app.py

import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import pickle

filename="chatbot_120_epochs.h5"
model= tf.keras.models.load_model(filename)
tokenizer = pickle.load(open("tokenizer.sav","rb"))

max_story_len=156
max_question_len=6

def predict_answer(story, question):
    # Preprocess the story and question
    story_seq = tokenizer.texts_to_sequences([story.split()])
    question_seq = tokenizer.texts_to_sequences([question.split()])

    story_padded = pad_sequences(story_seq, maxlen=max_story_len)
    question_padded = pad_sequences(question_seq, maxlen=max_question_len)

    # Get the prediction from the model
    pred_results = model.predict([story_padded, question_padded])

    # Get the predicted word
    val_max = np.argmax(pred_results[0])
    predicted_word = ""
    for key, val in tokenizer.word_index.items():
        if val == val_max:
            predicted_word = key
            break

    return predicted_word
	
import gradio as gr

# Create the Gradio interface
iface = gr.Interface(
    fn=predict_answer,
    inputs=["text", "text"],
    outputs="text",
    live=False,
    title="Question Answering Chatbot",
    description="Enter a story and a question to get an answer from the model."
)

# Launch the interface
iface.launch(debug=True)