Update app.py

app.py

@@ -3,16 +3,10 @@ import random
 import pickle
 import numpy as np
 import re
+import time  # Import modul time untuk delay
 from flask import Flask, request, jsonify
 from sentence_transformers import SentenceTransformer
 from sklearn.metrics.pairwise import cosine_similarity
-# import os
-
-# os.environ['HF_HOME'] = '/tmp/huggingface'
-# os.environ['TRANSFORMERS_CACHE'] = '/tmp/huggingface/transformers'
-# os.environ['HF_DATASETS_CACHE'] = '/tmp/huggingface/datasets'
-# os.environ['HF_METRICS_CACHE'] = '/tmp/huggingface/metrics'
-
 
 class ImprovedBPJSChatbot:
     def __init__(self):
@@ -167,6 +161,10 @@ app = Flask(__name__)
 def chat():
     try:
         msg = request.json.get("message", "").strip()
+        
+        # Tambahkan delay 1 detik sebelum memproses pesan
+        time.sleep(1)
+        
         response = chatbot.generate_response(msg)
         return jsonify({"reply": response})
     except Exception as e:
@@ -181,3 +179,188 @@ if __name__ == '__main__':
     from waitress import serve
     serve(app, host='0.0.0.0', port=7860)
 
+
+
+# import json
+# import random
+# import pickle
+# import numpy as np
+# import re
+# from flask import Flask, request, jsonify
+# from sentence_transformers import SentenceTransformer
+# from sklearn.metrics.pairwise import cosine_similarity
+# # import os
+
+# # os.environ['HF_HOME'] = '/tmp/huggingface'
+# # os.environ['TRANSFORMERS_CACHE'] = '/tmp/huggingface/transformers'
+# # os.environ['HF_DATASETS_CACHE'] = '/tmp/huggingface/datasets'
+# # os.environ['HF_METRICS_CACHE'] = '/tmp/huggingface/metrics'
+
+
+# class ImprovedBPJSChatbot:
+#     def __init__(self):
+#         self.load_models()
+#         self.load_intents()
+        
+#     def load_models(self):
+#         """Load semua model yang diperlukan"""
+#         print("Memuat model dan konfigurasi...")
+        
+#         # Load konfigurasi
+#         with open('model_config.pkl', 'rb') as f:
+#             config = pickle.load(f)
+        
+#         # Load sentence transformer
+#         self.st_model = SentenceTransformer("Dyna-99/local-st-model")
+#         self.preprocessing_enabled = config['preprocessing_enabled']
+        
+#         # Load classifier
+#         with open('svm_model.pkl', 'rb') as f:
+#             self.clf = pickle.load(f)
+        
+#         # Load label encoder
+#         with open('label_encoder.pkl', 'rb') as f:
+#             self.label_encoder = pickle.load(f)
+            
+#         print("Semua model berhasil dimuat!")
+    
+#     def load_intents(self):
+#         """Load data intents untuk responses"""
+#         with open('intents.json', 'r', encoding='utf-8') as f:
+#             self.intents_data = json.load(f)
+        
+#         self.tag_responses = {intent['tag']: intent['responses'] for intent in self.intents_data['intents']}
+        
+#         # Buat embeddings untuk semua patterns (untuk similarity fallback)
+#         self.pattern_embeddings = []
+#         self.pattern_tags = []
+        
+#         for intent in self.intents_data['intents']:
+#             for pattern in intent['patterns']:
+#                 processed_pattern = self.preprocess_text(pattern) if self.preprocessing_enabled else pattern
+#                 embedding = self.st_model.encode(processed_pattern)
+#                 self.pattern_embeddings.append(embedding)
+#                 self.pattern_tags.append(intent['tag'])
+                
+#         self.pattern_embeddings = np.array(self.pattern_embeddings)
+    
+#     def preprocess_text(self, text):
+#         """Preprocessing teks yang sama dengan training"""
+#         text = text.lower()
+        
+#         # Normalisasi singkatan
+#         text = re.sub(r'\bjkk\b', 'jaminan kecelakaan kerja', text)
+#         text = re.sub(r'\bjkm\b', 'jaminan kematian', text)
+#         text = re.sub(r'\bjht\b', 'jaminan hari tua', text)
+#         text = re.sub(r'\bjp\b', 'jaminan pensiun', text)
+#         text = re.sub(r'\bbpjs\b', 'bpjs ketenagakerjaan', text)
+        
+#         # Hapus karakter khusus
+#         text = re.sub(r'[^\w\s]', ' ', text)
+#         text = re.sub(r'\s+', ' ', text).strip()
