Update app.py

app.py

@@ -22,31 +22,20 @@ def respond(
     top_p,
     frequency_penalty,
     seed,
-    model
+    model,
+    custom_model
 ):
     """
-    This function handles the chatbot response. It takes in:
-    - message: the user's new message
-    - history: the list of previous messages, each as a tuple (user_msg, assistant_msg)
-    - system_message: the system prompt
-    - max_tokens: the maximum number of tokens to generate in the response
-    - temperature: sampling temperature
-    - top_p: top-p (nucleus) sampling
-    - frequency_penalty: penalize repeated tokens in the output
-    - seed: a fixed seed for reproducibility; -1 will mean 'random'
-    - model: the selected model for text generation
+    Handles the chatbot response with given parameters.
     """
-
     print(f"Received message: {message}")
     print(f"History: {history}")
     print(f"System message: {system_message}")
-    print(f"Max tokens: {max_tokens}, Temperature: {temperature}, Top-P: {top_p}")
-    print(f"Frequency Penalty: {frequency_penalty}, Seed: {seed}")
-    print(f"Model: {model}")
+    print(f"Model: {model}, Custom Model: {custom_model}")
 
-    # Convert seed to None if -1 (meaning random)
-    if seed == -1:
-        seed = None
+    # Use custom model if provided, else use selected model
+    selected_model = custom_model.strip() if custom_model.strip() else model
+    print(f"Selected model: {selected_model}")
 
     # Construct the messages array required by the API
     messages = [{"role": "system", "content": system_message}]
@@ -69,15 +58,15 @@ def respond(
     response = ""
     print("Sending request to OpenAI API.")
 
-    # Make the streaming request to the HF Inference API via openai-like client
+    # Make the streaming request to the HF Inference API via OpenAI-like client
     for message_chunk in client.chat.completions.create(
-        model=model,   # Use the selected model
+        model=selected_model,
         max_tokens=max_tokens,
-        stream=True,  # Stream the response
+        stream=True,
         temperature=temperature,
         top_p=top_p,
-        frequency_penalty=frequency_penalty,  # <-- NEW
-        seed=seed,                             # <-- NEW
+        frequency_penalty=frequency_penalty,
+        seed=seed if seed != -1 else None,
         messages=messages,
     ):
         # Extract the token text from the response chunk
@@ -88,179 +77,88 @@ def respond(
 
     print("Completed response generation.")
 
-# Create a Chatbot component with a specified height
+# Create a Chatbot component
 chatbot = gr.Chatbot(height=600)
 print("Chatbot interface created.")
 
-# Create the Gradio ChatInterface
-# We add two new sliders for Frequency Penalty and Seed
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="", label="System message"),
-        gr.Slider(minimum=1,   maximum=4096, value=512, step=1,   label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0,  value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(minimum=0.1, maximum=1.0,  value=0.95, step=0.05, label="Top-P"),
-        gr.Slider(
-            minimum=-2.0,
-            maximum=2.0,
-            value=0.0,
-            step=0.1,
-            label="Frequency Penalty"
-        ),
-        gr.Slider(
-            minimum=-1,
-            maximum=65535,  # Arbitrary upper limit for demonstration
-            value=-1,
-            step=1,
-            label="Seed (-1 for random)"
-        ),
-        gr.Textbox(label="Custom Model", info="Model Hugging Face path (optional)", placeholder="meta-llama/Llama-3.3-70B-Instruct"),
-    ],
-    fill_height=True,
-    chatbot=chatbot,
-    theme="Nymbo/Nymbo_Theme",
-)
-print("Gradio interface initialized.")
-
-# Custom CSS to hide the footer in the interface
-css = """
-* {}
-footer {visibility: hidden !important;}
-"""
-
-print("Initializing Gradio interface...")  # Debug log
+# Define the featured models for the dropdown
+models_list = [
+    "meta-llama/Llama-3.3-70B-Instruct",
+    "bigscience/bloom-176b",
+    "gpt-j-6b",
+    "opt-30b",
+    "flan-t5-xxl",
+]
+
+# Function to filter models based on user input
+def filter_models(search_term):
+    return [m for m in models_list if search_term.lower() in m.lower()]
+
+# Gradio interface
+with gr.Blocks(theme="Nymbo/Nymbo_Theme") as demo:
+    with gr.Row():
+        chatbot = gr.Chatbot(height=600)
 
