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Yhhxhfh commited on Sep 22, 2024

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Create app.py

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app.py +198 -0

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+import os
+import logging
+import asyncio
+import uvicorn
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from fastapi import FastAPI, Query, HTTPException
+from fastapi.responses import HTMLResponse
+# Configuración de logging
+logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger(__name__)
+# Inicializar la aplicación FastAPI
+app = FastAPI()
+# Diccionario para almacenar los modelos
+data_and_models_dict = {}
+# Lista para almacenar el historial de mensajes
+message_history = []
+# Función para cargar modelos
+async def load_models():
+    gpt_models = ["gpt2-medium", "gpt2-large", "gpt2"]
+    for model_name in gpt_models:
+        try:
+            model = AutoModelForCausalLM.from_pretrained(model_name)
+            tokenizer = AutoTokenizer.from_pretrained(model_name)
+            logger.info(f"Successfully loaded {model_name} model")
+            return model, tokenizer
+        except Exception as e:
+            logger.error(f"Failed to load GPT-2 model: {e}")
+    raise HTTPException(status_code=500, detail="Failed to load any models")
+# Función para descargar modelos
+async def download_models():
+    model, tokenizer = await load_models()
+    data_and_models_dict['model'] = (model, tokenizer)
+@app.get('/')
+async def main():
+    html_code = """
+    <!DOCTYPE html>
+    <html lang="en">
+    <head>
+        <meta charset="UTF-8">
+        <meta name="viewport" content="width=device-width, initial-scale=1.0">
+        <title>ChatGPT Chatbot</title>
+        <style>
+            body, html {
+                height: 100%;
+                margin: 0;
+                padding: 0;
+                font-family: Arial, sans-serif;
+            }
+            .container {
+                height: 100%;
+                display: flex;
+                flex-direction: column;
+                justify-content: center;
+                align-items: center;
+            }
+            .chat-container {
+                border-radius: 10px;
+                overflow: hidden;
+                box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
+                width: 100%;
+                height: 100%;
+            }
+            .chat-box {
+                height: calc(100% - 60px);
+                overflow-y: auto;
+                padding: 10px;
+            }
+            .chat-input {
+                width: calc(100% - 100px);
+                padding: 10px;
+                border: none;
+                border-top: 1px solid #ccc;
+                font-size: 16px;
+            }
+            .input-container {
+                display: flex;
+                align-items: center;
+                justify-content: space-between;
+                padding: 10px;
+                background-color: #f5f5f5;
+                border-top: 1px solid #ccc;
+                width: 100%;
+            }
+            button {
+                padding: 10px;
+                border: none;
+                cursor: pointer;
+                background-color: #007bff;
+                color: #fff;
+                font-size: 16px;
+            }
+            .user-message {
+                background-color: #cce5ff;
+                border-radius: 5px;
+                align-self: flex-end;
+                max-width: 70%;
+                margin-left: auto;
+                margin-right: 10px;
+                margin-bottom: 10px;
+            }
+            .bot-message {
+                background-color: #d1ecf1;
+                border-radius: 5px;
+                align-self: flex-start;
+                max-width: 70%;
+                margin-bottom: 10px;
+            }
+        </style>
+    </head>
+    <body>
+        <div class="container">
+            <div class="chat-container">
+                <div class="chat-box" id="chat-box"></div>
+                <div class="input-container">
+                    <input type="text" class="chat-input" id="user-input" placeholder="Escribe un mensaje...">
+                    <button onclick="sendMessage()">Enviar</button>
+                </div>
+            </div>
+        </div>
+        <script>
+            const chatBox = document.getElementById('chat-box');
+            const userInput = document.getElementById('user-input');
+            function saveMessage(sender, message) {
+                const messageElement = document.createElement('div');
+                messageElement.textContent = `${sender}: ${message}`;
+                messageElement.classList.add(`${sender}-message`);
+                chatBox.appendChild(messageElement);
+                userInput.value = '';
+            }
+            async function sendMessage() {
+                const userMessage = userInput.value.trim();
+                if (!userMessage) return;
+                saveMessage('user', userMessage);
+                await fetch(`/autocomplete?q=${userMessage}`)
+                    .then(response => response.text())
+                    .then(data => {
+                        saveMessage('bot', data);
+                        chatBox.scrollTop = chatBox.scrollHeight;
+                    })
+                    .catch(error => console.error('Error:', error));
+            }
+            userInput.addEventListener("keyup", function(event) {
+                if (event.keyCode === 13) {
+                    event.preventDefault();
+                    sendMessage();
+                }
+            });
+        </script>
+    </body>
+    </html>
+    """
+    return HTMLResponse(content=html_code, status_code=200)
+# Ruta para la generación de respuestas
+@app.get('/autocomplete')
+async def autocomplete(q: str = Query(...)):
+    global data_and_models_dict, message_history
+    # Verificar si hay modelos cargados
+    if 'model' not in data_and_models_dict:
+        await download_models()
+    # Obtener el modelo
+    model, tokenizer = data_and_models_dict['model']
+    # Guardar el mensaje del usuario en el historial
+    message_history.append(q)
+    # Generar una respuesta utilizando el modelo
+    input_ids = tokenizer.encode(q, return_tensors="pt")
+    output = model.generate(input_ids, max_length=50, num_return_sequences=1)
+    response_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    # Guardar la respuesta en el historial
+    message_history.append(response_text)
+    return response_text
+# Función para ejecutar la aplicación sin reiniciarla
+def run_app():
+    asyncio.run(download_models())
+    uvicorn.run(app, host='0.0.0.0', port=7860)
+# Ejecutar la aplicación
+if __name__ == "__main__":
+    run_app()