adding debugging logs and excessive comments, will return in the morning :)

app.py

@@ -50,22 +50,24 @@ def respond(
 
     # Construct the messages array required by the API
     messages = [{"role": "system", "content": system_message}]
+    print("Initial messages array constructed.")
 
     # Add conversation history to the context
     for val in history:
-        user_part = val[0]
-        assistant_part = val[1]
+        user_part = val[0]  # Extract user message from the tuple
+        assistant_part = val[1]  # Extract assistant message from the tuple
         if user_part:
-            messages.append({"role": "user", "content": user_part})
+            messages.append({"role": "user", "content": user_part})  # Append user message
             print(f"Added user message to context: {user_part}")
         if assistant_part:
-            messages.append({"role": "assistant", "content": assistant_part})
+            messages.append({"role": "assistant", "content": assistant_part})  # Append assistant message
             print(f"Added assistant message to context: {assistant_part}")
 
     # Append the latest user message
     messages.append({"role": "user", "content": message})
+    print("Latest user message appended.")
 
-    # If user provided a model, use that; otherwise, fall back to a default
+    # If user provided a model, use that; otherwise, fall back to a default model
     model_to_use = custom_model.strip() if custom_model.strip() != "" else "meta-llama/Llama-3.3-70B-Instruct"
     print(f"Model selected for inference: {model_to_use}")
 
@@ -76,13 +78,13 @@ def respond(
     # Make the streaming request to the HF Inference API via openai-like client
     for message_chunk in client.chat.completions.create(
         model=model_to_use,              # Use either the user-provided or default model
-        max_tokens=max_tokens,
-        stream=True,                     # Stream the response
-        temperature=temperature,
-        top_p=top_p,
-        frequency_penalty=frequency_penalty,
-        seed=seed,
-        messages=messages,
+        max_tokens=max_tokens,           # Maximum tokens for the response
+        stream=True,                     # Enable streaming responses
+        temperature=temperature,         # Adjust randomness in response
+        top_p=top_p,                     # Control diversity in response generation
+        frequency_penalty=frequency_penalty,  # Penalize repeated phrases
+        seed=seed,                       # Set random seed for reproducibility
+        messages=messages,               # Contextual conversation messages
     ):
         # Extract the token text from the response chunk
         token_text = message_chunk.choices[0].delta.content
@@ -98,91 +100,94 @@ def respond(
 # -------------------------
 
 # Create a Chatbot component with a specified height
-chatbot = gr.Chatbot(height=600)
+chatbot = gr.Chatbot(height=600)  # Define the height of the chatbot interface
 print("Chatbot interface created.")
 
-# We'll create text boxes & sliders for system prompt, tokens, etc.
-system_message_box = gr.Textbox(value="", label="System message")
+# Create textboxes and sliders for system prompt, tokens, and other parameters
+system_message_box = gr.Textbox(value="", label="System message")  # Input box for system message
 
 max_tokens_slider = gr.Slider(
-    minimum=1,
-    maximum=4096,
-    value=512,
-    step=1,
-    label="Max new tokens"
+    minimum=1,   # Minimum allowable tokens
+    maximum=4096,  # Maximum allowable tokens
+    value=512,   # Default value
+    step=1,      # Increment step size
+    label="Max new tokens"  # Slider label
 )
 temperature_slider = gr.Slider(
-    minimum=0.1,
-    maximum=4.0,
-    value=0.7,
-    step=0.1,
-    label="Temperature"
+    minimum=0.1,  # Minimum temperature
+    maximum=4.0,  # Maximum temperature
+    value=0.7,    # Default value
+    step=0.1,     # Increment step size
+    label="Temperature"  # Slider label
 )
 top_p_slider = gr.Slider(
-    minimum=0.1,
-    maximum=1.0,
-    value=0.95,
-    step=0.05,
-    label="Top-P"
+    minimum=0.1,  # Minimum top-p value
+    maximum=1.0,  # Maximum top-p value
+    value=0.95,   # Default value
+    step=0.05,    # Increment step size
+    label="Top-P"  # Slider label
 )
 frequency_penalty_slider = gr.Slider(
-    minimum=-2.0,
-    maximum=2.0,
-    value=0.0,
-    step=0.1,
-    label="Frequency Penalty"
+    minimum=-2.0,  # Minimum penalty
+    maximum=2.0,   # Maximum penalty
+    value=0.0,     # Default value
+    step=0.1,      # Increment step size
+    label="Frequency Penalty"  # Slider label
 )
 seed_slider = gr.Slider(
-    minimum=-1,
-    maximum=65535,
-    value=-1,
-    step=1,
-    label="Seed (-1 for random)"
+    minimum=-1,    # -1 for random seed
+    maximum=65535, # Maximum seed value
+    value=-1,      # Default value
+    step=1,        # Increment step size
+    label="Seed (-1 for random)"  # Slider label
 )
 
