change model directory

app.py

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+from transformers import ViTImageProcessor, AutoModelForImageClassification
+
+import torch
+import gradio as gr
+import os
+import glob
+import mediapipe as mp
+import numpy as np
+from PIL import Image
+
+feature_extractor = ViTImageProcessor.from_pretrained('ArdyL/VIT_SIBI_ALL')
+model = AutoModelForImageClassification.from_pretrained('ArdyL/VIT_SIBI_ALL')
+
+mp_hands = mp.solutions.hands
+# mp_drawing_styles = mp.solutions.drawing_styles
+# mp_holistic = mp.solutions.holistic
+# mp_pose = mp.solutions.pose
+mp_drawing = mp.solutions.drawing_utils
+examples_dir = './'
+example_files = glob.glob(os.path.join(examples_dir, '*.jpg'))
+
+
+def preprocess(im):
+    with mp_hands.Hands(min_detection_confidence=0.3, min_tracking_confidence=0.3) as hands:
+
+        # Read image file with cv2 and process with face_mesh
+        results = hands.process(im)
+        image2 = np.array(im)
+        annotated_image = image2.copy()
+        annotated_image = np.empty(annotated_image.shape)
+        annotated_image.fill(255)
+        hand_found = bool(results.multi_hand_landmarks)
+        if hand_found:
+            for hand_landmarks in results.multi_hand_landmarks:
+                mp_drawing.draw_landmarks(annotated_image, hand_landmarks, mp_hands.HAND_CONNECTIONS,
+                                          mp_drawing.DrawingSpec(
+                                              color=(0, 0, 0), thickness=2, circle_radius=2),
+                                          mp_drawing.DrawingSpec(
+                                              color=(0, 0, 0), thickness=2, circle_radius=2),
+                                          )
+
+            annotated_image[...] /= 255
+    return annotated_image
+
+
+def classify_image(image):
+    preprocessedImage = preprocess(image)
+    with torch.no_grad():
+        model.eval()
+        inputs = feature_extractor(
+            images=preprocessedImage, return_tensors="pt")
+        outputs = model(**inputs)
+
+    logits = outputs.logits
+    predicted_label = logits.argmax(-1).item()
+    label = model.config.id2label[predicted_label]
+    return label  # confidences
+
+
+with gr.Blocks(title=">ViT - SIBI Classifier") as demo:
+    with gr.Tab("Upload Image", id='upload-image'):
+        with gr.Row():
+            uploadImage = gr.Image(
+                type="numpy", image_mode="RGB", shape=(224, 224))
+            output_label = gr.Label(label="Hasil", num_top_classes=5)
+        with gr.Row():
+            send_btn = gr.Button("Terjemahkan")
+            send_btn.click(fn=classify_image, inputs=uploadImage,
+                           outputs=output_label)
+
+    with gr.Tab("Capture Image", id='capture-image'):
+        with gr.Row():
+            streamImage = gr.Image(
+                type="numpy", source='webcam', image_mode="RGB", shape=(224, 224))
+            output_label2 = gr.Label(label="Hasil", num_top_classes=5)
+        with gr.Row():
+            send_btn2 = gr.Button("Terjemahkan")
+            send_btn2.click(fn=classify_image,
+                            inputs=streamImage, outputs=output_label2)
+
+
+# demo.queue(concurrency_count=3)
+demo.launch(debug=True)

requirements.txt

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+mediapipe
+transformers
+torch