Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 import os
+import json
 
 ACCESS_TOKEN = os.getenv("HF_TOKEN")
 print("Access token loaded.")
@@ -106,7 +107,6 @@ def respond(
 
 # GRADIO UI
 
-# Define all the UI components first
 chatbot = gr.Chatbot(height=600, show_copy_button=True, placeholder="Select a model and begin chatting", layout="panel")
 print("Chatbot interface created.")
 
@@ -149,6 +149,14 @@ seed_slider = gr.Slider(
     label="Seed (-1 for random)"
 )
 
+# Custom model box
+custom_model_box = gr.Textbox(
+    value="",
+    label="Custom Model",
+    info="(Optional) Provide a custom Hugging Face model path. Overrides any selected featured model.",
+    placeholder="meta-llama/Llama-3.3-70B-Instruct"
+)
+
 # Provider selection
 providers_list = [
     "hf-inference",  # Default Hugging Face Inference
@@ -160,7 +168,6 @@ providers_list = [
     "fireworks-ai",  # Fireworks AI
     "hyperbolic",    # Hyperbolic
     "nebius",        # Nebius
-    "openai"         # OpenAI compatible endpoints
 ]
 
 provider_radio = gr.Radio(
@@ -171,6 +178,12 @@ provider_radio = gr.Radio(
 )
 
 # Model selection components
+model_search_box = gr.Textbox(
+    label="Filter Models",
+    placeholder="Search for a featured model...",
+    lines=1
+)
+
 models_list = [
     "meta-llama/Llama-3.3-70B-Instruct",
     "meta-llama/Llama-3.1-70B-Instruct",
@@ -223,79 +236,43 @@ def set_custom_model_from_radio(selected):
     print(f"Featured model selected: {selected}")
     return selected
 
-# Create the Gradio interface with blocks for more control
-with gr.Blocks(theme="Nymbo/Nymbo_Theme") as demo:
-    with gr.Row():
-        # Create the main chat area
-        with gr.Column(scale=3):
-            # Add the chatbot UI
-            chat_interface = gr.ChatInterface(
-                respond,
-                chatbot=chatbot,
-                additional_inputs=[
-                    system_message_box,
-                    max_tokens_slider,
-                    temperature_slider,
-                    top_p_slider,
-                    frequency_penalty_slider,
-                    seed_slider,
-                    # These will be added manually outside the ChatInterface
-                    # custom_model_box,
-                    # model_search_box,
-                    provider_radio,
-                    # featured_model_radio will be linked manually
-                ]
-            )
-        
-    # Put the "Custom Model" and "Filter Models" textboxes in the same row
-    with gr.Row():
-        with gr.Column(scale=1):
-            custom_model_box = gr.Textbox(
-                value="",
-                label="Custom Model",
-                info="(Optional) Provide a custom Hugging Face model path. Overrides any selected featured model.",
-                placeholder="meta-llama/Llama-3.3-70B-Instruct"
-            )
-        with gr.Column(scale=1):
-            model_search_box = gr.Textbox(
-                label="Filter Models",
-                placeholder="Search for a featured model...",
-                lines=1
-            )
-    
-    # Add the featured model radio separately
-    featured_model_radio_display = gr.Radio(
-        label="Select a model below",
-        choices=models_list,
-        value="meta-llama/Llama-3.3-70B-Instruct",
-        interactive=True
-    )
-    
+# Create the Gradio interface
+demo = gr.ChatInterface(
+    fn=respond,
+    additional_inputs=[
+        system_message_box,
+        max_tokens_slider,
+        temperature_slider,
+        top_p_slider,
+        frequency_penalty_slider,
+        seed_slider,
+        custom_model_box,
+        provider_radio,     # Provider selection
+        model_search_box,    # Model search box
+        featured_model_radio # Featured model radio
+    ],
+    fill_height=True,
+    chatbot=chatbot,
+    theme="Nymbo/Nymbo_Theme",
+)
+print("ChatInterface object created.")
+
+with demo:
     # Connect the model filter to update the radio choices
     model_search_box.change(
         fn=filter_models,
         inputs=model_search_box,
-        outputs=featured_model_radio_display
+        outputs=featured_model_radio
     )
     print("Model search box change event linked.")
 
     # Connect the featured model radio to update the custom model box
-    featured_model_radio_display.change(
+    featured_model_radio.change(
         fn=set_custom_model_from_radio,
-        inputs=featured_model_radio_display,
+        inputs=featured_model_radio,
         outputs=custom_model_box
     )
     print("Featured model radio button change event linked.")
-    
-    # Make sure the custom model and selected model are passed to the respond function
-    def modified_respond(*args):
-        # The last two arguments are supposed to be model_search_term and selected_model
-        args_list = list(args)
-        args_list[-2] = model_search_box.value  # Set the model_search_term
-        args_list[-1] = featured_model_radio_display.value  # Set the selected_model
-        return respond(*args_list)
-    
-    chat_interface.chatbot.submit_callback = modified_respond
 
 print("Gradio interface initialized.")