Update app.py

app.py

@@ -1,4 +1,38 @@
 import gradio as gr
+from PIL import Image, ImageDraw
+from transformers import pipeline
+
+
+def plot_results(image, results, threshold=0.7):
+    image = Image.fromarray(np.uint8(image))
+    draw = ImageDraw.Draw(image)
+    for result in results:
+        score = result["score"]
+        label = result["label"]
+        box = list(result["box"].values())
+        if score > threshold:
+            x, y, x2, y2 = tuple(box)
+            draw.rectangle((x, y, x2, y2), outline="red", width=1)
+            draw.text((x, y), label, fill="white")
+            draw.text(
+                (x + 0.5, y - 0.5),
+                text=str(score),
+                fill="green" if score > 0.7 else "red",
+            )
+    return image
+
+def predict(image):
+    # make the object detection pipeline
+    obj_detector = pipeline(
+        "object-detection", model="anindya64/detr-resnet-50-dc5-hardhat-finetuned"
+    )
+    results = obj_detector(train_dataset[0]["image"])
+    return plot_results(image)
+
+
+
+results = obj_detector(image)
+plot_results(image, results)
 
 def greet(name):
     return "Hello " + name + "!!"
@@ -10,8 +44,8 @@ DETR model finetuned on "anindya64/hardhat" for hard hats detection.
 
 demo = gr.Interface(
     fn=greet, 
-    inputs="text", 
-    outputs="text",
+    inputs=gr.Image(type="filepath", label="Input Image"), 
+    outputs="image",
     title=title,
     description=description
 )