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import gradio as gr | |
from huggingface_hub import InferenceClient | |
# Initialize your REAL AI model (with Hugging Face API token) | |
def get_client(): | |
api_token = "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" | |
return InferenceClient(token=api_token) | |
model_name = "HuggingFaceH4/zephyr-7b-beta" | |
# Function to generate AI-driven response | |
def generate_response(prompt, max_length, temperature, repetition_penalty): | |
client = get_client() | |
response = client.text_generation( | |
prompt, | |
model=model_name, | |
max_new_tokens=max_length, | |
temperature=temperature, | |
repetition_penalty=repetition_penalty, | |
do_sample=True, | |
) | |
return response | |
# Enhanced UI with Gradio | |
demo = gr.Interface( | |
fn=generate_response, | |
inputs=[ | |
gr.Textbox(lines=4, label="Enter your prompt", placeholder="Provide clear instructions or questions to avoid repetitive outputs."), | |
gr.Slider(50, 500, value=200, step=50, label="Response Length"), | |
gr.Slider(0.1, 1.0, value=0.7, step=0.1, label="Creativity (temperature)"), | |
gr.Slider(1.0, 2.0, value=1.1, step=0.1, label="Repetition Penalty") | |
], | |
outputs=gr.Textbox(label="AI Response"), | |
title="AI Assistant", | |
description="Provide your prompt below and get a dynamic, well-structured AI-generated response." | |
) | |
# Launch app | |
demo.launch() | |