Update app.py

app.py

@@ -16,63 +16,71 @@ print("OpenAI client initialized.")
 def respond(
     message,
     history: list[tuple[str, str]],
-    model,
-    custom_model,
     system_message,
     max_tokens,
     temperature,
     top_p,
     frequency_penalty,
-    seed
+    seed,
+    model
 ):
     """
-    This function handles the chatbot response.
+    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 model to use for text generation
     """
+
     print(f"Received message: {message}")
     print(f"History: {history}")
-    print(f"Model: {model}")
-    print(f"Custom model: {custom_model}")
     print(f"System message: {system_message}")
-    print(f"Parameters - Max tokens: {max_tokens}, Temperature: {temperature}, Top-P: {top_p}")
+    print(f"Max tokens: {max_tokens}, Temperature: {temperature}, Top-P: {top_p}")
     print(f"Frequency Penalty: {frequency_penalty}, Seed: {seed}")
+    print(f"Model: {model}")
 
-    # Convert seed to None if -1
+    # Convert seed to None if -1 (meaning random)
     if seed == -1:
         seed = None
 
-    # Set the model based on selection or custom input
-    selected_model = custom_model.strip() if custom_model.strip() != "" else model
-
-    # Construct messages array
+    # Construct the messages array required by the API
     messages = [{"role": "system", "content": system_message}]
 
-    # Add conversation history
+    # Add conversation history to the context
     for val in history:
         user_part = val[0]
         assistant_part = val[1]
         if user_part:
             messages.append({"role": "user", "content": user_part})
+            print(f"Added user message to context: {user_part}")
         if assistant_part:
             messages.append({"role": "assistant", "content": assistant_part})
+            print(f"Added assistant message to context: {assistant_part}")
 
-    # Append latest message
+    # Append the latest user message
     messages.append({"role": "user", "content": message})
 
-    # Start with empty response
+    # Start with an empty string to build the response as tokens stream in
     response = ""
-    print("Sending request to API.")
+    print("Sending request to OpenAI API.")
 
-    # Make the streaming request
+    # Make the streaming request to the HF Inference API via openai-like client
     for message_chunk in client.chat.completions.create(
-        model=selected_model,
+        model=model,   # Use the selected model
         max_tokens=max_tokens,
-        stream=True,
+        stream=True,  # Stream the response
         temperature=temperature,
         top_p=top_p,
-        frequency_penalty=frequency_penalty,
-        seed=seed,
+        frequency_penalty=frequency_penalty,  # <-- NEW
+        seed=seed,                             # <-- NEW
         messages=messages,
     ):
+        # Extract the token text from the response chunk
         token_text = message_chunk.choices[0].delta.content
         print(f"Received token: {token_text}")
         response += token_text
@@ -80,135 +88,168 @@ def respond(
 
     print("Completed response generation.")
 
-# Create Chatbot component
+# Create a Chatbot component with a specified height
 chatbot = gr.Chatbot(height=600)
 print("Chatbot interface created.")
 
-# Define available models
-models_list = [
-    "meta-llama/Llama-2-70b-chat-hf",
-    "meta-llama/Llama-2-13b-chat-hf",
-    "mistralai/Mixtral-8x7B-Instruct-v0.1",
-    "mistralai/Mistral-7B-Instruct-v0.2",
-    "HuggingFaceH4/zephyr-7b-beta",
-]
-
-# Create the Gradio interface with tabs
-with gr.Blocks(theme="Nymbo/Nymbo_Theme") as demo:
-    with gr.Tab("Chat"):
+# 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", placeholder="Enter a custom model path"),
+    ],
+    fill_height=True,
+    chatbot=chatbot,
+    theme="Nymbo/Nymbo_Theme",
+)
+print("Gradio interface initialized.")
+
+# 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.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-3.3-13B-Instruct",
+                            "meta-llama/Llama-3.3-30B-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"):
+        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)
+        with gr.Row():
+            # Slider for setting the temperature
+            temperature = gr.Slider(label="Temperature", value=0.7, minimum=0.1, maximum=4.0, step=0.1)
         with gr.Row():
-            with gr.Column():
-                # Model selection accordion
-                with gr.Accordion("Featured Models", open=True):
-                    model_search = gr.Textbox(
-                        label="Filter Models",
-                        placeholder="Search for a model...",
-                        lines=1
-                    )
-                    model = gr.Radio(
-                        label="Select a model",
-                        choices=models_list,
-                        value="meta-llama/Llama-2-70b-chat-hf"
-                    )
-                
-                # Custom model input
-                custom_model = gr.Textbox(
-                    label="Custom Model",
-                    info="Enter Hugging Face model path (optional)",
-                    placeholder="organization/model-name"
-                )
-
-                # System message and parameters
-                system_message = gr.Textbox(label="System message")
-                max_tokens = gr.Slider(minimum=1, maximum=4096, value=512, step=1, label="Max new tokens")
-                temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
-                top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, 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)")
+            # Slider for setting the top-p (nucleus) sampling
+            top_p = gr.Slider(label="Top-P", value=0.95, minimum=0.1, maximum=1.0, step=0.05)
+        with gr.Row():
+            # Slider for setting the frequency penalty
+            frequency_penalty = gr.Slider(label="Frequency Penalty", value=0.0, minimum=-2.0, maximum=2.0, step=0.1)
+        with gr.Row():
+            # Slider for setting the seed for reproducibility
+            seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=65535, step=1)
 