+        
+#         return text
+    
+#     def get_prediction_confidence(self, msg_embedding):
+#         """Dapatkan prediksi dengan confidence score"""
+#         # Prediksi probabilitas
+#         probabilities = self.clf.predict_proba(msg_embedding)[0]
+#         max_prob = np.max(probabilities)
+#         predicted_class = np.argmax(probabilities)
+#         predicted_tag = self.label_encoder.inverse_transform([predicted_class])[0]
+        
+#         return predicted_tag, max_prob
+    
+#     def similarity_fallback(self, msg_embedding, threshold=0.7):
+#         """Fallback menggunakan cosine similarity"""
+#         similarities = cosine_similarity(msg_embedding, self.pattern_embeddings)[0]
+#         max_similarity_idx = np.argmax(similarities)
+#         max_similarity = similarities[max_similarity_idx]
+        
+#         if max_similarity >= threshold:
+#             return self.pattern_tags[max_similarity_idx], max_similarity
+        
+#         return 'fallback', max_similarity
+    
+#     def get_contextual_response(self, tag, user_message):
+#         """Pilih response yang paling kontekstual"""
+#         responses = self.tag_responses.get(tag, self.tag_responses['fallback'])
+        
+#         # Jika hanya ada satu response, return langsung
+#         if len(responses) == 1:
+#             return responses[0]
+        
+#         # Pilih response berdasarkan kata kunci dalam pesan user
+#         user_words = set(user_message.lower().split())
+        
+#         best_response = responses[0]
+#         best_score = 0
+        
+#         for response in responses:
+#             response_words = set(response.lower().split())
+#             # Hitung kesamaan kata
+#             common_words = user_words.intersection(response_words)
+#             score = len(common_words)
+            
+#             if score > best_score:
+#                 best_score = score
+#                 best_response = response
+        
+#         # Jika tidak ada yang cocok, pilih random
+#         if best_score == 0:
+#             return random.choice(responses)
+        
+#         return best_response
+    
+#     def generate_response(self, message):
+#         """Generate response dengan multiple strategies"""
+#         if not message.strip():
+#             return "Tolong kirim sebuah pesan."
+        
+#         # Preprocessing
+#         processed_msg = self.preprocess_text(message) if self.preprocessing_enabled else message
+#         msg_embedding = self.st_model.encode(processed_msg).reshape(1, -1)
+        
+#         # Strategy 1: SVM prediction dengan confidence
+#         predicted_tag, confidence = self.get_prediction_confidence(msg_embedding)
+        
+#         # Strategy 2: Similarity fallback jika confidence rendah
+#         if confidence < 0.6:  # Threshold bisa di-adjust
+#             fallback_tag, similarity = self.similarity_fallback(msg_embedding)
+#             if similarity > confidence:
+#                 predicted_tag = fallback_tag
+        
+#         # Strategy 3: Contextual response selection
+#         response = self.get_contextual_response(predicted_tag, message)
+        
+#         # Logging untuk debugging
+#         print(f"Input: {message}")
+#         print(f"Processed: {processed_msg}")
+#         print(f"Predicted tag: {predicted_tag} (confidence: {confidence:.3f})")
+        
+#         return response
+
+# # Inisialisasi chatbot
+# chatbot = ImprovedBPJSChatbot()
+
+# # Flask app
+# app = Flask(__name__)
+
+# @app.route('/chat', methods=['POST'])
+# def chat():
+#     try:
+#         msg = request.json.get("message", "").strip()
+#         response = chatbot.generate_response(msg)
+#         return jsonify({"reply": response})
+#     except Exception as e:
+#         print(f"Error: {e}")
+#         return jsonify({"reply": "Maaf, terjadi kesalahan sistem. Silakan coba lagi."})
+
+# @app.route('/health', methods=['GET'])
+# def health():
+#     return jsonify({"status": "healthy", "model": "BPJS Chatbot Improved"})
+
+# if __name__ == '__main__':
+#     from waitress import serve
+#     serve(app, host='0.0.0.0', port=7860)
+