Update app.py

app.py

@@ -14,297 +14,145 @@ client = OpenAI(
 print("OpenAI client initialized.")
 
 def respond(
-    user_message,
-    chat_history,
-    system_msg,
+    message,
+    history: list[tuple[str, str]],
+    system_message,
     max_tokens,
     temperature,
     top_p,
     frequency_penalty,
     seed,
-    featured_model,
-    custom_model
+    custom_model,
+    selected_model
 ):
     """
-    This function handles the chatbot response. It takes in:
-    - user_message: the user's newly typed message
-    - chat_history: the list of (user, assistant) message pairs
-    - system_msg: the system instruction or system-level context
-    - max_tokens: the maximum number of tokens to generate
-    - temperature: sampling temperature
-    - top_p: top-p (nucleus) sampling
-    - frequency_penalty: penalize repeated tokens in the output
-    - seed: a fixed seed for reproducibility; -1 means 'random'
-    - featured_model: the chosen model name from 'Featured Models' radio
-    - custom_model: the optional custom model that overrides the featured one if provided
+    Handles the chatbot response generation.
     """
-
-    print(f"Received user message: {user_message}")
-    print(f"System message: {system_msg}")
-    print(f"Max tokens: {max_tokens}, Temperature: {temperature}, Top-P: {top_p}, Freq-Penalty: {frequency_penalty}, Seed: {seed}")
-    print(f"Featured model: {featured_model}")
+    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"Custom model: {custom_model}")
+    print(f"Selected model: {selected_model}")
 
-    # Convert the seed to None if user set it to -1 (meaning random)
+    # Convert seed to None if -1 (meaning random)
     if seed == -1:
         seed = None
 
-    # Decide which model to actually use
-    # If custom_model is non-empty, use that; otherwise use the chosen featured_model
-    model_to_use = custom_model.strip() if custom_model.strip() != "" else featured_model
-    # Provide a default fallback if for some reason both are empty
-    if model_to_use.strip() == "":
-        model_to_use = "meta-llama/Llama-3.3-70B-Instruct"
-
+    # Construct the messages array required by the API
+    messages = [{"role": "system", "content": system_message}]
+
+    # 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 the latest user message
+    messages.append({"role": "user", "content": message})
+
+    # Determine which model to use
+    model_to_use = (
+        custom_model.strip()
+        if custom_model.strip() != ""
+        else selected_model.strip()
+    )
     print(f"Model selected for inference: {model_to_use}")
 
-    # Construct the conversation history in the format required by HF's Inference API
-    messages = []
-    if system_msg.strip():
-        messages.append({"role": "system", "content": system_msg.strip()})
-
-    # Add the conversation history
-    for user_text, assistant_text in chat_history:
-        if user_text:
-            messages.append({"role": "user", "content": user_text})
-        if assistant_text:
-            messages.append({"role": "assistant", "content": assistant_text})
-
-    # Add the new user message to the conversation
-    messages.append({"role": "user", "content": user_message})
-
-    # We'll build the response token-by-token in a streaming loop
-    response_so_far = ""
-    print("Sending request to the Hugging Face Inference API...")
-
-    # Make the streaming request to the HF Inference API
-    try:
-        for resp_chunk in client.chat.completions.create(
-            model=model_to_use,
-            max_tokens=max_tokens,
-            stream=True,
-            temperature=temperature,
-            top_p=top_p,
-            frequency_penalty=frequency_penalty,
-            seed=seed,
-            messages=messages,
-        ):
-            token_text = resp_chunk.choices[0].delta.content
-            response_so_far += token_text
-            # We yield back the updated message to display partial progress in the chatbot
-            yield response_so_far
-    except Exception as e:
-        # If there's an error, let's at least show it in the chat
-        error_text = f"[ERROR] {str(e)}"
-        print(error_text)
-        yield response_so_far + "\n\n" + error_text
+    # Start with an empty string to build the response as tokens stream in
+    response = ""
+    print("Sending request to OpenAI API.")
+
+    # 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,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+        frequency_penalty=frequency_penalty,
+        seed=seed,
+        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
+        yield response
 
     print("Completed response generation.")
 
