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from flask import Flask, request, jsonify
from transformers import AutoTokenizer, AutoModelForCausalLM
from flask_cors import CORS
import torch
app = Flask(__name__)
CORS(app)
print("🚀 Loading Phi model (microsoft/phi-2)...")
model_name = "microsoft/phi-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
low_cpu_mem_usage=True
)
model.to("cuda" if torch.cuda.is_available() else "cpu")
print("✅ Phi model loaded!")
@app.route("/api/ask", methods=["POST"])
def ask():
data = request.get_json(force=True)
prompt = data.get("prompt", "")
chat_prompt = f"### Instruction:\nYou are Acla, a helpful AI powered by phi-3 mini that can reason about math, code, and logic.\n\n### Input:\n{prompt}\n\n### Response:"
inputs = tokenizer(chat_prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=250,
temperature=0.7,
top_p=0.9,
do_sample=True
)
text = tokenizer.decode(outputs[0], skip_special_tokens=True)
response = text.split("### Response:")[-1].strip()
return jsonify({"reply": response})
@app.route("/")
def home():
return "🧠 Phi-2 chatbot is running! POST JSON to /api/ask with {'prompt': 'your question'}."
if __name__ == "__main__":
app.run(host="0.0.0.0", port=7860)
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