Update app.py

app.py

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+pip install gradio==3.14.0
+import gradio as gr
+import numpy as np
+import tensorflow as tf
+import matplotlib.pyplot as plt
+from PIL import Image
+import pickle
+from tensorflow.keras.models import load_model
+
+# Load the RGB to hyperspectral conversion model
+converion_model = load_model('/kaggle/input/convmo/Conversion_model.h5')
+
+# Load the cancer classification model
+#cancer_model = pickle.load(open("/kaggle/input/classi/ClassRF (1).pkl", "rb"))
+
+cancer_model = pickle.load(open("/kaggle/input/logistic/LRclass.pkl", "rb"))
+
+def classify(rgb_image):
+    img = Image.fromarray(rgb_image.astype('uint8'), 'RGB')
+    img = img.resize((272, 512))
+    arr = np.array(img).astype('float32') / 255.0
+    new_size = (272, 512)
+    resized_rgb_img = tf.image.resize(arr, new_size)
+    resized_rgb_img = tf.reshape(resized_rgb_img, (272, 512, 3))
+    resized_rgb_img = np.expand_dims(resized_rgb_img , axis=0)
+    
+  # Convert the RGB image to hyperspectral using your model
+    hyperspectral_image = converion_model(resized_rgb_img)
+    hyperspectral_image = tf.image.resize(hyperspectral_image, new_size)
+    hyperspectral_image = tf.reshape(hyperspectral_image, (272, 512, 16))
+    imgplot = hyperspectral_image.numpy().astype(np.float32)
+    imgplot= imgplot.reshape(-1, 272*512*16)
+    prediction = cancer_model.predict(imgplot)
+    if np.argmax(prediction) == 0:
+        x= "cancer"
+    else:
+        x="not a cancer"
+    return x  
+
+# Define the Gradio interface
+#image_input = gr.inputs.Image()
+output_label = gr.components.Label()
+#output_label=["text"]
+image_input = gr.components.Image()
+gr.Interface(
+    classify, 
+    image_input, 
+    output_label, 
+    title="RGB to Hyperspectral Conversion and Cancer Classification", 
+    description="Upload an RGB image and get a prediction of whether you have skin cancer or not."
+).launch(share=True)
+
+