-#
-# BUILDING THE GRADIO INTERFACE BELOW
-#
-
-# List of featured models; adjust or replace these placeholders with real text-generation models
+# Predefined list of placeholder models for the Featured Models accordion
 models_list = [
     "meta-llama/Llama-3.3-70B-Instruct",
-    "meta-llama/Llama-2-13B-chat-hf",
-    "bigscience/bloom",
-    "openlm-research/open_llama_7b",
-    "facebook/opt-6.7b",
-    "google/flan-t5-xxl",
+    "bigscience/bloom-7b1",
+    "EleutherAI/gpt-neo-2.7B",
+    "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
+    "HuggingFace/distilgpt2",
 ]
 
+# Function to filter models based on search input
 def filter_models(search_term):
-    """Filters the models_list by the given search_term and returns an update for the Radio component."""
-    filtered = [m for m in models_list if search_term.lower() in m.lower()]
-    return gr.update(choices=filtered)
-
-with gr.Blocks(theme="Nymbo/Nymbo_Theme_5") as demo:
-    gr.Markdown("# Serverless-TextGen-Hub (Enhanced)")
-    gr.Markdown("**A comprehensive UI for text generation with a featured-models dropdown and a custom override**.")
-
-    # We keep track of the conversation in a Gradio state variable (list of tuples)
-    chat_history = gr.State([])
-
-    # Tabs for organization
-    with gr.Tab("Basic Settings"):
-        with gr.Row():
-            with gr.Column(elem_id="prompt-container"):
-                # System Message
-                system_msg = gr.Textbox(
-                    label="System message",
-                    placeholder="Enter system-level instructions or context here.",
-                    lines=2
-                )
-                # Accordion for featured models
-                with gr.Accordion("Featured Models", open=True):
-                    model_search = gr.Textbox(
-                        label="Filter Models",
-                        placeholder="Search for a featured model...",
-                        lines=1
-                    )
-                    # The radio that lists our featured models
-                    model_radio = gr.Radio(
-                        label="Select a featured model below",
-                        choices=models_list,
-                        value=models_list[0],  # default
-                        interactive=True
-                    )
-                    # Link the search box to update the model_radio choices
-                    model_search.change(filter_models, inputs=model_search, outputs=model_radio)
-
-                # Custom Model
-                custom_model_box = gr.Textbox(
-                    label="Custom Model (Optional)",
-                    info="If provided, overrides the featured model above. e.g. 'meta-llama/Llama-3.3-70B-Instruct'",
-                    placeholder="Your huggingface.co/username/model_name path"
-                )
-
-    with gr.Tab("Advanced Settings"):
-        with gr.Row():
-            max_tokens_slider = gr.Slider(
-                minimum=1,
-                maximum=4096,
-                value=512,
-                step=1,
-                label="Max new tokens"
-            )
-            temperature_slider = gr.Slider(
-                minimum=0.1,
-                maximum=4.0,
-                value=0.7,
-                step=0.1,
-                label="Temperature"
-            )
-            top_p_slider = gr.Slider(
-                minimum=0.1,
-                maximum=1.0,
-                value=0.95,
-                step=0.05,
-                label="Top-P"
-            )
-        with gr.Row():
-            freq_penalty_slider = gr.Slider(
-                minimum=-2.0,
-                maximum=2.0,
-                value=0.0,
-                step=0.1,
-                label="Frequency Penalty"
+    filtered_models = [m for m in models_list if search_term.lower() in m.lower()]
+    return gr.update(choices=filtered_models)
+
+# Create a Chatbot component with a specified height
+chatbot = gr.Chatbot(height=600)
+print("Chatbot interface created.")
+
+# Create the Gradio ChatInterface
+# Added "Featured Models" accordion and integrated filtering
+demo = gr.Interface(
+    fn=respond,
+    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(
+            value="",
+            label="Custom Model",
+            info="(Optional) Provide a custom Hugging Face model path. This will override the default model if not empty.",
