Add application file

app_huggingface_spaces.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Cargar el modelo y el tokenizer
+model_name = "epfl-llm/meditron-7b"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Función para generar respuesta del modelo
+def generate_response(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(inputs.input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Configurar la interfaz de Gradio
+def chat(paciente_input):
+    prompt = f"Paciente: {paciente_input}\nDoctor:"
+    response = generate_response(prompt)
+    return response
+
+iface = gr.Interface(fn=chat, inputs="text", outputs="text", title="Consulta Médica con Meditron-7B")
+
+iface.launch()