Update app.py

Added comments

app.py

@@ -1,32 +1,41 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
+# the training data to use
 client = InferenceClient("Qwen/Qwen2.5-7B-Instruct")
+# the container of the response of the chatbot
 response = ""
 
+# load climate information on Chicago to instruct chatbot
 def load_weather_info():
     with open("chicago_weather.txt", "r", encoding="utf-8") as file:
         design_knowledge = file.read()
     return design_knowledge
 
+# load aesthetics info on different styles to instruct chatbot on what styles to recommend and what theyre comprised of
 def load_aesthetics_info():
     with open("clothing_aesthetics_breakdown.txt", "r", encoding="utf-8") as file:
         aesthetics_knowledge = file.read()
     return aesthetics_knowledge
-    
+
+# function where chatbot, whose role is outfit picker, generates response in a visual stream
 def respond(message, history):
 
+    # load info from txt files
     weather = load_weather_info()
     aesthetics = load_aesthetics_info()
+    # hold messages from chat and assign role to chatbot
     messages = [{"role": "system", 
                  "content": f"You are an expert in picking out awesome cute outfits based on the weather in Chicago using this info: {weather} and based on aesthetics styles using this info: {aesthetics}. If the user asks for an outfit, give a top, bottom, and shoe recommendation with details about color, material, and length based on the current weather. Ask them about their preferred style to collect knowledge on what to recommend. Keep the response to 250 words while being friendly and informal!"}]
-    
+
+    # if there is a history add the message to it
     if history:
         messages.extend(history)
-        
+
+    # add the user's message as their content
     messages.append({"role": "user", "content": message})
 
-    # Adjusted temperature to be variable enough to offer a diversity of possible solutions
+    # make responses appear as a stream, limit to 350 tokens, and temp 0.6 to be both creative and direct
     response = client.chat_completion(
         messages,
         max_tokens=350,
@@ -34,12 +43,14 @@ def respond(message, history):
         stream=True
     )
 
+    # holds responses of the chatbot
     responses = ""
     for message in response:
         token = message.choices[0].delta.content
         responses += token
         yield responses
 
+# define chatbot
 chatbot = gr.ChatInterface(respond)
 
 chatbot.launch(debug=True)