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Update app.py
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app.py
CHANGED
@@ -3,7 +3,7 @@ import requests
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import json
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import os
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# Funci贸n para generar respuestas usando
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def generate_response(user_message):
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try:
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if not user_message.strip():
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@@ -13,7 +13,7 @@ def generate_response(user_message):
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with open("prompt.txt", "r", encoding="utf-8") as f:
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system_prompt = f.read().strip()
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# Configurar la solicitud a DeepInfra
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api_key = os.environ.get("DEEPINFRA_API_KEY", "")
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if not api_key:
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return "Error: No se ha configurado la clave API. Por favor, configura la variable de entorno DEEPINFRA_API_KEY."
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@@ -23,14 +23,15 @@ def generate_response(user_message):
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"Content-Type": "application/json"
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}
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# Formato de prompt para
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_message}
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]
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data = {
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"model": "
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"messages": messages,
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"max_tokens": 500,
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"temperature": 0.7,
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@@ -49,7 +50,19 @@ def generate_response(user_message):
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result = response.json()
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return result["choices"][0]["message"]["content"]
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else:
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-
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except Exception as e:
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return f"Lo siento, ha ocurrido un error: {str(e)}"
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@@ -75,4 +88,4 @@ demo = gr.Interface(
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# Lanzar la aplicaci贸n
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if __name__ == "__main__":
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demo.queue(max_size=1).launch(share=False, debug=False)
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import json
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import os
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# Funci贸n para generar respuestas usando DeepInfra
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def generate_response(user_message):
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try:
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if not user_message.strip():
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with open("prompt.txt", "r", encoding="utf-8") as f:
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system_prompt = f.read().strip()
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# Configurar la solicitud a DeepInfra
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api_key = os.environ.get("DEEPINFRA_API_KEY", "")
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if not api_key:
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return "Error: No se ha configurado la clave API. Por favor, configura la variable de entorno DEEPINFRA_API_KEY."
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"Content-Type": "application/json"
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}
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# Formato de prompt para modelos de chat
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_message}
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]
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# Usar un modelo que s铆 est谩 disponible en DeepInfra
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data = {
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"model": "meta-llama/Llama-2-7b-chat-hf", # Modelo disponible en DeepInfra
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"messages": messages,
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"max_tokens": 500,
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"temperature": 0.7,
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result = response.json()
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return result["choices"][0]["message"]["content"]
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else:
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# Intentar con otro modelo si el primero falla
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data["model"] = "mistralai/Mistral-7B-Instruct-v0.2"
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response = requests.post(
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"https://api.deepinfra.com/v1/openai/chat/completions",
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headers=headers,
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json=data
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)
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if response.status_code == 200:
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result = response.json()
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return result["choices"][0]["message"]["content"]
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else:
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return f"Error en la API: {response.status_code} - {response.text}"
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except Exception as e:
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return f"Lo siento, ha ocurrido un error: {str(e)}"
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# Lanzar la aplicaci贸n
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if __name__ == "__main__":
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demo.queue(max_size=1).launch(share=False, debug=False)
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