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File size: 1,808 Bytes
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import os
from flask import Flask, render_template, request, jsonify
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
app = Flask(__name__)
# Define cache directory
cache_dir = "/app/cache"
os.environ["HF_HOME"] = cache_dir
# Load Myanmarsar-GPT (1.42B params) from Hugging Face
MODEL_NAME = "simbolo-ai/Myanmarsar-GPT"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, cache_dir=cache_dir)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, cache_dir=cache_dir)
# Function to generate chatbot responses
def generate_response(prompt):
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
output = model.generate(**inputs, max_length=200)
return tokenizer.decode(output[0], skip_special_tokens=True)
# Serve the HTML page
@app.route("/")
def home():
return render_template("index.html")
# API route for chatbot responses
@app.route("/chat", methods=["POST"])
def chat():
try:
if not request.is_json:
print("Error: Request is not JSON")
return jsonify({"error": "Request must be JSON"}), 415
data = request.get_json()
user_message = data.get("message", "")
if not user_message:
print("Error: No message received")
return jsonify({"error": "No message provided"}), 400
print(f"Received message: {user_message}")
bot_reply = generate_response(user_message)
print(f"AI response: {bot_reply}")
return jsonify({"reply": bot_reply})
except Exception as e:
print(f"Error processing request: {e}")
return jsonify({"error": str(e)}), 500
if __name__ == "__main__":
port = int(os.environ.get("PORT", 7860)) # Default to 7860, but use any assigned port
app.run(host="0.0.0.0", port=port) |