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import numpy as np | |
import gradio as gr | |
import tensorflow as tf | |
from tensorflow.keras.models import load_model | |
from tensorflow.keras.preprocessing.text import Tokenizer | |
import pickle | |
# Load the model | |
model = load_model('doctor_ai_model.h5') | |
# Load the tokenizer | |
with open('tokenizer.pkl', 'rb') as f: | |
tokenizer = pickle.load(f) | |
# Load the label encoder | |
with open('label_encoder.pkl', 'rb') as f: | |
label_encoder = pickle.load(f) | |
def chatbot(input_text): | |
# Tokenize and pad the input | |
sequences = tokenizer.texts_to_sequences([input_text]) | |
input_tensor = tf.keras.preprocessing.sequence.pad_sequences(sequences) | |
# Make a prediction | |
response = model.predict(input_tensor) | |
print("Model output probabilities:", response) | |
# Get predicted label | |
predicted_label = np.argmax(response, axis=1) | |
# Handle unknown labels | |
if predicted_label[0] < len(label_encoder.classes_): | |
decoded_label = label_encoder.inverse_transform(predicted_label) | |
else: | |
decoded_label = "Unknown label" | |
return decoded_label[0] | |
# Create a Gradio interface | |
iface = gr.Interface(fn=chatbot, | |
inputs="text", | |
outputs="text", | |
title="Doctor AI Chatbot", | |
description="Enter a medical-related question to get answers based on trained categories.") | |
# Launch the interface | |
iface.launch() | |