 # The custom_model_box is what the respond function sees as "custom_model"
 custom_model_box = gr.Textbox(
-    value="",
-    label="Custom Model",
-    info="(Optional) Provide a custom Hugging Face model path. Overrides any selected featured model."
+    value="",  # Default value
+    label="Custom Model",  # Label for the textbox
+    info="(Optional) Provide a custom Hugging Face model path. Overrides any selected featured model."  # Additional info
 )
 
-# Define a function that, when a user selects a model from the radio, populates `custom_model_box`
+# Define a function that updates the custom model box when a featured model is selected
 def set_custom_model_from_radio(selected):
     """
     This function will get triggered whenever someone picks a model from the 'Featured Models' radio.
     We will update the Custom Model text box with that selection automatically.
     """
+    print(f"Featured model selected: {selected}")  # Log selected model
     return selected
 
-# The main ChatInterface object
+# Create the main ChatInterface object
 demo = gr.ChatInterface(
-    fn=respond,
-    # For ChatInterface, we can pass additional inputs in order to feed them into the "respond" function
+    fn=respond,  # The function to handle responses
     additional_inputs=[
-        system_message_box,
-        max_tokens_slider,
-        temperature_slider,
-        top_p_slider,
-        frequency_penalty_slider,
-        seed_slider,
-        custom_model_box
+        system_message_box,          # System message input
+        max_tokens_slider,           # Max tokens slider
+        temperature_slider,          # Temperature slider
+        top_p_slider,                # Top-P slider
+        frequency_penalty_slider,    # Frequency penalty slider
+        seed_slider,                 # Seed slider
+        custom_model_box             # Custom model input
     ],
-    fill_height=True,
-    chatbot=chatbot,
-    theme="Nymbo/Nymbo_Theme",
+    fill_height=True,  # Allow the chatbot to fill the container height
+    chatbot=chatbot,   # Chatbot UI component
+    theme="Nymbo/Nymbo_Theme",  # Theme for the interface
 )
 
+print("ChatInterface object created.")
+
 # -----------
 # ADDING THE "FEATURED MODELS" ACCORDION
 # -----------
 with demo:
-    with gr.Accordion("Featured Models", open=False):
+    with gr.Accordion("Featured Models", open=False):  # Collapsible section for featured models
         model_search_box = gr.Textbox(
-            label="Filter Models",
-            placeholder="Search for a featured model...",
-            lines=1
+            label="Filter Models",  # Label for the search box
+            placeholder="Search for a featured model...",  # Placeholder text
+            lines=1  # Single-line input
         )
+        print("Model search box created.")
 
         # Sample list of popular text models
         models_list = [
@@ -204,32 +209,38 @@ with demo:
             "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
             "microsoft/Phi-3.5-mini-instruct",
         ]
+        print("Models list initialized.")
 
         featured_model_radio = gr.Radio(
-            label="Select a model below",
-            choices=models_list,
-            value="meta-llama/Llama-3.3-70B-Instruct",
-            interactive=True
+            label="Select a model below",  # Label for the radio buttons
+            choices=models_list,  # List of available models
+            value="meta-llama/Llama-3.3-70B-Instruct",  # Default selection
+            interactive=True  # Allow user interaction
         )
+        print("Featured models radio button created.")
 
-        # Filter function for the radio
-        def filter_models(search_term):
-            filtered = [m for m in models_list if search_term.lower() in m.lower()]
+        # Filter function for the radio button list
+def filter_models(search_term):
+            print(f"Filtering models with search term: {search_term}")  # Log the search term
+            filtered = [m for m in models_list if search_term.lower() in m.lower()]  # Filter models by search term
+            print(f"Filtered models: {filtered}")  # Log filtered models
             return gr.update(choices=filtered)
 
-        # Whenever we type in the search box, update the radio with the filtered list
+        # Update the radio list when the search box value changes
         model_search_box.change(
-            fn=filter_models,
-            inputs=model_search_box,
-            outputs=featured_model_radio
+            fn=filter_models,  # Function to filter models
+            inputs=model_search_box,  # Input: search box value
+            outputs=featured_model_radio  # Output: update radio button list
         )
+        print("Model search box change event linked.")
 
-        # Whenever we select a featured model, populate the 'Custom Model' textbox
+        # Update the custom model textbox when a featured model is selected
         featured_model_radio.change(
-            fn=set_custom_model_from_radio,
-            inputs=featured_model_radio,
-            outputs=custom_model_box
+            fn=set_custom_model_from_radio,  # Function to set custom model
+            inputs=featured_model_radio,  # Input: selected model
+            outputs=custom_model_box  # Output: update custom model textbox
         )
+        print("Featured model radio button change event linked.")
 
 print("Gradio interface initialized.")