Update app.py

app.py

@@ -31,26 +31,26 @@ def classify_psychiatric_text(txt):
     probabilities = torch.softmax(logits, dim=1).squeeze().tolist()
     return dict(zip(psychiatric_labels, probabilities))
 
-# Load BlenderBot for Lifestyle and Nutrition Chatbot
-blender_model_name = "facebook/blenderbot-3B"  # Pre-trained BlenderBot 3B model
-blender_tokenizer = BlenderbotTokenizer.from_pretrained(blender_model_name)
-blender_model = BlenderbotForConditionalGeneration.from_pretrained(blender_model_name)
+# # Load BlenderBot for Lifestyle and Nutrition Chatbot
+# blender_model_name = "facebook/blenderbot-3B"  # Pre-trained BlenderBot 3B model
+# blender_tokenizer = BlenderbotTokenizer.from_pretrained(blender_model_name)
+# blender_model = BlenderbotForConditionalGeneration.from_pretrained(blender_model_name)
 
 # Chat function for Lifestyle and Nutrition
 chat_history = []
 
-def chatbot_response(user_input):
-    global chat_history
-    new_input_ids = blender_tokenizer.encode(user_input + blender_tokenizer.eos_token, return_tensors='pt')
-    bot_input_ids = torch.cat([chat_history, new_input_ids], dim=-1) if chat_history else new_input_ids
-    chat_history = blender_model.generate(bot_input_ids, max_length=1000, pad_token_id=blender_tokenizer.eos_token_id)
-    response = blender_tokenizer.decode(chat_history[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
-    return response
-
-def clear_chat():
-    global chat_history
-    chat_history = []
-    return []
+# def chatbot_response(user_input):
+#     global chat_history
+#     new_input_ids = blender_tokenizer.encode(user_input + blender_tokenizer.eos_token, return_tensors='pt')
+#     bot_input_ids = torch.cat([chat_history, new_input_ids], dim=-1) if chat_history else new_input_ids
+#     chat_history = blender_model.generate(bot_input_ids, max_length=1000, pad_token_id=blender_tokenizer.eos_token_id)
+#     response = blender_tokenizer.decode(chat_history[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+#     return response
+
+# def clear_chat():
+#     global chat_history
+#     chat_history = []
+#     return []
 
 # Gradio Interfaces
 medical_text = gr.Textbox(lines=2, label='Describe your symptoms in detail')