-# Define the Gradio interface
-with gr.Blocks(theme='Nymbo/Nymbo_Theme_5') as textgen:
-    # Tab for basic settings
-    with gr.Tab("Basic Settings"):
+    with gr.Tab("Chat Interface"):
         with gr.Row():
-            with gr.Column(elem_id="prompt-container"):
-                with gr.Row():
-                    # Textbox for user to input the prompt
-                    text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=3, elem_id="prompt-text-input")
-                with gr.Row():
-                    # Textbox for custom model input
-                    custom_model = gr.Textbox(label="Custom Model", info="Model Hugging Face path (optional)", placeholder="meta-llama/Llama-3.3-70B-Instruct")
-                with gr.Row():
-                    # Accordion for selecting the model
-                    with gr.Accordion("Featured Models", open=True):
-                        # Textbox for searching models
-                        model_search = gr.Textbox(label="Filter Models", placeholder="Search for a featured model...", lines=1, elem_id="model-search-input")
-                        models_list = (
-                            "meta-llama/Llama-3.3-70B-Instruct",
-                            "meta-llama/Llama-3.3-30B-Instruct",
-                            "meta-llama/Llama-3.3-13B-Instruct",
-                            "meta-llama/Llama-3.3-7B-Instruct",
-                        )
-
-                        # Radio buttons to select the desired model
-                        model = gr.Radio(label="Select a model below", value="meta-llama/Llama-3.3-70B-Instruct", choices=models_list, interactive=True, elem_id="model-radio")
-
-                        # Filtering models based on search input
-                        def filter_models(search_term):
-                            filtered_models = [m for m in models_list if search_term.lower() in m.lower()]
-                            return gr.update(choices=filtered_models)
-
-                        # Update model list when search box is used
-                        model_search.change(filter_models, inputs=model_search, outputs=model)
-
-    # Tab for advanced settings
-    with gr.Tab("Advanced Settings"):
+            user_input = gr.Textbox(label="Your Message", placeholder="Type your message here...")
         with gr.Row():
-            # Slider for setting the maximum number of new tokens
-            max_tokens = gr.Slider(label="Max new tokens", value=512, minimum=1, maximum=4096, step=1)
+            system_message = gr.Textbox(value="", label="System Message")
         with gr.Row():
-            # Slider for adjusting the temperature
-            temperature = gr.Slider(label="Temperature", value=0.7, minimum=0.1, maximum=4.0, step=0.1)
+            max_tokens = gr.Slider(minimum=1, maximum=4096, value=512, step=1, label="Max Tokens")
+            temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature")
         with gr.Row():
-            # Slider for adjusting the top-p (nucleus) sampling
-            top_p = gr.Slider(label="Top-P", value=0.95, minimum=0.1, maximum=1.0, step=0.05)
+            top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.05, label="Top-P")
+            frequency_penalty = gr.Slider(minimum=-2.0, maximum=2.0, value=0.0, step=0.1, label="Frequency Penalty")
+            seed = gr.Slider(minimum=-1, maximum=65535, value=-1, step=1, label="Seed (-1 for random)")
         with gr.Row():
-            # Slider for adjusting the frequency penalty
-            frequency_penalty = gr.Slider(label="Frequency Penalty", value=0.0, minimum=-2.0, maximum=2.0, step=0.1)
+            model = gr.Dropdown(label="Select a Model", choices=models_list, value="meta-llama/Llama-3.3-70B-Instruct")
+            custom_model = gr.Textbox(label="Custom Model", placeholder="Enter custom model path")
         with gr.Row():
-            # Slider for setting the seed for reproducibility
-            seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=65535, step=1)
+            run_button = gr.Button("Generate Response")
 
