Updated the app fie for gardio changes

app.py

@@ -1,3 +1,4 @@
+
 import pandas as pd
 
 from torchvision import transforms
@@ -7,7 +8,14 @@ from detecto.visualize import show_labeled_image
 from detecto.core import Model
 import matplotlib.pyplot as plt
 import matplotlib.image as img
-
+import matplotlib.pyplot as plt
+import gradio as gr
+import os
+from typing import List, Optional
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib import patches
+from pathlib import Path
 
 transform_img = transforms.Compose([transforms.ToPILImage(),
                                    transforms.Resize(400),
@@ -17,14 +25,104 @@ transform_img = transforms.Compose([transforms.ToPILImage(),
 
 
 labels =  ['damage','BG']
-model = Model.load('/content/drive/MyDrive/Trained_Model.pth', labels)
+model = Model.load('/Trained_Model.pth', labels) # CHange this while uploading
 
-def prediction_defect(new_image,model = model):
+
+
+def prediction_defect(input_image,model = model):
     '''Function takes input of the damaged vehicle
     and provides the damaged area of the vehicle
     '''
-    image = utils.read_image(new_image)
+    image = utils.read_image(input_image)
     new_image = transform_img(image)
     labels, boxes, scores = model.predict(image)
     top = len(scores[scores > .5])
-    show_labeled_image(image, boxes[:top], labels[:top])
+
+    return plot_bboxes( input_image, bboxes= boxes[:top],
+  xywh=False, labels=labels[:top])
+
+
+
+def plot_bboxes(
+  image_file: str,
+  bboxes: List[List[float]],
+  xywh: bool = True,
+  labels: Optional[List[str]] = None
+) -> None:
+    """
+    Args:
+      image_file: str specifying the image file path
+      bboxes: list of bounding box annotations for all the detections
+      xywh: bool, if True, the bounding box annotations are specified as
+        [xmin, ymin, width, height]. If False the annotations are specified as
+        [xmin, ymin, xmax, ymax]. If you are unsure what the mode is try both
+        and check the saved image to see which setting gives the
+        correct visualization.
+
+    """
+    fig = plt.figure()
+
+    # add axes to the image
+    ax = fig.add_axes([0, 0, 1, 1])
+
+    image_folder = Path(image_file).parent
+
+    # read and plot the image
+    image = plt.imread(image_file)
+    plt.imshow(image)
+
+    # Iterate over all the bounding boxes
+    for i, bbox in enumerate(bboxes):
+        if xywh:
+          xmin, ymin, w, h = bbox
+        else:
+          xmin, ymin, xmax, ymax = bbox
+          w = xmax - xmin
+          h = ymax - ymin
+
+        # add bounding boxes to the image
+        box = patches.Rectangle(
+            (xmin, ymin), w, h, edgecolor="red", facecolor="none"
+        )
+
+        ax.add_patch(box)
+
+        if labels is not None:
+          rx, ry = box.get_xy()
+          cx = rx + box.get_width()/2.0
+          cy = ry + box.get_height()/8.0
+          l = ax.annotate(
+            labels[i],
+            (cx, cy),
+            fontsize=8,
+            fontweight="bold",
+            color="white",
+            ha='center',
+            va='center'
+          )
+          l.set_bbox(
+            dict(facecolor='red', alpha=0.5, edgecolor='red')
+          )
+
+    plt.axis('off')
+    outfile = os.path.join(image_folder, "image_bbox.jpg")
+    fig.savefig(outfile)
+
+    print("Saved image with detections to %s" % outfile)
+    return outfile
+
+
+gr.Interface(fn=prediction_defect, 
+            
+           inputs = [ gr.inputs.Image(type="filepath", label="Please Upload the Defect Image") ],
+             outputs= [gr.outputs.Image(type="pil")],
+             examples=[]).launch(debug= True)
+
+
+
+
+
+
+
+
+

requirements.txt

@@ -1,3 +1,4 @@
 pandas
 detecto
-torchvision
+torchvision
+gradio