app.py

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from huggingface_hub import snapshot_download

# 🔹 Download & load the model from Hugging Face
model_name = "HyperX-Sen/Qwen-2.5-7B-Reasoning"
model_path = snapshot_download(repo_id=model_name, repo_type="model")

# 🔹 Load the model & tokenizer
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_path)

# 🔹 System prompt
SYSTEM_PROMPT = """
Respond in the following format:
<reasoning>
...
</reasoning>
<answer>
...
</answer>
"""

# 🔹 Function to generate response
def chat_response(user_input, top_p, top_k, temperature, max_length):
    messages = [
        {"role": "system", "content": f"{SYSTEM_PROMPT}"},
        {"role": "user", "content": user_input}
    ]

    # 🔹 Format & tokenize input
    input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

    # 🔹 Generate response
    with torch.no_grad():
        output = model.generate(
            **inputs,
            max_length=max_length,
            do_sample=True,
            top_p=top_p,
            top_k=top_k,
            temperature=temperature
        )

    # 🔹 Decode output
    response = tokenizer.decode(output[0], skip_special_tokens=True)
    return response

# 🔹 Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("# 🤖 Qwen-2.5-7B-Reasoning Chatbot")

    with gr.Row():
        chatbot = gr.Textbox(label="Model Response", lines=8, interactive=False)
    
    with gr.Row():
        user_input = gr.Textbox(label="Your Prompt", placeholder="Ask me anything...", lines=2)
    
    with gr.Accordion("🔧 Advanced Settings", open=False):
        top_p = gr.Slider(0.1, 1.0, value=0.9, label="Top-p")
        top_k = gr.Slider(1, 100, value=50, label="Top-k")
        temperature = gr.Slider(0.1, 1.5, value=0.7, label="Temperature")
        max_length = gr.Slider(128, 1024, value=512, label="Max Length")

    with gr.Row():
        submit_button = gr.Button("Generate Response")

    submit_button.click(chat_response, inputs=[user_input, top_p, top_k, temperature, max_length], outputs=[chatbot])

# 🔹 Launch the Gradio app
demo.launch()