Update app.py

app.py

@@ -7,6 +7,7 @@ import torch
 from transformers import AutoModelForImageClassification
 from optimum.pipelines import pipeline
 from PIL import Image
+import numpy as np
 device = 1 if torch.cuda.is_available() else "cpu"
 
 chk_point = "kdhht2334/autotrain-diffusion-emotion-facial-expression-recognition-40429105176"
@@ -32,6 +33,9 @@ emotion_dict = {
 }
 
 
+output_img_size = (2100, 700)
+
+
 try:
     pipe = pipeline(
         "image-classification",
@@ -52,11 +56,16 @@ def face_detector(input_img):
     crop = img.crop(bbox)
     return crop
 
-
 def predict(image):
     cropped_face = face_detector(image)
+    face_w, face_h = cropped_face.size
+    print(face_h, face_w)
+    face_re_w = int(face_w * (700 / face_h))
+    resized_face = cropped_face.resize((face_re_w, 700))
+    output_img = Image.new("RGBA", output_img_size)
+    output_img.paste(resized_face, (1050 - int(face_re_w/2), 0))
     predictions = pipe(cropped_face)
-    return cropped_face, {p["label"]: p["score"] for p in predictions}
+    return output_img, {p["label"]: p["score"] for p in predictions}
 
 gr.Interface(
     predict,