Update app.py

app.py

@@ -1,15 +1,13 @@
 from flask import Flask, request, jsonify
-from transformers import pipeline
-from flask_cors import CORS  # Import CORS
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from flask_cors import CORS
 
 app = Flask(__name__)
-CORS(app)  # Enable CORS for all routes
+CORS(app)  # Enable CORS for all routes (you can restrict this if needed)
 
-# Alternatively, you can enable CORS for specific origins only (e.g., your GitHub Pages URL)
-# CORS(app, origins=["https://ahil-mohammad.github.io"])
-
-# Load your Hugging Face model (e.g., a conversational model)
-model = pipeline("conversational", model="microsoft/DialoGPT-medium")
+# Load BloomZ model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-1b1")
+model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-1b1")
 
 @app.route('/send_message', methods=['POST'])
 def send_message():
@@ -18,9 +16,14 @@ def send_message():
         data = request.get_json()
         user_message = data['message']
         
-        # Generate the model's response
-        response = model(user_message)
-        bot_reply = response[0]['generated_text']  # Assuming the model returns a list of conversations
+        # Tokenize the input message
+        inputs = tokenizer(user_message, return_tensors="pt")
+        
+        # Generate response from the model
+        outputs = model.generate(inputs['input_ids'], max_length=50, num_return_sequences=1)
+        
+        # Decode the response
+        bot_reply = tokenizer.decode(outputs[0], skip_special_tokens=True)
         
         # Return the response as a JSON
         return jsonify({'response': bot_reply})