| import gradio as gr |
| from huggingface_hub import InferenceClient |
| from model import load_model, load_tokenizer |
| from utils import clean_output, get_shap_values |
| import torch |
| import shap |
| import matplotlib.pyplot as plt |
|
|
| """ |
| For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference |
| """ |
| client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") |
|
|
| model = load_model() |
| tokenizer = load_tokenizer() |
|
|
| def gradio_generate(context, num_questions, max_length): |
| input_prompt = f"generate question: {context.strip()}" |
| inputs = tokenizer(input_prompt, return_tensors="pt", truncation=True, padding="longest").to(model.device) |
| outputs = model.generate( |
| input_ids=inputs["input_ids"], |
| attention_mask=inputs["attention_mask"], |
| max_length=max_length, |
| num_return_sequences=num_questions, |
| do_sample=True, |
| top_p=0.95, |
| temperature=1.0 |
| ) |
| decoded = tokenizer.batch_decode(outputs, skip_special_tokens=True) |
| questions = clean_output(decoded) |
| return "\n".join([f"{i+1}. {q}" for i, q in enumerate(questions)]) |
|
|
| def gradio_shap(context): |
| input_prompt = f"generate question: {context.strip()}" |
| try: |
| shap_values, tokens = get_shap_values(tokenizer, model, input_prompt) |
| fig, ax = plt.subplots(figsize=(10, 2)) |
| shap.plots.text(shap.Explanation(values=shap_values, data=tokens), display=False) |
| plt.tight_layout() |
| return fig |
| except Exception as e: |
| fig, ax = plt.subplots(figsize=(8, 2)) |
| ax.text(0.5, 0.5, f"SHAP explanation failed:\n{e}", ha='center', va='center', wrap=True, fontsize=12) |
| ax.axis('off') |
| return fig |
|
|
| with gr.Blocks() as demo: |
| gr.Markdown("# 🧠 C3QG – Context-Controlled Question Generation with FLAN-T5") |
| context = gr.Textbox(label="📄 Paste your context paragraph here:", lines=6) |
| num_questions = gr.Slider(1, 5, value=3, label="Number of Questions") |
| max_length = gr.Slider(32, 128, value=64, label="Max Output Tokens") |
| generate_btn = gr.Button("🔄 Generate Questions") |
| questions_output = gr.Textbox(label="🎯 Generated Questions") |
| shap_btn = gr.Button("Show SHAP Explanation") |
| shap_output = gr.Plot(label="SHAP Token Importance") |
|
|
| generate_btn.click( |
| gradio_generate, |
| inputs=[context, num_questions, max_length], |
| outputs=questions_output |
| ) |
| if __name__ == "__main__": |
| demo.launch() |
|
|
