created app.py

app.py

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+import tensorflow as tf
+import numpy as np
+import pickle
+import gradio as gr
+from tensorflow.keras.preprocessing import sequence
+
+# Load the encoder model
+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('/kaggle/input/model-1/decoder_model.h5')
+
+with open('/kaggle/input/tokenizer1/tokenizer.pkl', 'rb') as f:
+    tokenizer = pickle.load(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"]
+maxlen_answers = tokenizer_params["maxlen_answers"]
+
+def str_to_tokens(sentence: str):
+    words = sentence.lower().split()
+    tokens_list = list()
+
+    for word in words:
+        tokens_list.append(tokenizer.word_index[word])
+    return sequence.pad_sequences([tokens_list], maxlen=maxlen_questions, padding='post')
+
+def chatbot_response(question):
+    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']
+    stop_condition = False
+    decoded_translation = ''
+
+    while not stop_condition:
+        dec_outputs, h, c = dec_model.predict([empty_target_seq] + states_values)
+        sampled_word_index = np.argmax(dec_outputs[0, -1, :])
+        sampled_word = None
+
+        for word, index in tokenizer.word_index.items():
+            if sampled_word_index == index:
+                decoded_translation += f' {word}'
+                sampled_word = word
+
+        if sampled_word == 'end' or len(decoded_translation.split()) > maxlen_answers:
+            stop_condition = True
+
+        empty_target_seq = np.zeros((1, 1))
+        empty_target_seq[0, 0] = sampled_word_index
+        states_values = [h, c]
+    
+    decoded_translation = decoded_translation.split(' end')[0]
+    return decoded_translation
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=chatbot_response,
+    inputs=gr.inputs.Textbox(),
+    outputs=gr.outputs.Textbox(),
+    title="Chatbot",
+    description="Talk to the chatbot and it will respond!"
+)
+
+# Launch the Gradio interface on Hugging Face Spaces
+iface.launch(share=True)