Update app.py

app.py

@@ -14,13 +14,17 @@ huggingface_token = os.getenv('LLAMA_ACCES_TOKEN')
 huggingface_token = os.getenv('LLAMA_ACCES_TOKEN')
 
 # Use the token with from_pretrained
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf", token=huggingface_token)
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", token=huggingface_token)
+#tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf", token=huggingface_token)
+#model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", token=huggingface_token)
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
 
 # Assuming BERTopic and other necessary components are initialized here
 # Initialize your BERTopic model
-sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
-topic_model = BERTopic(embedding_model=sentence_model)
+#sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
+#topic_model = BERTopic(embedding_model=sentence_model)
 
 def print_like_dislike(x: gr.LikeData):
     print(x.index, x.value, x.liked)
@@ -50,13 +54,13 @@ def generate_response(selected_question):
 
     
     
-    try:
-        topics, _ = topic_model.transform([response])
-        topic_names = [", ".join([word for word, _ in topic_model.get_topic(topic)[:5]]) for topic in topics if topic != -1]
-        topics_str = "; ".join(topic_names[:10])
-    except Exception as e:
-        topics_str = "Topic analysis not available"
-        print(f"Error during topic analysis: {e}")
+    #try:
+        #topics, _ = topic_model.transform([response])
+        #topic_names = [", ".join([word for word, _ in topic_model.get_topic(topic)[:5]]) for topic in topics if topic != -1]
+        #topics_str = "; ".join(topic_names[:10])
+    #except Exception as e:
+    topics_str = "Topic analysis not available"
+        #print(f"Error during topic analysis: {e}")
     
     # Adjusted to return a list of tuples as expected by the Chatbot component
     new_response = (prompt, response + "\n\nTopics: " + topics_str)
@@ -87,5 +91,7 @@ with gr.Blocks() as demo:
     ]
     gr.Examples(examples, inputs=[txt], outputs=[chatbot], label="Select Question")
 
+    chatbot.like(print_like_dislike, None, None)
+
 if __name__ == "__main__":
     demo.launch()