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Update app.py
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app.py
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@@ -3,11 +3,12 @@ import gradio as gr
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from transformers import T5ForConditionalGeneration, T5Tokenizer, pipeline
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# -----------------------------
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# 1. Load
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#
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# -----------------------------
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tokenizer = T5Tokenizer.from_pretrained("valhalla/t5-
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model = T5ForConditionalGeneration.from_pretrained("valhalla/t5-
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qg_pipeline = pipeline(
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"text2text-generation",
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model=model,
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@@ -108,16 +109,39 @@ def check_answer(state, user_answer):
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return result_text + "\n" + difficulty_update
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# -----------------------------
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# 7. Build the Gradio Interface
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# -----------------------------
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# Persistent state for the session.
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state = gr.State(init_state())
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gr.Markdown("# Adaptive Language Tutor")
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gr.Markdown(
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"This demo uses a T5-based model to generate questions from a passage. "
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"
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)
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# Display current difficulty and score.
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from transformers import T5ForConditionalGeneration, T5Tokenizer, pipeline
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# -----------------------------
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# 1. Load a Better Model & Slow Tokenizer
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# Using the "valhalla/t5-base-qg-hl" model (a step up from the small version)
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# and disabling the fast tokenizer.
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# -----------------------------
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tokenizer = T5Tokenizer.from_pretrained("valhalla/t5-base-qg-hl", use_fast=False)
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model = T5ForConditionalGeneration.from_pretrained("valhalla/t5-base-qg-hl")
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qg_pipeline = pipeline(
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"text2text-generation",
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model=model,
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return result_text + "\n" + difficulty_update
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# -----------------------------
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# 7. Build the Gradio Interface with Custom CSS for a More Colorful UI
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# -----------------------------
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custom_css = """
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body {
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background: linear-gradient(135deg, #e0c3fc, #8ec5fc);
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font-family: 'Helvetica', sans-serif;
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}
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.gradio-container {
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background-color: #ffffff;
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border-radius: 15px;
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padding: 20px;
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box-shadow: 0 4px 12px rgba(0,0,0,0.15);
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}
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.gradio-markdown h1 {
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color: #4b0082;
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}
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.gradio-markdown p, .gradio-markdown li {
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font-size: 16px;
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color: #333333;
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}
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.gradio-button, .gradio-textbox {
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font-size: 16px;
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}
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"""
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with gr.Blocks(css=custom_css) as demo:
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# Persistent state for the session.
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state = gr.State(init_state())
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gr.Markdown("# Adaptive Language Tutor")
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gr.Markdown(
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"This demo uses a T5-based model to generate questions from a passage. "
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"The difficulty will automatically adjust based on your performance."
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)
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# Display current difficulty and score.
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