Update main.py

main.py

@@ -1,116 +1,122 @@
-from flask import Flask, request, jsonify
-from sentence_transformers import SentenceTransformer
-from pinecone import Pinecone
-import google.generativeai as genai
-from langdetect import detect
-
-# تهيئة الموديلات
-embedding_model = SentenceTransformer("intfloat/multilingual-e5-large")
-
-pc = Pinecone(api_key="pcsk_3ax4D8_PH7vWF1KWAMRpyjmEnXhwxswmHSjvqgwovna3xGGbfsgZsMRtRyFi9uCpPyi4B9")
-index = pc.Index("newindex")
-
-genai.configure(api_key="AIzaSyBXtRzMkpm9RNDO09A9N3XoG_vfjgUe5Vw")
-model = genai.GenerativeModel("gemini-2.0-flash")
-
-app = Flask(__name__)
-chat_history = []
-
-def detect_language(text):
-    try:
-        return detect(text)
-    except:
-        return "unknown"
-
-def get_answer_from_pinecone(user_question, embedding_model, index, top_k=5, similarity_threshold=0.7):
-    try:
-        question_vector = embedding_model.encode(user_question).tolist()
-    except Exception as e:
-        return [f"❌ Error embedding question: {e}"]
-
-    try:
-        search_result = index.query(
-            vector=question_vector,
-            top_k=top_k,
-            include_metadata=True
-        )
-    except Exception as e:
-        return [f"❌ Error querying Pinecone: {e}"]
-
-    matches = [m for m in search_result.matches if m.score >= similarity_threshold]
-    sorted_matches = sorted(matches, key=lambda x: x.score, reverse=True)
-
-    answers = []
-    for m in sorted_matches:
-        answer = m.metadata.get('answer', '').strip()
-        source = m.metadata.get('source', 'unknown')
-        score = round(m.score, 3)
-        if answer:
-            answers.append(f"• ({score}) from [{source}]:\n{answer}")
-    return answers if answers else ["⚠️ No similar answers found."]
-
-def ask_gemini_with_combined_answer(user_question, pinecone_answers=[], history=[]):
-    context = "\n".join([f"👤 {q}\n🤖 {a}" for q, a in history])
-    extracted_info = "\n".join([f"• {ans}" for ans in pinecone_answers]) if pinecone_answers else "None"
-    lang = detect_language(user_question)
-
-    if lang == "ar":
-        greeting = "مرحبًا! 😊"
-        instructions = """
-❗ هام: استخدم فقط المعلومات من قاعدة البيانات.
-
-📜 المحادثة السابقة:
-{context}
-
-👤 المستخدم يسأل: {user_question}
-📚 معلومات من قاعدة البيانات:
-{extracted_info}
-
-📌 الرد:
-"""
-    else:
-        greeting = "Hello! 😊"
-        instructions = """
-❗ Important: Use only database information.
