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import gradio as gr
from sentence_transformers import SentenceTransformer, util
import pandas as pd
import torch
# تحميل الموديل من Hugging Face
model = SentenceTransformer("yazied49/NAdine3")
# تحميل البيانات
df = pd.read_csv("final_special_needs_qa.csv") # استخدم CSV بدلًا من Excel
questions = df["question"].tolist()
answers = df["answer"].tolist()
question_embeddings = model.encode(questions, convert_to_tensor=True)
# الردود الاجتماعية الجاهزة
greetings = {
"هاي": "أهلاً وسهلاً! 😊 إزاي أقدر أساعدك؟",
"ازيك": "أنا تمام! شكرًا لسؤالك. عندك أي سؤال متعلق بذوي الاحتياجات الخاصة؟",
"السلام عليكم": "وعليكم السلام ورحمة الله وبركاته!",
"شكرا": "العفو! أنا دايمًا هنا للمساعدة 😊",
"thanks": "You're welcome! 💙",
"hi": "Hi there! How can I help you?",
"hello": "Hello! Feel free to ask anything.",
"merci": "على الرحب والسعة!",
}
def get_answer(user_input):
user_input_lower = user_input.lower().strip()
# الردود الاجتماعية
for key in greetings:
if key in user_input_lower:
return greetings[key]
# التحقق من أقرب سؤال
input_embedding = model.encode(user_input, convert_to_tensor=True)
cos_scores = util.pytorch_cos_sim(input_embedding, question_embeddings)[0]
best_match_idx = torch.argmax(cos_scores).item()
best_score = cos_scores[best_match_idx].item()
if best_score < 0.4:
return "Sorry, I didn't understand your question. Can you please rephrase? 🤔"
return answers[best_match_idx]
# واجهة Gradio
iface = gr.Interface(
fn=get_answer,
inputs=gr.Textbox(lines=2, placeholder="Type your question here..."),
outputs="text",
title="🤖 Special Needs Medical Assistant",
description="Ask any question related to special needs and we'll try to help you"
)
iface.launch()
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