sepia

app.py

@@ -1,16 +1,16 @@
+import numpy as np
 import gradio as gr
-from fastai.vision.all import *
 
-learn = load_learner('export.pkl')
-labels = learn.dls.vocab
+def sepia(input_img):
+    sepia_filter = np.array([
+        [0.393, 0.769, 0.189], 
+        [0.349, 0.686, 0.168], 
+        [0.272, 0.534, 0.131]
+    ])
+    sepia_img = input_img.dot(sepia_filter.T)
+    sepia_img /= sepia_img.max()
+    return sepia_img
 
-def classify_image(img):
-    img = PILImage.create(img)
-    pred, idx, probs = learn.predict(img)
-    return dict(zip(labels, map(float,probs)))
-
-
-
-intf = gr.Interface(fn=classify_image,
-                    inputs=gr.Image(),
-                    outputs=[gr.Textbox(), gr.Textbox(), gr.Textbox()])
+demo = gr.Interface(sepia, gr.Image(), "image")
+if __name__ == "__main__":
+    demo.launch()