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import os | |
import tensorflow as tf | |
from tensorflow import keras | |
import gradio as gr | |
# Load the model | |
model_path = 'doctor_ai_model.h5' # or .h5 | |
model = keras.models.load_model(model_path) | |
def predict(input_data): | |
# Convert input to tensor | |
input_tensor = tf.convert_to_tensor(input_data) | |
# Ensure the input shape is correct | |
if input_tensor.shape[1] != 27: # Adjust based on your input shape | |
return "Input data must have shape: (None, 27)" | |
# Expand dimensions for the model | |
input_tensor = tf.expand_dims(input_tensor, axis=0) | |
# Make prediction | |
prediction = model.predict(input_tensor) | |
predicted_class = tf.argmax(prediction, axis=1).numpy().tolist() | |
return predicted_class | |
# Create Gradio interface | |
iface = gr.Interface(fn=predict, | |
inputs=gr.inputs.Dataframe(type='numpy', | |
label='Input Data (should have shape (None, 27))'), | |
outputs='json') | |
# Launch the Gradio app | |
iface.launch() | |