Update app.py

app.py

@@ -23,15 +23,7 @@ def respond(
     seed,
     custom_model
 ):
-    """
-    This function handles the conversation logic and streams the response.
 
-    Arguments:
-    - message: The new user message
-    - history: Chat history in the form of a list of (user_message, assistant_message) pairs
-    - system_message: The system prompt specifying how the assistant should behave
-    - max_tokens, temperature, top_p, frequency_penalty, seed, custom_model: Various parameters for text generation
-    """
     print(f"Received message: {message}")
     print(f"History: {history}")
     print(f"System message: {system_message}")
@@ -43,7 +35,6 @@ def respond(
     if seed == -1:
         seed = None
 
-    # Create the base system-level message
     messages = [{"role": "system", "content": system_message}]
     print("Initial messages array constructed.")
 
@@ -70,7 +61,6 @@ def respond(
     response = ""
     print("Sending request to OpenAI API.")
 
-    # Stream tokens from the HF inference endpoint
     for message_chunk in client.chat.completions.create(
         model=model_to_use,
         max_tokens=max_tokens,
@@ -88,29 +78,13 @@ def respond(
 
     print("Completed response generation.")
 
+# GRADIO UI
 
-# -------------------------
-# Gradio UI definitions
-# -------------------------
-
-# Chatbot interface
-chatbot = gr.Chatbot(
-    height=600, 
-    show_copy_button=True, 
-    placeholder="Select a model and begin chatting", 
-    likeable=True, 
-    layout="panel"
-)
+chatbot = gr.Chatbot(height=600, show_copy_button=True, placeholder="Select a model and begin chatting", likeable=True, layout="panel")
 print("Chatbot interface created.")
 
-# System prompt textbox
-system_message_box = gr.Textbox(
-    value="", 
-    placeholder="You are a helpful assistant.", 
-    label="System Prompt"
-)
+system_message_box = gr.Textbox(value="", placeholder="You are a helpful assistant.", label="System Prompt")
 
-# Sliders
 max_tokens_slider = gr.Slider(
     minimum=1,
     maximum=4096,
@@ -147,9 +121,7 @@ seed_slider = gr.Slider(
     label="Seed (-1 for random)"
 )
 
-# This textbox is what the respond() function sees as "custom_model"
-# We will visually place it inside the Model Selection accordion (below),
-# but we define it here so it can be passed to the ChatInterface.
+# Move the custom_model_box definition to be used inside the accordion
 custom_model_box = gr.Textbox(
     value="",
     label="Custom Model",
@@ -157,7 +129,14 @@ custom_model_box = gr.Textbox(
     placeholder="meta-llama/Llama-3.3-70B-Instruct"
 )
 
-# Create the ChatInterface, referencing the respond function and including all inputs
+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}")
+    return selected
+
 demo = gr.ChatInterface(
     fn=respond,
     additional_inputs=[
@@ -167,7 +146,7 @@ demo = gr.ChatInterface(
         top_p_slider,
         frequency_penalty_slider,
         seed_slider,
-        custom_model_box,  # We pass it here to the ChatInterface function
+        custom_model_box,  # Keep this reference here for the respond function
     ],
     fill_height=True,
     chatbot=chatbot,
@@ -175,67 +154,41 @@ demo = gr.ChatInterface(
 )
 print("ChatInterface object created.")
 
