Upload folder using huggingface_hub

README.md

@@ -1,12 +1,6 @@
 ---
-title: Image Classifier 
-emoji: 🔥
-colorFrom: red
-colorTo: purple
+title: Image_Classifier_
+app_file: app.py
 sdk: gradio
 sdk_version: 4.27.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load the saved model
+model = tf.keras.models.load_model('mymodel.h5')
+
+# Define the labels
+labels = ['Buildings', 'Forest', 'Sea']
+
+# Define the image classification function
+def classify_image(image):
+    # Preprocess the image
+    image = np.array(image)
+    image = tf.image.resize(image, (128, 128))  # Resize the image to match the input size of the model
+    image = tf.expand_dims(image, axis=0)  # Add batch dimension
+    image = tf.keras.applications.resnet50.preprocess_input(image)
+
+    # Predict the class
+    predictions = model.predict(image).flatten()
+
+    # Get the predicted class label
+    predicted_class = labels[np.argmax(predictions)]
+
+    return predicted_class
+
+# Define the Gradio interface
+image_input = gr.Image(type="pil", label="Upload Image")
+label_output = gr.Label()
+
+# Create the Gradio interface
+interface = gr.Interface(fn=classify_image, inputs=image_input, outputs=label_output, title="Image Classifier")
+
+# Launch the interface
+interface.launch()

mymodel.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7b080e26296e770721783f7029b33af1e9c90174c80decc599e2ad9e99103dcf
+size 39713120