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README.md

@@ -1,10 +1,10 @@
 ---
-title: Catvsdog
-emoji: 🦀
-colorFrom: yellow
-colorTo: purple
+title: Blogfeedback Demo
+emoji: 📚
+colorFrom: green
+colorTo: blue
 sdk: gradio
-sdk_version: 3.50.2
+sdk_version: 3.42.0
 app_file: app.py
 pinned: false
 ---

app.py

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+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# Load the pre-trained model
+model = tf.keras.models.load_model('model.h5')  # Replace with the path to your saved model
+
+# Define a Gradio interface for image classification
+def classify_image(image):
+    # Preprocess the input image
+    image = Image.fromarray(image)
+    image = image.resize((128, 128))
+    image = np.array(image)
+    image = image / 255.0  # Normalize the pixel values
+
+    # Make a prediction
+    prediction = model.predict(np.expand_dims(image, axis=0))
+
+    # Get the class label
+    class_label = "Dog" if prediction[0][0] < 0.5 else "Cat"
+
+    return class_label
+
+# Create a Gradio interface
+iface = gr.Interface(fn=classify_image, 
+                     inputs="image",
+                     outputs="text",
+                     capture_session=True)
+
+# Launch the Gradio interface
+
+
+iface.launch(server_name="0.0.0.0", server_port=7860)

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ae15813984fa0368d190eb52389ba701d7f91dee0cc2a22bf4899f59dce5facd
+size 61441760

requirements.txt

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+xgboost==1.7.6
+gradio 
+scikit-learn==1.2.2
+tensorflow==2.12.0
+numpy
+