Delete app.py

app.py

@@ -1,97 +0,0 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-# Create an InferenceClient to interact with the model
-client = InferenceClient("meta-llama/Llama-3.2-3B-Instruct")
-
-# Define the function to generate a response
-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.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
-
-# Custom CSS for styling
-css = """
-body {
-    font-family: 'Arial', sans-serif;
-    background-color: #f8f9fa; /* Light background */
-    color: #333;
-}
-.gr-button {
-    background-color: #0b2545 !important;
-    color: white !important;
-    border: none !important;
-    border-radius: 25px !important;
-    padding: 8px 20px !important;
-    font-size: 14px;
-    font-weight: bold;
-    cursor: pointer;
-}
-.gr-button:hover {
-    background-color: #0a1b35 !important;
-}
-.search-box {
-    border-radius: 20px;
-    border: 1px solid #ccc;
-    padding: 10px;
-    width: 100%;
-    font-size: 16px;
-    background-color: #ffffff;
-}
-"""
-
-# Main function to create the interface
-with gr.Blocks(css=css) as demo:
-    gr.Markdown("<h1 style='text-align: center;'>Health Assistant GPT</h1>")
-    gr.Markdown("<h3 style='text-align: center;'>What do you want to know about health and wellness?</h3>")
-
-    # Sidebar
-    with gr.Sidebar():
-        gr.Markdown("### Settings")
-        system_message = gr.Textbox(
-            value="You are a virtual health assistant designed to provide accurate and reliable information related to health, wellness, and medical topics. Your primary goal is to assist users with their health-related queries, offer general guidance, and suggest when to consult a licensed medical professional. If a user asks a question that is unrelated to health, wellness, or medical topics, respond politely but firmly.",
-            label="System message",
-            visible=False
-        )
-        max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens", visible=False)
-        temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature", visible=False)
-        top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)", visible=False)
-
-    # Main content
-    with gr.Row():
-        with gr.Column(scale=7):
-            gr.Markdown("### Ask a health-related question:")
-            search_input = gr.Textbox(label="Search Input", placeholder="Type your health-related question here...", lines=1)
-            submit_button = gr.Button("Generate Response")
-            output = gr.Markdown()
-
-        with gr.Column(scale=3):
-            gr.Markdown("### Upload a relevant file (Optional):")
-            uploaded_file = gr.File(label="Upload PDF")
-
-    # Button click action to trigger response generation
-    submit_button.click(
-        fn=respond,
-        inputs=[search_input, [], system_message, max_tokens, temperature, top_p],  # Empty history for fresh chat
-        outputs=output
-    )
-
-demo.launch()