Update app.py

app.py

@@ -3,42 +3,24 @@ from ultralytics import YOLO
 import cv2
 import numpy as np
 import gradio as gr
-import requests
 
-# ✅ Download model from Hugging Face if not already present
-model_path = "best.pt"
-hf_model_url = "https://huggingface.co/Sakthi3214/pcb_detection/resolve/main/best.pt"
-
-if not os.path.exists(model_path):
-    print("Downloading YOLOv8 model...")
-    response = requests.get(hf_model_url, stream=True)
-    with open(model_path, "wb") as f:
-        for chunk in response.iter_content(chunk_size=1024):
-            f.write(chunk)
-    print("Download complete!")
-
-# ✅ Load the YOLOv8 model
+# ✅ Load YOLOv8 model from Hugging Face
+model_path = "https://huggingface.co/Sakthi3214/pcb_detection/resolve/main/best.pt"
 model = YOLO(model_path)
 
 # ✅ Define class names (Manually if model.names is empty)
-class_names = model.names if model.names else {
+class_names = {
     0: "Missing Hole",
     1: "Mouse Bite",
     2: "Open Circuit",
     3: "Short",
     4: "Spur",
     5: "Copper",
-}
+} if not model.names else model.names  # Use model.names if available
 
 def detect_pcb_faults(image):
     """Runs YOLOv8 on the input image and returns detected defects."""
-    img_copy = image.copy()  # ✅ Avoid modifying the original image
-
-    # ✅ Run inference
-    results = model.predict(img_copy, conf=0.01)  
-
-    if not results:
-        return img_copy  # No detections, return original image
+    results = model(image, conf=0.02)  # 🔥 Lower confidence to detect more defects
 
     boxes = results[0].boxes.xyxy.cpu().numpy()  # Extract bounding boxes
     confs = results[0].boxes.conf.cpu().numpy()  # Extract confidence scores
@@ -46,15 +28,15 @@ def detect_pcb_faults(image):
 
     # ✅ Draw bounding boxes and labels
     for (x1, y1, x2, y2), conf, class_id in zip(boxes, confs, class_ids):
-        cv2.rectangle(img_copy, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 3)  # Green box
+        cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 3)  # 🟢 Thicker Box
         
-        # ✅ Get class label
+        # Get class label from dictionary
         label = f"{class_names.get(int(class_id), 'Unknown')} ({conf:.2f})"
         
-        # ✅ Add text
-        cv2.putText(img_copy, label, (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 255, 0), 3)
+        # 🔥 Larger Text Size & Thicker Font
+        cv2.putText(image, label, (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 255, 0), 3)
 
-    return img_copy
+    return image
 
 # ✅ Gradio UI for PCB Fault Detection
 gr.Interface(