-
-📜 Previous conversation:
-{context}
-
-👤 User asks: {user_question}
-📚 Retrieved info:
-{extracted_info}
-
-📌 Response:
-"""
-
-    prompt = instructions.format(
-        context=context or ("لا يوجد" if lang == "ar" else "None"),
-        user_question=user_question,
-        extracted_info=extracted_info
-    )
-    response = model.generate_content(prompt)
-    return f"{greeting}\n{response.text.strip()}"
-
-@app.route("/ask", methods=["POST"])
-def ask():
-    data = request.json
-    question = data.get("question")
-    if not question:
-        return jsonify({"error": "Missing question"}), 400
-
-    pinecone_answer = get_answer_from_pinecone(question, embedding_model, index)
-    final_answer = ask_gemini_with_combined_answer(question, pinecone_answer, chat_history)
-    chat_history.append((question, final_answer))
-
-    return jsonify({
-        "question": question,
-        "pinecone_matches": pinecone_answer,
-        "answer": final_answer
-    })
-
-@app.route("/")
-def home():
-    return "🤖 API is running. Use POST /ask with {'question': '...'}"
-
-if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860)
+import os
+
+# تحديد مجلد للكاش داخل Docker
+os.environ['TRANSFORMERS_CACHE'] = '/tmp/huggingface_cache'
+os.environ['HF_HOME'] = '/tmp/huggingface'
+
+from flask import Flask, request, jsonify
+from sentence_transformers import SentenceTransformer
+from pinecone import Pinecone
+import google.generativeai as genai
+from langdetect import detect
+
+# تهيئة الموديلات
+embedding_model = SentenceTransformer("intfloat/multilingual-e5-large")
+
+pc = Pinecone(api_key="pcsk_3ax4D8_PH7vWF1KWAMRpyjmEnXhwxswmHSjvqgwovna3xGGbfsgZsMRtRyFi9uCpPyi4B9")
+index = pc.Index("newindex")
+
+genai.configure(api_key="AIzaSyBXtRzMkpm9RNDO09A9N3XoG_vfjgUe5Vw")
+model = genai.GenerativeModel("gemini-2.0-flash")
+
+app = Flask(__name__)
+chat_history = []
+
+def detect_language(text):
+    try:
+        return detect(text)
+    except:
+        return "unknown"
+
+def get_answer_from_pinecone(user_question, embedding_model, index, top_k=5, similarity_threshold=0.7):
+    try:
+        question_vector = embedding_model.encode(user_question).tolist()
+    except Exception as e:
+        return [f"❌ Error embedding question: {e}"]
+
+    try:
+        search_result = index.query(
+            vector=question_vector,
+            top_k=top_k,
+            include_metadata=True
+        )
+    except Exception as e:
+        return [f"❌ Error querying Pinecone: {e}"]
+
+    matches = [m for m in search_result.matches if m.score >= similarity_threshold]
+    sorted_matches = sorted(matches, key=lambda x: x.score, reverse=True)
+
+    answers = []
+    for m in sorted_matches:
+        answer = m.metadata.get('answer', '').strip()
+        source = m.metadata.get('source', 'unknown')
+        score = round(m.score, 3)
+        if answer:
+            answers.append(f"• ({score}) from [{source}]:\n{answer}")
+    return answers if answers else ["⚠️ No similar answers found."]
+
+def ask_gemini_with_combined_answer(user_question, pinecone_answers=[], history=[]):
+    context = "\n".join([f"👤 {q}\n🤖 {a}" for q, a in history])
+    extracted_info = "\n".join([f"• {ans}" for ans in pinecone_answers]) if pinecone_answers else "None"
+    lang = detect_language(user_question)
+
+    if lang == "ar":
+        greeting = "مرحبًا! 😊"
+        instructions = """
+❗ هام: استخدم فقط المعلومات من قاعدة البيانات.
+
+📜 المحادثة السابقة:
+{context}
+
+👤 المستخدم يسأل: {user_question}
+📚 معلومات من قاعدة البيانات:
+{extracted_info}
+
+📌 الرد:
+"""
+    else:
+        greeting = "Hello! 😊"
+        instructions = """
+❗ Important: Use only database information.
+
+📜 Previous conversation:
+{context}
+
+👤 User asks: {user_question}
+📚 Retrieved info:
+{extracted_info}
+
+📌 Response:
+"""
+
+    prompt = instructions.format(
+        context=context or ("لا يوجد" if lang == "ar" else "None"),
+        user_question=user_question,
+        extracted_info=extracted_info
+    )
+    response = model.generate_content(prompt)
+    return f"{greeting}\n{response.text.strip()}"
+
+@app.route("/ask", methods=["POST"])
+def ask():
+    data = request.json
+    question = data.get("question")
+    if not question:
+        return jsonify({"error": "Missing question"}), 400
+
+    pinecone_answer = get_answer_from_pinecone(question, embedding_model, index)
+    final_answer = ask_gemini_with_combined_answer(question, pinecone_answer, chat_history)
+    chat_history.append((question, final_answer))
+
+    return jsonify({
+        "question": question,
+        "pinecone_matches": pinecone_answer,
+        "answer": final_answer
+    })
+
+@app.route("/")
+def home():
+    return "🤖 API is running. Use POST /ask with {'question': '...'}"
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)