Create app.py

app.py

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+from gradio import Interface
+import torch
+from transformers import AutoTokenizer, AutoModelForObjectDetection
+
+# Replace with your desired model name (e.g., yolov5s, efficientdet-d0)
+model_name = "your_pcb_detection_model"
+
+# Load tokenizer and model (assuming pre-trained for object detection)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForObjectDetection.from_pretrained(model_name)
+
+# Define labels (replace with your actual component types)
+labels = ["Resistor", "Capacitor", "IC", "Inductor"]
+
+def predict(image):
+    """Performs image preprocessing, prediction, and label assignment."""
+    # Preprocess image (e.g., resize, normalize) based on model requirements
+    preprocessed_image = preprocess_image(image)  # Implement your preprocessing logic
+    inputs = tokenizer(preprocessed_image, return_tensors="pt")
+
+    # Perform inference
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    # Extract predicted bounding boxes and labels (modify as needed based on model output)
+    predictions = postprocess_predictions(outputs)  # Implement your post-processing logic
+
+    # Assign labels to detections based on model predictions (modify as needed)
+    labeled_predictions = assign_labels(predictions, labels)
+
+    return labeled_predictions
+
+def preprocess_image(image):
+    # Implement your image preprocessing logic here
+    # This might involve resizing, normalization, or other transformations
+    # specific to your chosen model
+    # ...
+    return preprocessed_image
+
+def postprocess_predictions(outputs):
+    # Extract predicted bounding boxes and labels from model output
+    # This might involve accessing specific tensors or attributes
+    # based on your chosen model's architecture
+    # ...
+    return predictions
+
+def assign_labels(predictions, labels):
+    # Assign labels to detections based on model predictions
+    # This might involve using confidence scores or other criteria
+    # ...
+    return labeled_predictions
+
+# Create Gradio interface
+iface = Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),  # Accepts PIL Image format
+    outputs=gr.Label(num_top_classes=len(labels)),
+    title="PCB Component Identification",
+    description="Upload an image of a PCB to identify its components.",
+    allow_flagging=True  # Enable user feedback (optional)
+)
+
+# Launch the interface
+iface.launch()