Create app.py

app.py

@@ -0,0 +1,32 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from flask import Flask, request, jsonify
+from threading import Thread
+
+app = Flask(__name__)
+
+tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
+model = AutoModelForCausalLM.from_pretrained(
+    "HuggingFaceH4/zephyr-7b-beta",
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto"
+)
+
+@app.route("/api/chat", methods=["POST"])
+def chat():
+    data = request.get_json()
+    question = data.get("question", "")
+
+    prompt = f"Eres BITER, un mentor experto en negocios. Siempre respondes en español con consejos breves y útiles.\nUsuario: {question}\nBITER:"
+    
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=200)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    respuesta_final = response.split("BITER:")[-1].strip()
+
+    return jsonify({"choices": [{"message": {"content": respuesta_final}}]})
+
+def run():
+    app.run(host='0.0.0.0', port=7860)
+
+Thread(target=run).start()