Update app.py

app.py

@@ -1,49 +1,44 @@
-# Import render_template to serve HTML files
-from flask import Flask, request, render_template
-from flask_cors import CORS
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-import os
-
-# --- 1. SETUP ---
-app = Flask(__name__)
-CORS(app)
-
-MODEL_NAME = "facebook/blenderbot-400M-distill"
-model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-print(f"Successfully loaded model: {MODEL_NAME}")
-
-# --- 2. API ENDPOINTS ---
-
-# MODIFIED: This now serves your HTML front-end
-@app.route('/')
-def home():
-    """Serves the main HTML page for the chatbot."""
-    return render_template('index.html')
-
-@app.route('/chatbot', methods=['POST'])
-def chatbot_endpoint():
-    """The main endpoint to handle chatbot conversations."""
-    if not request.is_json:
-        return {"error": "Request must be a JSON"}, 400
-
-    data = request.get_json()
-    user_input = data.get("prompt")
-    
-    if not user_input:
-        return {"error": "Missing 'prompt' in request body"}, 400
-
-    try:
-        inputs = tokenizer(user_input, return_tensors="pt")
-        outputs = model.generate(**inputs, max_length=60)
-        response_text = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
-        return {"response": response_text}
-    except Exception as e:
-        print(f"Error during model inference: {e}")
-        return {"error": "Failed to generate a response"}, 500
-
-# --- 3. RUN THE APP ---
-if __name__ == '__main__':
-    # Check if the app is running in debug mode
-    is_debug = os.environ.get('FLASK_DEBUG', 'False').lower() == 'true'
-    app.run(host='0.0.0.0', port=5000, debug=is_debug)
+from flask import Flask, request, render_template
+from flask_cors import CORS
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+# --- 1. SETUP ---
+app = Flask(__name__)
+CORS(app)
+
+MODEL_NAME = "facebook/blenderbot-400M-distill"
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+print(f"Successfully loaded model: {MODEL_NAME}")
+
+# --- 2. API ENDPOINTS ---
+@app.route('/')
+def home():
+    """Serves the main HTML page for the chatbot."""
+    return render_template('index.html')
+
+@app.route('/chatbot', methods=['POST'])
+def chatbot_endpoint():
+    """The main endpoint to handle chatbot conversations."""
+    if not request.is_json:
+        return {"error": "Request must be a JSON"}, 400
+
+    data = request.get_json()
+    user_input = data.get("prompt")
+    
+    if not user_input:
+        return {"error": "Missing 'prompt' in request body"}, 400
+
+    try:
+        inputs = tokenizer(user_input, return_tensors="pt")
+        outputs = model.generate(**inputs, max_length=60)
+        response_text = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
+        return {"response": response_text}
+    except Exception as e:
+        print(f"Error during model inference: {e}")
+        return {"error": "Failed to generate a response"}, 500
+
+# --- 3. RUN THE APP ---
+if __name__ == '__main__':
+    # Use port 7860, the standard for Hugging Face Spaces web apps
+    app.run(host='0.0.0.0', port=7860)