Update app.py

app.py

@@ -6,73 +6,6 @@ import time
 from load_llms import model_choices, llm_intro, load_model
 
 
-# populate all models available from GPT4All
-# url = "https://raw.githubusercontent.com/nomic-ai/gpt4all/main/gpt4all-chat/metadata/models3.json"
-# response = urlopen(url)
-# data_json = json.loads(response.read())
-
-
-# def model_choices():
-#     model_list = [data_json[i]['filename'] for i in range(len(data_json))]
-#     return model_list
-
-    
-# get each models' description
-# model_description = {model['filename']: model['description'] for model in data_json}
-
-
-# def remove_endtags(html_string, tags):
-#     """Remove rear HTML tags from the input string."""
-#     for tag in tags:
-#         html_string = re.sub(fr"</{tag}>", "", html_string)
-#     return html_string
-
-
-# def replace_starttags(html_string, replacements):
-#     """Replace starting HTML tags with the corresponding values."""
-#     for tag, replacement in replacements.items():
-#         html_string = html_string.replace(tag, replacement)
-#     return html_string
-
-
-# def format_html_string(html_string):
-#     """Format the HTML string to a readable text format."""
-#     tags_to_remove = ["ul", "li", "br"]
-#     html_string = remove_endtags(html_string, tags_to_remove)
-
-#     tag_replacements = {
-#         "<li>": "\n➤ ",
-#         "<br>": "\n",
-#         "<strong>": "**",
-#         "</strong>": "**"
-#     }
-#     formatted_string = replace_starttags(html_string, tag_replacements)
-
-#     return formatted_string
-
-
-# def llm_intro(selected_model):
-#     html_string = model_description.get(selected_model, "No description available for this model selection.")
-#     formatted_description = format_html_string(html_string)
-#     return formatted_description
-    
-# # cache models for faster reloads
-# model_cache = {}  
-
-
-# def load_model(model_name):
-#     """
-#     This function checks the cache before loading a model.
-#     If the model is cached, it returns the cached version.
-#     Otherwise, it loads the model, caches it, and then returns it.
-#     """
-#     if model_name not in model_cache:
-#         model = GPT4All(model_name)  
-#         model_cache[model_name] = model
-#     return model_cache[model_name]
-
-#     clear = gr.ClearButton([input_text, chatbot])
-
 # Construct chatbot
 def generate_response(model_name, message, chat_history):
     model = load_model(model_name)
@@ -112,4 +45,6 @@ with gr.Blocks() as demo:
     message.submit(generate_response, inputs=[model_dropdown, message, state], outputs=[chatbot, state])
 
 # Launch the Gradio app
-demo.launch()
+demo.launch()
+
+#     clear = gr.ClearButton([input_text, chatbot])