Update app.py

app.py

@@ -1,64 +1,31 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+import whisper
+import requests
+
+# Cargar el modelo Whisper Medium
+modelo = whisper.load_model("medium")
+
+# URL del servidor LLaMA en Google Colab (se actualizará más adelante)
+LLAMA_SERVER_URL = "https://your-colab-server-url/run_llama"
+
+# Función para transcribir el audio y enviarlo a LLaMA 2
+def transcribir_y_enviar(audio):
+    transcripcion = modelo.transcribe(audio)["text"]
+    
+    # Enviar texto transcrito al servidor de LLaMA 2 en Google Colab
+    respuesta = requests.post(LLAMA_SERVER_URL, json={"texto": transcripcion})
+    
+    return respuesta.json().get("respuesta", "Error en el servidor de LLaMA")
+
+# Interfaz en Gradio
+interfaz = gr.Interface(
+    fn=transcribir_y_enviar,
+    inputs=gr.Audio(source="upload", type="filepath"),
+    outputs="text",
+    title="Chatbot de Miopía Magna",
+    description="Sube un audio y el chatbot responderá."
 )
 
-
+# Ejecutar el servidor
 if __name__ == "__main__":
-    demo.launch()
+    interfaz.launch()