Update app.py

app.py

@@ -1,37 +1,26 @@
-# -*- coding: utf-8 -*-
-"""Untitled1.ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1ZlMhMCHSQcigQYuNzsGtJAda3vE_K5R5
-"""
-
-!pip install transformers datasets gradio
-
+import gradio as gr
 from transformers import DetrFeatureExtractor, DetrForObjectDetection
+import torch
+from PIL import Image
 
+
+# Load the pre-trained model and feature extractor
 feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50")
 model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
 
-import torch
-from PIL import Image
-import requests
 
-def predict(image_url):
-    image = Image.open(requests.get(image_url, stream=True).raw)
+
+def predict(image):
     inputs = feature_extractor(images=image, return_tensors="pt")
     outputs = model(**inputs)
 
-    # convert outputs (bounding boxes and class logits) to COCO API output
     target_sizes = torch.tensor([image.size[::-1]])
     results = feature_extractor.post_process(outputs, target_sizes=target_sizes)[0]
 
     potholes = []
     for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
         box = [round(i, 2) for i in box.tolist()]
-        if score > 0.7: # Adjust threshold as needed
-            #  Here,  we'll simply identify everything.  Ideally, you'd filter by the "pothole" class.
+        if score > 0.7: 
             potholes.append({
                 "box": box,
                 "score": round(score.item(), 3),
@@ -40,14 +29,12 @@ def predict(image_url):
 
     return potholes
 
-import gradio as gr
-
 iface = gr.Interface(
     fn=predict,
-    inputs=gr.Textbox(label="Image URL"),  # Input is a text box for the image URL
-    outputs=gr.JSON(label="Detected Potholes"), # Output the results as JSON
+    inputs=gr.inputs.Image(type="pil"),  # Image upload input
+    outputs=gr.outputs.JSON(label="Detected Potholes"),
     title="Pothole Detection POC",
-    description="Enter the URL of an image to detect potholes."
+    description="Upload an image to detect potholes."
 )
 
-iface.launch(debug=True)  # Launch the interface within the Colab notebook (for testing)
+iface.launch()