Update app.py

app.py

@@ -1,163 +1,3 @@
 import gradio as gr
-import spaces
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
 
-# Load the model and tokenizer
-model_name = "manjunathshiva/ICD11-Clinical-Terminology-16bit"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype=torch.bfloat16,
-    device_map="auto"
-)
-
-@spaces.GPU(duration=120)
-def generate_text(prompt, max_length, temperature):
-    messages = [
-        {"role": "system", "content": "You are a helpful assistant."},
-        {"role": "user", "content": prompt}
-    ]
-    formatted_prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=False, tokenize=False)
-
-    inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device)
-
-    outputs = model.generate(
-        **inputs,
-        max_new_tokens=max_length,
-        do_sample=True,
-        temperature=temperature,
-        top_k=100,
-        top_p=0.95,
-    )
-
-    return tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
-
-
-# Custom CSS
-css = """
-body {
-    background-color: #1a1a2e;
-    color: #e0e0e0;
-    font-family: 'Arial', sans-serif;
-}
-.container {
-    max-width: 900px;
-    margin: auto;
-    padding: 20px;
-}
-.gradio-container {
-    background-color: #16213e;
-    border-radius: 15px;
-    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
-}
-.header {
-    background-color: #0f3460;
-    padding: 20px;
-    border-radius: 15px 15px 0 0;
-    text-align: center;
-    margin-bottom: 20px;
-}
-.header h1 {
-    color: #e94560;
-    font-size: 2.5em;
-    margin-bottom: 10px;
-}
-.header p {
-    color: #a0a0a0;
-}
-.header img {
-    max-width: 300px;
-    border-radius: 10px;
-    margin: 15px auto;
-    display: block;
-}
-.input-group, .output-group {
-    background-color: #1a1a2e;
-    padding: 20px;
-    border-radius: 10px;
-    margin-bottom: 20px;
-}
-.input-group label, .output-group label {
-    color: #e94560;
-    font-weight: bold;
-}
-.generate-btn {
-    background-color: #e94560 !important;
-    color: white !important;
-    border: none !important;
-    border-radius: 5px !important;
-    padding: 10px 20px !important;
-    font-size: 16px !important;
-    cursor: pointer !important;
-    transition: background-color 0.3s ease !important;
-}
-.generate-btn:hover {
-    background-color: #c81e45 !important;
-}
-.example-prompts {
-    background-color: #1f2b47;
-    padding: 15px;
-    border-radius: 10px;
-    margin-bottom: 20px;
-}
-.example-prompts h3 {
-    color: #e94560;
-    margin-bottom: 10px;
-}
-.example-prompts ul {
-    list-style-type: none;
-    padding-left: 0;
-}
-.example-prompts li {
-    margin-bottom: 5px;
-    cursor: pointer;
-    transition: color 0.3s ease;
-}
-.example-prompts li:hover {
-    color: #e94560;
-}
-"""
-
-# Example prompts
-example_prompts = [
-    "What is the code for Coronary microvascular disease ?",
-    "What is the ICD 11 code for Diseases of coronary artery, unspecified ?",
-    "The code is BA85.0. What does it mean?",
-]
-
-# Gradio interface
-# Gradio interface
-with gr.Blocks(css=css) as iface:
-    gr.HTML(
-        """
-        <div class="header">
-            <h1>manjunathshiva/ICD11-Clinical-Terminology-16bit</h1>
-            <p>Generate ICD 11 code using the fintetuned Llama3.1 8B model : manjunathshiva/ICD11-Clinical-Terminology-16bit model. Enter a prompt and let the AI create!</p>
-            <img src="https://cdn-uploads.huggingface.co/production/uploads/64c75c1237333ccfef30a602/tmOlbERGKP7JSODa6T06J.jpeg" alt="Llama">
-        </div>
-        """
-    )
-
-    with gr.Group():
-        with gr.Group(elem_classes="example-prompts"):
-            gr.HTML("<h3>Example Prompts:</h3>")
-            example_buttons = [gr.Button(prompt) for prompt in example_prompts]
-
-        with gr.Group(elem_classes="input-group"):
-            prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...", lines=5)
-            max_length = gr.Slider(minimum=1, maximum=500, value=128, step=1, label="Max Length")
-            temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature")
-            generate_btn = gr.Button("Generate", elem_classes="generate-btn")
-
-        with gr.Group(elem_classes="output-group"):
-            output = gr.Textbox(label="Generated Text", lines=10)
-
-    generate_btn.click(generate_text, inputs=[prompt, max_length, temperature], outputs=output)
-
-    # Set up example prompt buttons
-    for button in example_buttons:
-        button.click(lambda x: x, inputs=[button], outputs=[prompt])
-
-# Launch the app
-iface.launch()
+gr.load("models/manjunathshiva/ICD11-Clinical-Terminology-16bit").launch()