Update app.py

app.py

@@ -3,17 +3,19 @@ import numpy as np
 import pickle
 import gradio as gr
 from tensorflow.keras.preprocessing import sequence
+import random
+import time
 
 # Load the encoder model
-enc_model = tf.keras.models.load_model('./encoder_model.h5')
+enc_model = tf.keras.models.load_model('/kaggle/input/model-1/encoder_model.h5')
 
 # Load the decoder model
-dec_model = tf.keras.models.load_model('./decoder_model.h5')
+dec_model = tf.keras.models.load_model('/kaggle/input/model-1/decoder_model.h5')
 
-with open('./tokenizer.pkl', 'rb') as f:
+with open('/kaggle/input/tokenizer1/tokenizer.pkl', 'rb') as f:
     tokenizer = pickle.load(f)
 
-with open('./tokenizer_params (1).pkl', 'rb') as f:
+with open('/kaggle/input/tokenizer-params/tokenizer_params (1).pkl', 'rb') as f:
     tokenizer_params = pickle.load(f)
 
 maxlen_questions = tokenizer_params["maxlen_questions"]
@@ -27,7 +29,7 @@ def str_to_tokens(sentence: str):
         tokens_list.append(tokenizer.word_index[word])
     return sequence.pad_sequences([tokens_list], maxlen=maxlen_questions, padding='post')
 
-def chatbot_response(question):
+def chatbot_response(question, chat_history):
     states_values = enc_model.predict(str_to_tokens(question))
     empty_target_seq = np.zeros((1, 1))
     empty_target_seq[0, 0] = tokenizer.word_index['start']
@@ -52,16 +54,28 @@ def chatbot_response(question):
         states_values = [h, c]
     
     decoded_translation = decoded_translation.split(' end')[0]
-    return decoded_translation
+    bot_message = decoded_translation
+    
+    chat_history.append((question, bot_message))
+    time.sleep(2)
+    return "", chat_history
+
+# Gradio Blocks Interface
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.ClearButton([msg, chatbot])
+
+    def respond(message, chat_history):
+        return chatbot_response(message, chat_history)
 
-# Gradio Interface
+    msg.submit(respond, [msg, chatbot], [msg, chatbot])
+
+# Launch the Gradio interface on Hugging Face Spaces
 iface = gr.Interface(
-    fn=chatbot_response,
-    inputs=gr.inputs.Textbox(),
-    outputs=gr.outputs.Textbox(),
+    fn=demo,
     title="Chatbot",
     description="Talk to the chatbot and it will respond!"
 )
 
-# Launch the Gradio interface on Hugging Face Spaces
 iface.launch()