Upload 4 files

app.py

@@ -0,0 +1,47 @@
+import gradio as gr
+import joblib
+import numpy as np
+from PIL import Image
+from sklearn.preprocessing import StandardScaler
+
+# Load the trained SVM model and scaler
+clf = joblib.load('svm_model.pkl')
+scaler = joblib.load('scaler.pkl')
+
+# Define a function to preprocess and classify the image
+def classify_image(input_image):
+    # Check if the input is a NumPy array
+    if isinstance(input_image, np.ndarray):
+        # Preprocess the input image
+        image = Image.fromarray(input_image)
+        image = image.resize((64, 64))
+        image = np.array(image)
+        image = image / 255.0  # Normalize the pixel values
+        flattened_image = image.flatten()
+
+        # Scale the image using the same scaler used during training
+        scaled_image = scaler.transform([flattened_image])
+
+        # Make a prediction using the SVM model
+        prediction = clf.predict(scaled_image)
+
+        # Interpret the prediction
+        if prediction[0] == 1:
+            label = "Cat"
+        else:
+            label = "Dog"
+
+        return label
+    else:
+        return "Invalid input. Please provide a valid image."
+
+# Create a Gradio interface
+iface = gr.Interface(fn=classify_image, 
+                     inputs="image",
+                     outputs="text",
+                     live=True,
+                     capture_session=True)
+
+
+# Launch the Gradio interface
+iface.launch(server_name="0.0.0.0", server_port=7860)

requirements.txt

@@ -0,0 +1,4 @@
+gradio 
+scikit-learn==1.2.2
+numpy
+

scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ac42b73f2d17f8a226959fa8f8852aab1154b5446120eb4efbc911df3285255b
+size 295527

svm_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c338ac3007ce5734f81115a7574ed6439ef621db6b34fae9a4318c69943fc46f
+size 196649375