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import gradio as gr
import tensorflow as tf
from PIL import Image
import numpy as np
labels = ['Banana', 'Coconut', 'Eggplant', 'Mango', 'Melon', 'Orange', 'Pineapple', 'Watermelon']
def predict_pokemon_type(uploaded_file):
if uploaded_file is None:
return "No file uploaded.", None, "No prediction"
model = tf.keras.models.load_model('fruits-xception-model.keras')
# Load the image from the file path
with Image.open(uploaded_file) as img:
img = img.resize((150, 150))
img_array = np.array(img)
prediction = model.predict(np.expand_dims(img_array, axis=0))
# Identify the most confident prediction
confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
return img, confidences
# Define the Gradio interface
iface = gr.Interface(
fn=predict_pokemon_type, # Function to process the input
inputs=gr.File(label="Upload File"), # File upload widget
outputs=["image", "text"], # Output types for image and text
title="Fruit Classifier", # Title of the interface
description="Upload a picture of a Fruit (preferably a Banana, Coconut, Eggplant, Mango, Melon, Orange, Pineapple or Watermelon) to see what fruit it is and the models confidence level. Accuracy: 0.8997 - Loss: 0.4229 on Test Data" # Description of the interface
)
# Launch the interface
iface.launch()
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