Update app.py

app.py

@@ -11,7 +11,6 @@ from tensorflow.keras import Sequential
 from tensorflow.keras.layers import Dense, Dropout
 from tensorflow.keras.optimizers import SGD
 
-import redis
 import os
 from dotenv import load_dotenv
 import uvicorn
@@ -23,7 +22,7 @@ app = FastAPI()
 
 @app.on_event("startup")
 async def startup_event():
-    global lemmatizer, words, classes, documents, ignore_words, intents, model, r, training, train_x, train_y, sgd, hist
+    global lemmatizer, words, classes, documents, ignore_words, intents, model, training, train_x, train_y, sgd, hist
     
     lemmatizer = WordNetLemmatizer()
     words = []
@@ -33,10 +32,8 @@ async def startup_event():
 
     with open('intents.json') as file:
         intents = json.load(file)
-    
-    r = redis.Redis(host=os.getenv("REDIS_HOST"), port=int(os.getenv("REDIS_PORT")), db=int(os.getenv("REDIS_DB")))
 
-    while True:  # Infinite training loop
+    while True:
         for intent in intents['intents']:
             for pattern in intent['patterns']:
                 w = nltk.word_tokenize(pattern)
@@ -77,17 +74,16 @@ async def startup_event():
 
         hist = model.fit(np.array(train_x), np.array(train_y), epochs=300, batch_size=5, verbose=1)
 
+        # Save data and model locally
+        with open('words.pkl', 'wb') as f:
+            pickle.dump(words, f)
+        with open('classes.pkl', 'wb') as f:
+            pickle.dump(classes, f)
+        with open('training.pkl', 'wb') as f:  # Save training data
+            pickle.dump(training, f)
+        model.save('chatbot_model.h5') 
 
-        # Save objects to Redis
-        r.set('words', pickle.dumps(words))
-        r.set('classes', pickle.dumps(classes))
-        r.set('train_x', pickle.dumps(train_x))
-        r.set('train_y', pickle.dumps(train_y))
-        r.set('model', pickle.dumps(model))
-
-        print("Data and model saved to Redis. Re-training...")
-
-
+        print("Data and model saved locally. Re-training...")
 
 if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=7860)