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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()
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