+        ),
+        # Add Featured Models accordion
+        gr.Accordion("Featured Models", open=True, children=[
+            gr.Textbox(label="Filter Models", placeholder="Search for a featured model...", lines=1).change(
+                filter_models, inputs=["value"], outputs="choices"
+            ),
+            gr.Radio(
+                label="Select a featured model",
+                value="meta-llama/Llama-3.3-70B-Instruct",
+                choices=models_list,
+                elem_id="model-radio",
             )
-            seed_slider = gr.Slider(
-                minimum=-1,
-                maximum=65535,
-                value=-1,
-                step=1,
-                label="Seed (-1 for random)"
-            )
-
-    # Chat interface area: user input -> assistant output
-    with gr.Row():
-        chatbot = gr.Chatbot(
-            label="TextGen Chat",
-            height=500
-        )
-
-    # The user types a message here
-    user_input = gr.Textbox(
-        label="Your message",
-        placeholder="Type your text prompt here..."
-    )
-
-    # "Send" button triggers our respond() function, updates the chatbot
-    send_button = gr.Button("Send")
-
-    # A Clear Chat button to reset the conversation
-    clear_button = gr.Button("Clear Chat")
-
-    # Define how the Send button updates the state and chatbot
-    def user_submission(user_text, history):
-        """
-        This function gets called first to add the user's message to the chat.
-        We return the updated chat_history with the user's message appended,
-        plus an empty string for the next user input box.
-        """
-        if user_text.strip() == "":
-            return history, ""
-        # Append user message to chat
-        history = history + [(user_text, None)]
-        return history, ""
-
-    send_button.click(
-        fn=user_submission,
-        inputs=[user_input, chat_history],
-        outputs=[chat_history, user_input]
-    )
-
-    # Then we run the respond function (streaming) to generate the assistant message
-    def bot_response(
-        history,
-        system_msg,
-        max_tokens,
-        temperature,
-        top_p,
-        freq_penalty,
-        seed,
-        featured_model,
-        custom_model
-    ):
-        """
-        This function is called to generate the assistant's response
-        based on the conversation so far, system message, etc. 
-        We do the streaming here.
-        """
-        if not history:
-            yield history
-        # The last user message is in history[-1][0]
-        user_message = history[-1][0] if history else ""
-        # We pass everything to respond() generator
-        bot_stream = respond(
-            user_message=user_message,
-            chat_history=history[:-1],  # all except the newly appended user message
-            system_msg=system_msg,
-            max_tokens=max_tokens,
-            temperature=temperature,
-            top_p=top_p,
-            frequency_penalty=freq_penalty,
-            seed=seed,
-            featured_model=featured_model,
-            custom_model=custom_model
-        )
-        partial_text = ""
-        for partial_text in bot_stream:
-            # We'll keep updating the last message in the conversation with partial_text
-            updated_history = history[:-1] + [(history[-1][0], partial_text)]
-            yield updated_history
-
-    send_button.click(
-        fn=bot_response,
-        inputs=[
-            chat_history,
-            system_msg,
-            max_tokens_slider,
-            temperature_slider,
-            top_p_slider,
-            freq_penalty_slider,
-            seed_slider,
-            model_radio,
-            custom_model_box
-        ],
-        outputs=chatbot
-    )
-
-    # Clear chat just resets the state
-    def clear_chat():
-        return [], ""
+        ]),
+    ],
+    outputs=gr.Chatbot(height=600),
+    theme="Nymbo/Nymbo_Theme",
+)
 
-    clear_button.click(
-        fn=clear_chat,
-        inputs=[],
-        outputs=[chat_history, user_input]
-    )
+print("Gradio interface initialized.")
 
-# Launch the application
 if __name__ == "__main__":
-    print("Launching the Serverless-TextGen-Hub with Featured Models & Custom Model override.")
+    print("Launching the demo application.")
     demo.launch()