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import gradio as gr |
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from sentence_transformers import SentenceTransformer, util |
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import pandas as pd |
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import torch |
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model = SentenceTransformer("yazied49/NAdine3") |
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df = pd.read_csv("final_special_needs_qa.csv") |
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questions = df["question"].tolist() |
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answers = df["answer"].tolist() |
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question_embeddings = model.encode(questions, convert_to_tensor=True) |
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def get_answer(user_input): |
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input_embedding = model.encode(user_input, convert_to_tensor=True) |
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cos_scores = util.pytorch_cos_sim(input_embedding, question_embeddings)[0] |
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best_match_idx = torch.argmax(cos_scores).item() |
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return answers[best_match_idx] |
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iface = gr.Interface( |
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fn=get_answer, |
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inputs=gr.Textbox(lines=2, placeholder="اكتب سؤالك هنا..."), |
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outputs="text", |
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title="المساعد الطبي لذوي الاحتياجات الخاصة", |
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description="اسأل أي سؤال متعلق بذوي الاحتياجات الخاصة وسنحاول مساعدتك." |
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) |
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iface.launch() |
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