Update app.py

app.py

@@ -64,7 +64,9 @@ def process_image(pilimg):
     if classification == "Good":
         out_pilimg = pilimg.convert("RGB")
         draw = ImageDraw.Draw(out_pilimg)
-        font = ImageFont.truetype("arialbd.ttf", 30)  # Use arial.ttf for bold font
+        font_path = "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf"
+        font = ImageFont.truetype(font_path, 30)
+        #font = ImageFont.truetype("arialbd.ttf", 30)  # Use arial.ttf for bold font
 
         draw.text((250, 10), "Good", fill="green", font=font)
         gr.Info("No defect detected,GOOD!", duration=3)  
@@ -75,7 +77,9 @@ def process_image(pilimg):
         out_pilimg = Image.fromarray(img_bgr[..., ::-1])  # RGB-order PIL image
 
         draw = ImageDraw.Draw(out_pilimg)
-        font = ImageFont.truetype("arialbd.ttf", 30)  # Use arial.ttf for bold font
+        font_path = "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf"
+        font = ImageFont.truetype(font_path, 30)
+        #font = ImageFont.truetype("arialbd.ttf", 30)  # Use arial.ttf for bold font
 
         draw.text((250, 10), "Defective", fill="red", font=font)
         
@@ -97,118 +101,3 @@ interface = gr.Interface(
 
 # Launch the interface
 interface.launch(share=True)
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-from ultralytics import YOLO
-from PIL import Image, ImageDraw, ImageFont
-import gradio as gr
-from huggingface_hub import snapshot_download
-import os
-from torchvision import transforms
-
-classes = {0: "Defective", 1: "Good"}
-
-model_path = "best_int8_openvino_model"
-
-def load_model_local():   
-    detection_model = YOLO(model_path, task='classify') # Load the model
-    return detection_model
-
-def load_model(repo_id):
-    download_dir = snapshot_download(repo_id)
-    print(download_dir)
-    path  = os.path.join(download_dir, "best_int8_openvino_model")
-    print(path)
-    detection_model = YOLO(path, task='classify')
-    return detection_model
-
-def predict(pilimg):
-    source = pilimg
-
-    # Call the model to transform image size
-    transform = transforms.Compose([
-        transforms.Resize((224, 224)), 
-        transforms.ToTensor(), 
-    ]) 
-    
-    source = transform(source) # Update the source image size to 224x224, 1 of 2 sizes accepted by Yolo classification model
-    
-    #result = detection_model.predict(source, conf=0.55, iou=0.6)
-    result = detection_model.predict(source) # Make prediction
-    # Get the top prediction
-    label = result[0].probs.top1 
-
-    class_names = detection_model.names  # Retrieves the class names mapping (dict-like)
-    classified_type = class_names[label]  # Map numeric label to class name
-    print (">>> Class : ", classified_type)
-    
-    confidence = result[0].probs.top1conf # Get the top class confidence
-    print(">>> Confidence : ", confidence)
-    
-    annotated_image = pilimg.convert("RGB")
-    draw = ImageDraw.Draw(annotated_image)
-    font = ImageFont.truetype("arialbd.ttf", 30)  # Use arial.ttf for bold font
-
-    if classified_type == classes[0]:
-        draw.text((300, 10), classified_type, fill="red", font=font)
-        gr.Warning("Defect detected, BAD!.")  
-    else:
-        draw.text((300, 10), classified_type, fill="green", font=font)
-        gr.Info("No defect detected,GOOD!")  
-
-    #draw.text((300, 10), classified_type, fill="red", font=font)
-        
-    return annotated_image
-
-detection_model = load_model_local()
-
-title = "Detect the status of the cap, DEFECTIVE or GOOD"
-interface = gr.Interface(
-                fn=predict,
-                inputs=gr.Image(type="pil", label="Input Image"),
-                outputs=gr.Image(type="pil", label="Classification result"),
-                title=title,
-             )
-
-# Launch the interface
-interface.launch(share=True)
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