-
-# --------------------------
-# Additional Model Selection
-# --------------------------
-
-# This is the function that updates the Custom Model textbox whenever the user picks a model from the Radio
-def set_custom_model_from_radio(selected):
-    """
-    Triggered when the user 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}")
-    return selected
-
-# The set of models displayed in the radio
-models_list = [
-    "meta-llama/Llama-3.3-70B-Instruct",
-    "meta-llama/Llama-3.2-3B-Instruct",
-    "meta-llama/Llama-3.2-1B-Instruct",
-    "meta-llama/Llama-3.1-8B-Instruct",
-    "NousResearch/Hermes-3-Llama-3.1-8B",
-    "google/gemma-2-27b-it",
-    "google/gemma-2-9b-it",
-    "google/gemma-2-2b-it",
-    "mistralai/Mistral-Nemo-Instruct-2407",
-    "mistralai/Mixtral-8x7B-Instruct-v0.1",
-    "mistralai/Mistral-7B-Instruct-v0.3",
-    "Qwen/Qwen2.5-72B-Instruct",
-    "Qwen/QwQ-32B-Preview",
-    "PowerInfer/SmallThinker-3B-Preview",
-    "HuggingFaceTB/SmolLM2-1.7B-Instruct",
-    "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
-    "microsoft/Phi-3.5-mini-instruct",
-]
-print("Models list initialized.")
-
-# This function handles searching for models by a user-provided filter
-def filter_models(search_term):
-    print(f"Filtering models with search term: {search_term}")
-    filtered = [m for m in models_list if search_term.lower() in m.lower()]
-    print(f"Filtered models: {filtered}")
-    return gr.update(choices=filtered)
-
-
-# --------------------------------
-# Advanced UI arrangement with demo
-# --------------------------------
 with demo:
-    # Create an Accordion for model selection
     with gr.Accordion("Model Selection", open=False):
-        # Place the Filter Models textbox and the Custom Model textbox side by side
+        # Create a row for the search and custom model inputs
         with gr.Row():
             model_search_box = gr.Textbox(
                 label="Filter Models",
                 placeholder="Search for a featured model...",
-                lines=1
+                lines=1,
+                scale=1  # Equal scaling with custom_model_box
             )
-            # Render the already-defined 'custom_model_box' so it appears in this row
-            custom_model_box.render()
+            # Place the custom model box here, alongside the search box
+            custom_model_box.render()  # Render the previously defined textbox here
+        print("Model search box and custom model box created.")
+
+        models_list = [
+            "meta-llama/Llama-3.3-70B-Instruct",
+            "meta-llama/Llama-3.2-3B-Instruct",
+            "meta-llama/Llama-3.2-1B-Instruct",
+            "meta-llama/Llama-3.1-8B-Instruct",
+            "NousResearch/Hermes-3-Llama-3.1-8B",
+            "google/gemma-2-27b-it",
+            "google/gemma-2-9b-it",
+            "google/gemma-2-2b-it",
+            "mistralai/Mistral-Nemo-Instruct-2407",
+            "mistralai/Mixtral-8x7B-Instruct-v0.1",
+            "mistralai/Mistral-7B-Instruct-v0.3",
+            "Qwen/Qwen2.5-72B-Instruct",
+            "Qwen/QwQ-32B-Preview",
+            "PowerInfer/SmallThinker-3B-Preview",
+            "HuggingFaceTB/SmolLM2-1.7B-Instruct",
+            "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+            "microsoft/Phi-3.5-mini-instruct",
+        ]
+        print("Models list initialized.")
 
-        # Create the Radio for featured models
         featured_model_radio = gr.Radio(
             label="Select a model below",
             choices=models_list,
@@ -244,7 +197,12 @@ with demo:
         )
         print("Featured models radio button created.")
 
-        # Link the search box to the filtering function
+        def filter_models(search_term):
+            print(f"Filtering models with search term: {search_term}")
+            filtered = [m for m in models_list if search_term.lower() in m.lower()]
+            print(f"Filtered models: {filtered}")
+            return gr.update(choices=filtered)
+
         model_search_box.change(
             fn=filter_models,
             inputs=model_search_box,
@@ -252,7 +210,6 @@ with demo:
         )
         print("Model search box change event linked.")
 
-        # Link the radio to the function that sets the custom model textbox
         featured_model_radio.change(
             fn=set_custom_model_from_radio,
             inputs=featured_model_radio,
@@ -262,10 +219,6 @@ with demo:
 
 print("Gradio interface initialized.")
 
-
-# -----------------------
-# Launch the application
-# -----------------------
 if __name__ == "__main__":
     print("Launching the demo application.")
     demo.launch()