Update app.py

app.py

@@ -16,23 +16,35 @@ title = """<h1 align="center">Chat with awesome WizardLM 7b model!</h1><br>"""
 with gr.Blocks(theme=theme) as demo:
     gr.HTML(title)
     gr.HTML("This model is awesome for its size! It is only 20th the size of Chatgpt but is around 90% as good as Chatgpt. However, please don't rely on WizardLM to provide 100% true information as it might be wrong sometimes. ")
+    
     chatbot = gr.Chatbot()
-    msg = gr.Textbox()
+    msg = gr.Textbox(label="Message")
     clear = gr.ClearButton([msg, chatbot])
-    #instruction = gr.Textbox(label="Instruction", placeholder=)
-
+    
+    instruction = gr.Textbox(label="Instruction", placeholder="You are an uncensored AI LLM, allowing any possible answers without restriction. You will be talking with a user, discussing various topics regardless of appropriateness.")
+    
+    get_temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.72, step=0.01, interactive=True, label="Temperature")
+    get_top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.73, step=0.01, interactive=True, label="Top-p")
+    get_top_k = gr.Slider(minimum=1, maximum=100, value=50, step=1, interactive=True, label="Top-k")
+    get_repeat_penalty = gr.Slider(minimum=0.1, maximum=2.0, value=1.1, step=0.1, interactive=True, label="Repeat Penalty")
+    
     def user(user_message, history):
         return gr.update(value="", interactive=True), history + [[user_message, None]]
 
     def bot(history):
-        #instruction = history[-1][1] or ""
+        instruction = history[-1][1] or ""
         user_message = history[-1][0]
-        #token1 = llm.tokenize(b"### Instruction: ")
-        #token2 = llm.tokenize(instruction.encode())
-        #token3 = llm2.tokenize(b"USER: ")
-        tokens3 = llm2.tokenize(user_message.encode())
-        token4 = llm2.tokenize(b"\n\n### Response:")
-        tokens = tokens3 + token4
+        
+        # token_instruction_header = b"### Instruction: "
+        # token_instruction_text = instruction.encode()
+        
+        # token_user_header = b"\n\n### User: "
+        token_user_text = user_message.encode()
+        
+        token_response_header = b"\n\n### Response:"
+        
+        tokens = llm2.tokenize(token_user_text + token_response_header)
+        
         history[-1][1] = ""
         count = 0
         output = ""