-    # Tab to provide information to the user
     with gr.Tab("Information"):
-        with gr.Row():
-            # Display a sample prompt for guidance
-            gr.Textbox(label="Sample prompt", value="{prompt} | ultra detail, ultra elaboration, ultra quality, perfect.")
-
-        # Accordion displaying featured models
-        with gr.Accordion("Featured Models (WiP)", open=False):
+        with gr.Accordion("Featured Models", open=False):
             gr.HTML(
                 """
-            <p><a href="https://huggingface.co/models?inference=warm&pipeline_tag=text-generation&sort=trending">See all available models</a></p>
-            <table style="width:100%; text-align:center; margin:auto;">
-                <tr>
-                    <th>Model Name</th>
-                    <th>Typography</th>
-                    <th>Notes</th>
-                </tr>
-                <tr>
-                    <td>meta-llama/Llama-3.3-70B-Instruct</td>
-                    <td>✅</td>
-                    <td></td>
-                </tr>
-                <tr>
-                    <td>meta-llama/Llama-3.3-30B-Instruct</td>
-                    <td>✅</td>
-                    <td></td>
-                </tr>
-                <tr>
-                    <td>meta-llama/Llama-3.3-13B-Instruct</td>
-                    <td>✅</td>
-                    <td></td>
-                </tr>
-                <tr>
-                    <td>meta-llama/Llama-3.3-7B-Instruct</td>
-                    <td>✅</td>
-                    <td></td>
-                </tr>
-            </table>
-            """
+                <table>
+                    <tr><th>Model Name</th><th>Description</th></tr>
+                    <tr><td>meta-llama/Llama-3.3-70B-Instruct</td><td>Instruction-tuned LLaMA model</td></tr>
+                    <tr><td>bigscience/bloom-176b</td><td>Multilingual large language model</td></tr>
+                    <tr><td>gpt-j-6b</td><td>Open-source GPT model</td></tr>
+                    <tr><td>opt-30b</td><td>Meta's OPT model</td></tr>
+                    <tr><td>flan-t5-xxl</td><td>Google's Flan-tuned T5 XXL</td></tr>
+                </table>
+                """
             )
-
-        # Accordion providing an overview of advanced settings
         with gr.Accordion("Parameters Overview", open=False):
             gr.Markdown(
-            """
-            ## System Message
-            ###### This box is for setting the system prompt, which guides the AI's behavior and context.
-
-            ## Max New Tokens
-            ###### This slider allows you to specify the maximum number of tokens (words or parts of words) the AI will generate in response to your prompt. The default value is 512.
-
-            ## Temperature
-            ###### Temperature controls the randomness of the AI's output. A higher temperature makes the output more random and creative, while a lower temperature makes it more predictable and focused.
-
-            ## Top-P (Nucleus Sampling)
-            ###### Top-P sampling is a technique that selects the smallest set of top tokens whose cumulative probability exceeds a threshold (p). This helps in generating more coherent and relevant responses.
-
-            ## Frequency Penalty
-            ###### This parameter penalizes repeated tokens in the output, encouraging the AI to generate more diverse responses. A higher value means more penalty for repetition.
-
-            ## Seed
-            ###### The seed is a value that ensures reproducibility. If you set a specific seed, the AI will generate the same output for the same input. Setting it to -1 means the seed will be random.
-
-            ### Remember, these settings are all about giving you control over the text generation process. Feel free to experiment and see what each one does. And if you're ever in doubt, the default settings are a great place to start. Happy creating!
-            """
+                """
+                ### Parameters Overview
+                - **Max Tokens**: Maximum number of tokens in the response.
+                - **Temperature**: Controls the randomness of responses. Lower values make the output more deterministic.
+                - **Top-P**: Controls the diversity of responses by limiting the token selection to a probability mass.
+                - **Frequency Penalty**: Penalizes repeated tokens in the output.
+                - **Seed**: Fixes randomness for reproducibility. Use -1 for a random seed.
+                """
             )
 
-    # Row containing the 'Run' button to trigger the text generation
-    with gr.Row():
-        text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
-    # Row for displaying the generated text output
-    with gr.Row():
-        text_output = gr.Textbox(label="Text Output", elem_id="text-output")
-
-    # Set up button click event to call the respond function
-    text_button.click(respond, inputs=[text_prompt, chatbot, gr.Textbox(value="", label="System message"), max_tokens, temperature, top_p, frequency_penalty, seed, model], outputs=text_output)
-
-print("Launching Gradio interface...")  # Debug log
-# Launch the Gradio interface without showing the API or sharing externally
-textgen.launch(show_api=False, share=False)
+    run_button.click(
+        respond,
+        inputs=[
+            user_input,
+            chatbot.state,
+            system_message,
+            max_tokens,
+            temperature,
+            top_p,
+            frequency_penalty,
+            seed,
+            model,
+            custom_model
+        ],
+        outputs=chatbot
+    )
+
+print("Launching the demo application.")
+demo.launch()