Delete app.py

app.py

@@ -1,118 +0,0 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-# Define the InferenceClient for different models
-client_chatgpt = InferenceClient("openai/gpt-3.5-turbo")  # Example for ChatGPT model
-client_llama = InferenceClient("meta-llama/Llama-3.2-3B-Instruct")  # Llama model
-client_claude = InferenceClient("anthropic/claude-1")  # Claude model (adjust with correct model path)
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client_llama.chat_completion(  # Defaulting to Llama, update dynamically later
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-# Function to handle button clicks for different models
-def on_button_click(model_name, message, history, system_message, max_tokens, temperature, top_p):
-    # Choose the client based on the selected model
-    if model_name == "Chatgpt":
-        client = client_chatgpt
-    elif model_name == "Llama":
-        client = client_llama
-    elif model_name == "Claude":
-        client = client_claude
-    else:
-        return "Unknown model selected."
-
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    # Call the selected model for completion
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-        response += token
-        yield response
-
-# CSS for styling the interface
-css = """
-body {
-    background-color: #06688E; /* Dark background */
-    color: white; /* Text color for better visibility */
-}
-.gr-button {
-    background-color: #42B3CE !important; /* White button color */
-    color: black !important; /* Black text for contrast */
-    border: none !important;
-    padding: 8px 16px !important;
-    border-radius: 5px !important;
-}
-.gr-button:hover {
-    background-color: #e0e0e0 !important; /* Slightly lighter button on hover */
-}
-.gr-slider-container {
-    color: white !important; /* Slider labels in white */
-}
-"""
-
-# Interface using Blocks context
-with gr.Blocks() as demo:
-    # Add all your components here, including buttons
-    system_message = gr.Textbox(value="You are a virtual health assistant...", label="System message", visible=False)
-    message_input = gr.Textbox(label="User message")
-    history = gr.State([])  # Keep the history of interactions
-    max_tokens_slider = gr.Slider(minimum=1, maximum=2048, 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 (nucleus sampling)")
-
-    # Buttons to select the model
-    gr.Button("Chatgpt").click(on_button_click, inputs=[message_input, history, system_message, max_tokens_slider, temperature_slider, top_p_slider], outputs="text")
-    gr.Button("Llama").click(on_button_click, inputs=[message_input, history, system_message, max_tokens_slider, temperature_slider, top_p_slider], outputs="text")
-    gr.Button("Claude").click(on_button_click, inputs=[message_input, history, system_message, max_tokens_slider, temperature_slider, top_p_slider], outputs="text")
-    
-    # Optional: customize your layout with CSS if needed
-    demo.css = css
-
-# Launch the app
-if __name__ == "__main__":
-    demo.launch(share=True)