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Hhblvjgvg commited on 18 days ago

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9c15b84

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1 Parent(s): 3fd0ab6

Create app.py

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app.py +166 -0

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+from fastapi import FastAPI, HTTPException, Request
+from pydantic import BaseModel, Field
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+from typing import Optional, List
+import asyncio
+from fastapi.responses import StreamingResponse, HTMLResponse
+import uvicorn
+import psutil
+app = FastAPI()
+dispositivo = torch.device("cpu")
+CPU_LIMIT = 30.0
+RAM_LIMIT = 30.0
+html_code = """
+<!DOCTYPE html>
+<html>
+<head>
+    <title>Chatbot</title>
+    <style>
+        body { font-family: Arial, sans-serif; margin: 50px; }
+        #chat { border: 1px solid #ccc; padding: 10px; height: 300px; overflow-y: scroll; }
+        #input { width: 80%; padding: 10px; }
+        #send { padding: 10px; }
+    </style>
+</head>
+<body>
+    <h1>Chatbot</h1>
+    <div id="chat"></div>
+    <input type="text" id="input" placeholder="Escribe tu mensaje...">
+    <button id="send">Enviar</button>
+    <script>
+        const sendButton = document.getElementById('send');
+        const inputBox = document.getElementById('input');
+        const chatBox = document.getElementById('chat');
+        let history = [];
+        sendButton.addEventListener('click', () => {
+            const message = inputBox.value;
+            if (message.trim() === '') return;
+            history.push(`Tú: ${message}`);
+            chatBox.innerHTML += `<div><strong>Tú:</strong> ${message}</div>`;
+            inputBox.value = '';
+            fetch('/generar', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify({
+                    texto: message,
+                    history: history
+                })
+            })
+            .then(response => {
+                if (!response.body) {
+                    throw new Error('No soporta streaming');
+                }
+                const reader = response.body.getReader();
+                const decoder = new TextDecoder();
+                let botMessage = '';
+                function read() {
+                    reader.read().then(({ done, value }) => {
+                        if (done) {
+                            history.push(`Bot: ${botMessage}`);
+                            chatBox.innerHTML += `<div><strong>Bot:</strong> ${botMessage}</div>`;
+                            chatBox.scrollTop = chatBox.scrollHeight;
+                            return;
+                        }
+                        const chunk = decoder.decode(value, { stream: true });
+                        botMessage += chunk;
+                        chatBox.innerHTML += `<div><strong>Bot:</strong> ${botMessage}</div>`;
+                        chatBox.scrollTop = chatBox.scrollHeight;
+                        read();
+                    }).catch(error => {
+                        chatBox.innerHTML += `<div><strong>Bot:</strong> Error: ${error}</div>`;
+                        chatBox.scrollTop = chatBox.scrollHeight;
+                    });
+                }
+                read();
+            })
+            .catch(error => {
+                chatBox.innerHTML += `<div><strong>Bot:</strong> Error: ${error}</div>`;
+                chatBox.scrollTop = chatBox.scrollHeight;
+            });
+        });
+    </script>
+</body>
+</html>
+"""
+class Entrada(BaseModel):
+    texto: str = Field(..., example="Hola, ¿cómo estás?")
+    history: Optional[List[str]] = Field(default_factory=list)
+    top_p: Optional[float] = Field(0.95, ge=0.0, le=1.0)
+    top_k: Optional[int] = Field(50, ge=0)
+    temperature: Optional[float] = Field(1.0, gt=0.0)
+    max_length: Optional[int] = Field(100, ge=10, le=1000)
+    chunk_size: Optional[int] = Field(10, ge=1)
+@app.middleware("http")
+async def limitar_recursos(request: Request, call_next):
+    cpu = psutil.cpu_percent(interval=0.1)
+    ram = psutil.virtual_memory().percent
+    if cpu > CPU_LIMIT or ram > RAM_LIMIT:
+        raise HTTPException(status_code=503, detail="Servidor sobrecargado. Intenta de nuevo más tarde.")
+    response = await call_next(request)
+    return response
+@app.on_event("startup")
+def cargar_modelo():
+    global tokenizador, modelo, eos_token, pad_token
+    tokenizador = AutoTokenizer.from_pretrained("Yhhxhfh/dgdggd")
+    modelo = AutoModelForCausalLM.from_pretrained(
+        "Yhhxhfh/dgdggd",
+        torch_dtype=torch.float32,
+        device_map="cpu"
+    )
+    modelo.eval()
+    eos_token = tokenizador.eos_token
+    pad_token = tokenizador.pad_token
+async def generar_stream(prompt, top_p, top_k, temperature, max_length, chunk_size):
+    input_ids = tokenizador.encode(prompt, return_tensors="pt").to(dispositivo)
+    outputs = modelo.generate(
+        input_ids,
+        max_length=input_ids.shape[1] + max_length,
+        do_sample=True,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature,
+        no_repeat_ngram_size=2,
+        eos_token_id=tokenizador.eos_token_id if tokenizador.eos_token_id is not None else -1
+    )
+    generated_ids = outputs[0][input_ids.shape[1]:]
+    generated_text = tokenizador.decode(generated_ids, skip_special_tokens=True)
+    for i in range(0, len(generated_text), chunk_size):
+        yield generated_text[i:i+chunk_size]
+        await asyncio.sleep(0)
+@app.post("/generar")
+async def generar_texto(entrada: Entrada):
+    try:
+        prompt = "\n".join(entrada.history + [f"Tú: {entrada.texto}", "Bot:"])
+        async def stream():
+            async for chunk in generar_stream(
+                prompt,
+                entrada.top_p,
+                entrada.top_k,
+                entrada.temperature,
+                entrada.max_length,
+                entrada.chunk_size
+            ):
+                yield chunk
+        return StreamingResponse(stream(), media_type="text/plain")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+@app.get("/", response_class=HTMLResponse)
+async def get_home():
+    return html_code
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)