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import gradio as gr
import tensorflow as tf
import numpy as np
import json
# Load the trained model
model = tf.keras.models.load_model("model/plant_identifier_efficientnetb0.keras")
# Load class indices
with open("model/class_indices.json", "r") as f:
class_indices = json.load(f)
# Reverse the class_indices to map predicted index -> label
index_to_class = {v: k for k, v in class_indices.items()}
def predict(image):
image = image.resize((224, 224))
img_array = np.array(image) / 255.0
img_array = img_array[np.newaxis, ...]
# Predict
prediction = model.predict(img_array)
predicted_index = int(np.argmax(prediction))
confidence = float(np.max(prediction))
label = index_to_class[predicted_index]
return f"This looks like a {label} ({confidence:.2%} confidence)."
# Gradio interface
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs="text",
title="Plant Identifier",
description="Upload an image of a plant, and this AI will tell you what type it is.",
theme="default",
)
demo.launch()
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