Update app.py

app.py

@@ -1,48 +1,26 @@
 import gradio as gr
-import tempfile
 from PIL import Image
-import numpy as np
-from scipy.signal import find_peaks
-import cv2 as cv  # Add this import
+
+# Import the ObstructionDetector class from your module
 from obstruction_detector import ObstructionDetector
 
-# Crie um detector de obstrução
+# Create an instance of ObstructionDetector
 detector = ObstructionDetector()
 
-# Function to preprocess the image and return the report, preprocessed image, and histogram
-# Function to preprocess the image and return the report, preprocessed image, and histogram
-def preprocess_image(image):
-    import cv2 as cv  # Import OpenCV here
-
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_image:
-        image.save(temp_image.name, "PNG")
-        img_path = temp_image.name
-        report = detector.detect_obstruction(img_path)
-
-    # Load the image again for display
-    img = cv.imread(img_path)
-
-    # Preprocess the image
-    preprocessed_img = detector.preprocess_image(img)
-
-    # Calculate and smooth the histogram
-    smoothed_histogram = detector.plot_histogram(preprocessed_img)
-
-    # Convert the preprocessed image to a PIL image for display
-    preprocessed_img_pil = Image.fromarray(cv.cvtColor(preprocessed_img, cv.COLOR_BGR2RGB))
-
-    # Convert the histogram to a grayscale image and scale it to [0, 255]
-    histogram_min = smoothed_histogram.min()
-    histogram_max = smoothed_histogram.max()
-    scaled_histogram = ((smoothed_histogram - histogram_min) / (histogram_max - histogram_min) * 255).astype(np.uint8)
-    histogram_img_pil = Image.fromarray(scaled_histogram)
-
-    return report, preprocessed_img_pil, histogram_img_pil  # Return the histogram as an image
-
-# Create the Gradio interface
-iface = gr.Interface(fn=preprocess_image, 
-                     inputs=gr.inputs.Image(type="pil", label="Carregar Imagem"),
-                     outputs=["text", "image", "image"])  # Output the histogram as an image
-
-# Launch the interface
-iface.launch()
+# Define a Gradio function to process the image and return the report
+def process_image(image):
+    # Convert Gradio image data to a PIL image
+    pil_image = Image.fromarray(image.astype('uint8'), 'RGB')
+    
+    # Call the detect_obstruction method of the ObstructionDetector
+    report = detector.detect_obstruction_from_pil_image(pil_image)
+    
+    return report
+
+# Define the Gradio interface
+iface = gr.Interface(fn=process_image, 
+                     inputs=gr.inputs.Image(shape=(224, 224)),  # Adjust shape as needed
+                     outputs="text")
+
+# Launch the Gradio interface
+iface.launch()