORKSPACE_DIR.absolute()}")
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uvicorn.run(app, host="0.0.0.0", port=5000)
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