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Update app.py
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app.py
CHANGED
@@ -2,6 +2,9 @@ import gradio as gr
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from transformers import pipeline
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import PyPDF2
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import markdown
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# Preload models
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models = {
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@@ -19,6 +22,23 @@ def load_model(model_name):
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loaded_models[model_name] = pipeline("question-answering", model=models[model_name])
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return loaded_models[model_name]
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def answer_question(model_name, file, question, status):
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status = "Loading model..."
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model = load_model(model_name)
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@@ -42,11 +62,17 @@ def answer_question(model_name, file, question, status):
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answer = result['answer']
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score = result['score']
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# Explain score
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score_explanation = f"The confidence score ranges from 0 to 1, where a higher score indicates higher confidence in the answer's correctness. In this case, the score is {score:.2f}. A score closer to 1 implies the model is very confident about the answer."
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status = "Model loaded"
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return answer, f"{score:.2f}", score_explanation, status
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# Define the Gradio interface
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with gr.Blocks() as interface:
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@@ -73,6 +99,8 @@ with gr.Blocks() as interface:
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answer_output = gr.Textbox(label="Answer")
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score_output = gr.Textbox(label="Confidence Score")
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explanation_output = gr.Textbox(label="Score Explanation")
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with gr.Row():
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submit_button = gr.Button("Submit")
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@@ -85,7 +113,7 @@ with gr.Blocks() as interface:
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submit_button.click(
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on_submit,
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inputs=[model_dropdown, file_input, question_input],
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outputs=[answer_output, score_output, explanation_output, status_output]
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)
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if __name__ == "__main__":
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from transformers import pipeline
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import PyPDF2
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import markdown
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import matplotlib.pyplot as plt
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import io
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import base64
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# Preload models
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models = {
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loaded_models[model_name] = pipeline("question-answering", model=models[model_name])
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return loaded_models[model_name]
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def generate_score_chart(score):
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plt.figure(figsize=(6, 4))
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plt.bar(["Confidence Score"], [score], color='skyblue')
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plt.ylim(0, 1)
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plt.ylabel("Score")
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plt.title("Confidence Score")
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buf = io.BytesIO()
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plt.savefig(buf, format='png')
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plt.close()
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buf.seek(0)
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return base64.b64encode(buf.getvalue()).decode()
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def generate_report(answer, score, score_explanation, score_chart):
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report = f"### Answer:\n\n{answer}\n\n### Confidence Score: {score}\n\n### Score Explanation:\n\n{score_explanation}\n\n![Score Chart](data:image/png;base64,{score_chart})"
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return report
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def answer_question(model_name, file, question, status):
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status = "Loading model..."
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model = load_model(model_name)
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answer = result['answer']
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score = result['score']
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# Generate the score chart
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score_chart = generate_score_chart(score)
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# Explain score
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score_explanation = f"The confidence score ranges from 0 to 1, where a higher score indicates higher confidence in the answer's correctness. In this case, the score is {score:.2f}. A score closer to 1 implies the model is very confident about the answer."
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# Generate the report
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report = generate_report(answer, f"{score:.2f}", score_explanation, score_chart)
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status = "Model loaded"
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return answer, f"{score:.2f}", score_explanation, score_chart, report, status
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# Define the Gradio interface
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with gr.Blocks() as interface:
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answer_output = gr.Textbox(label="Answer")
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score_output = gr.Textbox(label="Confidence Score")
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explanation_output = gr.Textbox(label="Score Explanation")
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chart_output = gr.Image(label="Score Chart")
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report_output = gr.Markdown(label="Report")
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with gr.Row():
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submit_button = gr.Button("Submit")
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submit_button.click(
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on_submit,
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inputs=[model_dropdown, file_input, question_input],
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outputs=[answer_output, score_output, explanation_output, chart_output, report_output, status_output]
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)
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if __name__ == "__main__":
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