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import streamlit as st |
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from transformers import AutoModelForQuestionAnswering, AutoTokenizer |
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def main(): |
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custom_css = """ |
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<style> |
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body { |
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font-family: 'Arial', sans-serif; |
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background-color: #FF0000; /* Set your desired background color here */ |
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} |
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.title { |
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font-size: 2.5em; |
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color: #333; |
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text-align: center; |
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padding: 1em; |
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background-color: #3498db; |
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color: #fff; |
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border-radius: 10px; |
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margin-bottom: 1em; |
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} |
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.input-container { |
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margin: 2em; |
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} |
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.button-container { |
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text-align: center; |
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} |
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.result-container { |
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margin: 2em; |
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padding: 1em; |
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background-color: #fff; |
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border-radius: 10px; |
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box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); |
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} |
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.answer { |
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background-color: #3498db; |
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padding: 10px; |
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border-radius: 5px; |
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margin-top: 10px; |
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} |
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img { |
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max-width: 100%; |
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height: auto; |
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border-radius: 10px; |
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box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); |
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} |
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</style> |
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""" |
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st.markdown(custom_css, unsafe_allow_html=True) |
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st.markdown("<div class='title'>Question Answering with Transformers</div>", unsafe_allow_html=True) |
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model_name = st.selectbox("Select Model", ["deepset/roberta-base-squad2", "bert-large-uncased-whole-word-masking-finetuned-squad", "distilbert-base-cased-distilled-squad", |
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"bert-base-uncased", |
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"albert-base-v2"]) |
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model = AutoModelForQuestionAnswering.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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st.markdown("<div class='input-container'>", unsafe_allow_html=True) |
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context = st.text_area("Enter the context (max 400 words):") |
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question = st.text_input("Enter your question:") |
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st.markdown("</div>", unsafe_allow_html=True) |
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if st.button("Get Answer"): |
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if not question or not context: |
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st.warning("Please enter both a question and a context.") |
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else: |
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try: |
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if len(context.split()) > 400: |
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raise ValueError("Context exceeds 400 words limit.") |
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inputs = tokenizer.encode_plus(question, context, return_tensors='pt') |
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outputs = model(**inputs) |
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start_logits = outputs.start_logits |
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end_logits = outputs.end_logits |
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top_n = 3 |
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start_indexes = start_logits.argsort(dim=1, descending=True)[:, :top_n] |
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end_indexes = end_logits.argsort(dim=1, descending=True)[:, :top_n] |
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st.subheader(f"Question: {question}") |
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for i in range(top_n): |
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start_index = start_indexes[0, i].item() |
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end_index = end_indexes[0, i].item() |
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answer = tokenizer.decode(inputs['input_ids'][0, start_index:end_index + 1]) |
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highlighted_context = f"{context[:start_index]}**{context[start_index:end_index+1]}**{context[end_index+1:]}" |
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confidence_start = start_logits[0, start_index].item() |
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confidence_end = end_logits[0, end_index].item() |
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if answer == "": |
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continue |
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else: |
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st.markdown(f"<div class='answer'><strong>Answer:</strong> {answer}<br>" |
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f"<strong>Confidence (Start):</strong> {confidence_start:.4f}<br>" |
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f"<strong>Confidence (End):</strong> {confidence_end:.4f}</div>", unsafe_allow_html=True) |
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except ValueError as ve: |
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st.error(str(ve)) |
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except Exception as e: |
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st.error(f"An error occurred: {e}") |
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st.markdown("</div>", unsafe_allow_html=True) |
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if __name__ == "__main__": |
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main() |
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