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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+combined_qa_data.csv filter=lfs diff=lfs merge=lfs -text

app (1).py

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+import gradio as gr
+import pandas as pd
+import torch
+from sentence_transformers import SentenceTransformer, util
+
+# تحميل الداتا
+df = pd.read_csv("cleaned_questions.csv", encoding='ISO-8859-1')
+questions = df['question'].tolist()
+answers = df['answer'].tolist()
+
+# تحميل موديل التمثيل العددي
+model = SentenceTransformer('all-MiniLM-L6-v2')
+embeddings = model.encode(questions, convert_to_tensor=True)
+
+# دالة البحث
+def get_answer(user_question, threshold=0.75):
+    if not user_question.strip():
+        return "من فضلك أدخل سؤالًا."
+
+    question_embedding = model.encode(user_question, convert_to_tensor=True)
+    scores = util.pytorch_cos_sim(question_embedding, embeddings)
+    top_idx = torch.argmax(scores).item()
+    top_score = scores[0][top_idx].item()
+
+    if top_score >= threshold:
+        return answers[top_idx]
+    else:
+        return "Sorry, I couldn't find a suitable answer to your question. Please try rephrasing it."
+
+# Gradio واجهة
+iface = gr.Interface(
+    fn=get_answer,
+    inputs=gr.Textbox(lines=2, placeholder="اكتب سؤالك هنا..."),
+    outputs="text",
+    title="الرد على الأسئلة الشائعة",
+    description="أدخل سؤالاً وسأبحث عن أقرب إجابة من قاعدة البيانات."
+)
+
+if __name__ == "__main__":
+    iface.launch()

combined_qa_data.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3bb286d8da2db94a28695ca2c4a457584e97f16951bb48310c68da2fca1dad3b
+size 22954940

requirements (1).txt

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+streamlit
+sentence-transformers
+pandas
+torch
+gtts
+deep-translator
+gdown
+transformers
+sacremoses
+gradio
+
+
+
+