adding model

app.py

@@ -1,7 +1,40 @@
 import gradio as gr
+import pickle
+import numpy as np
+from PIL import Image
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Load your model
+with open("model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# Preprocessing function
+def preprocess_image(img: Image.Image):
+    # Resize and convert to a flat array (adjust according to your model's needs)
+    img = img.resize((64, 64))  # example size
+    img_array = np.array(img)
+    if img_array.ndim == 3 and img_array.shape[2] == 3:
+        img_array = img_array.mean(axis=2)  # convert to grayscale if needed
+    img_flat = img_array.flatten()
+    return img_flat
+
+# Prediction function
+def predict(image):
+    try:
+        img_flat = preprocess_image(image)
+        prediction = model.predict([img_flat])[0]
+        return prediction
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Cat vs Dog Classifier",
+    description="Upload an image and the model will predict: cat, dog, or idk.",
+)
+
+if __name__ == "__main__":
+    iface.launch()
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e672faf54a8d818b779e978b129e64b4e2f6ef5d228d63dbbc60d7d137f7e34d
+size 47068798

requirements.txt

@@ -0,0 +1,5 @@
+gradio==4.26.0
+scikit-learn==1.4.2
+pillow==10.3.0
+numpy==1.26.4
+