main file

app.py

@@ -0,0 +1,60 @@
+import gradio as gr
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import imutils
+import easyocr
+from PIL import Image
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+
+# Load the model and image processor
+processor = AutoImageProcessor.from_pretrained("beingamit99/car_damage_detection")
+model = AutoModelForImageClassification.from_pretrained("beingamit99/car_damage_detection")
+
+# Define the function that takes an image as input and returns a text output
+def classify_image(input_image):
+    # Load and process the image
+    image = np.array(input_image)
+    inputs = processor(images=image, return_tensors="pt")
+
+    # Make predictions
+    outputs = model(**inputs)
+    logits = outputs.logits.detach().cpu().numpy()
+    predicted_class_id = np.argmax(logits)
+    predicted_proba = np.max(logits)
+    label_map = model.config.id2label
+    predicted_class_name = label_map[predicted_class_id]
+
+    # OCR
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    bfilter = cv2.bilateralFilter(gray, 11, 17, 17)
+    edged = cv2.Canny(bfilter, 30, 200)
+    keypoints = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+    contours = imutils.grab_contours(keypoints)
+    contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10]
+    location = None
+    for contour in contours:
+        approx = cv2.approxPolyDP(contour, 10, True)
+        if len(approx) == 4:
+            location = approx
+            break
+    mask = np.zeros(gray.shape, np.uint8)
+    new_image = cv2.drawContours(mask, [location], 0, 255, -1)
+    new_image = cv2.bitwise_and(image, image, mask=mask)
+    (x, y) = np.where(mask == 255)
+    (x1, y1) = (np.min(x), np.min(y))
+    (x2, y2) = (np.max(x), np.max(y))
+    cropped_image = gray[x1:x2+3, y1:y2+3]
+    reader = easyocr.Reader(['en'])
+    result = reader.readtext(cropped_image)
+    text = result[0][1]
+
+    # Return the results
+    return f"Predicted class: {predicted_class_name} (probability: {predicted_proba:.4f}", text
+
+# Create Gradio interface
+input_image = gr.components.Image()
+output_text = gr.components.Text()
+output_text2 = gr.components.Text()
+
+gr.Interface(fn=classify_image, inputs=input_image, outputs=[output_text, output_text2], title="AutoVision").launch(debug = 1)