+    # Tab to provide information to the user
     with gr.Tab("Information"):
-        with gr.Accordion("Featured Models", open=False):
-            gr.HTML("""
-            <p><a href="https://huggingface.co/models?pipeline_tag=text-generation&sort=trending">See all available models</a></p>
+        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):
+            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>Parameters</th>
+                    <th>Typography</th>
                     <th>Notes</th>
                 </tr>
                 <tr>
-                    <td>Llama-2-70b-chat</td>
-                    <td>70B</td>
-                    <td>Meta's largest chat model</td>
+                    <td>meta-llama/Llama-3.3-70B-Instruct</td>
+                    <td>✅</td>
+                    <td></td>
+                </tr>
+                <tr>
+                    <td>meta-llama/Llama-3.3-13B-Instruct</td>
+                    <td>✅</td>
+                    <td></td>
                 </tr>
                 <tr>
-                    <td>Mixtral-8x7B</td>
-                    <td>47B</td>
-                    <td>Mixture of Experts architecture</td>
+                    <td>meta-llama/Llama-3.3-30B-Instruct</td>
+                    <td>✅</td>
+                    <td></td>
                 </tr>
                 <tr>
-                    <td>Mistral-7B</td>
-                    <td>7B</td>
-                    <td>Efficient base model</td>
+                    <td>meta-llama/Llama-3.3-7B-Instruct</td>
+                    <td>✅</td>
+                    <td></td>
                 </tr>
             </table>
-            """)
+            """
+            )
 
+        # Accordion providing an overview of advanced settings
         with gr.Accordion("Parameters Overview", open=False):
-            gr.Markdown("""
-            ## System Message
-            The system message sets the context and behavior for the AI assistant. It's like giving it a role or specific instructions.
-
+            gr.Markdown(
+            """
             ## Max New Tokens
-            Controls the maximum length of the generated response. Higher values allow for longer responses but take more time.
+            ###### This slider allows you to specify the maximum number of tokens to generate in the response. The default value is 512, and the maximum output is 4096.
 
             ## Temperature
-            Controls randomness in the response:
-            - Lower (0.1-0.5): More focused and deterministic
-            - Higher (0.7-1.0): More creative and varied
+            ###### The temperature controls the randomness of the output. A higher temperature makes the output more random, while a lower temperature makes it more deterministic. The default value is 0.7.
 
             ## Top-P
-            Nucleus sampling parameter:
-            - Lower values: More focused on likely tokens
-            - Higher values: More diverse vocabulary usage
+            ###### Top-P (nucleus) sampling is a way to control the diversity of the output. A higher value allows for more diverse outputs, while a lower value makes the output more focused. The default value is 0.95.
 
             ## Frequency Penalty
-            Discourages repetition:
-            - Negative: May allow more repetition
-            - Positive: Encourages more diverse word choice
+            ###### The frequency penalty penalizes repeated tokens in the output. A higher value makes the output more diverse, while a lower value allows for more repetition. The default value is 0.0.
 
             ## Seed
-            Controls randomness initialization:
-            - -1: Random seed each time
-            - Fixed value: Reproducible outputs
-            """)
-
-    # Function to filter models based on search
-    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)
-
-    # Connect the search box to the model filter function
-    model_search.change(filter_models, inputs=model_search, outputs=model)
-
-    # Create the chat interface
-    chat_interface = gr.ChatInterface(
-        respond,
-        additional_inputs=[
-            model,
-            custom_model,
-            system_message,
-            max_tokens,
-            temperature,
-            top_p,
-            frequency_penalty,
-            seed,
-        ],
-        chatbot=chatbot,
-    )
+            ###### The seed is a fixed value for reproducibility. If you find a seed that gives you a result you love, you can use it again to create a similar output. If you leave it at -1, the AI will generate a new seed every time.
 
-print("Gradio interface initialized.")
+            ### 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!
+            """
+            )
+
+    # 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)
 
-if __name__ == "__main__":
-    print("Launching the demo application.")
-    demo.launch(show_api=False, share=False)
+print("Launching Gradio interface...")  # Debug log
+# Launch the Gradio interface without showing the API or sharing externally
+textgen.launch(show_api=False, share=False)