Create app.py

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+from segment_anything import sam_model_registry, SamAutomaticMaskGenerator
+import base64
+import requests
+from gradio_client import Client
+
+# Load the segmentation model
+sam_checkpoint = "sam_vit_h_4b8939.pth"
+model_type = "vit_h"
+sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
+
+# Define a function for image segmentation
+def segment_image(input_image):
+    image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
+    mask_generator = SamAutomaticMaskGenerator(sam)
+    masks = mask_generator.generate(image)
+
+    segmented_regions = []  # List to store segmented regions
+
+    for i, mask_dict in enumerate(masks):
+        mask_data = (mask_dict['segmentation'] * 255).astype(np.uint8)
+        segmented_region = cv2.bitwise_and(input_image, input_image, mask=mask_data)
+
+        x, y, w, h = map(int, mask_dict['bbox'])
+        cropped_region = segmented_region[y:y+h, x:x+w]
+
+        # Convert to base64 image
+        _, buffer = cv2.imencode(".png", cv2.cvtColor(cropped_region, cv2.COLOR_BGR2RGB))
+        segmented_image_base64 = base64.b64encode(buffer).decode()
+        segmented_regions.append(segmented_image_base64)  # Add to the list
+
+    return segmented_regions
+
+# Function to call the API and calculate cosine similarity
+def calculate_cosine_similarity(segmented_images):
+    highest_cosine = -1
+    highest_cosine_base64 = ""
+    
+    client = Client("https://ktllc-clip-model-inputbase64.hf.space/--replicas/mmz7z/")
+    
+    for base64_image in segmented_images:
+        # Call the API here using the base64 image
+        result = client.predict(base64_image, base64_image, api_name="/predict")
+        
+        cosine_value = result['similarity']
+        print(f"Base64 Image: {base64_image}, Cosine Similarity: {cosine_value}")
+            
+        if cosine_value > highest_cosine:
+            highest_cosine = cosine_value
+            highest_cosine_base64 = base64_image
+
+    print(f"Highest Cosine Similarity: {highest_cosine} (Base64 Image: {highest_cosine_base64})")
+
+# Create Gradio components
+input_image = gr.inputs.Image()
+output_images = gr.outputs.JSON()
+
+# Create a Gradio interface
+segmentation_interface = gr.Interface(fn=segment_image, inputs=input_image, outputs=output_images)
+
+# Launch the segmentation interface
+segmentation_interface.launch()
+
+# Get the segmented images from the segmentation interface
+segmented_images = segmentation_interface.run()
+segmentation_interface.close()
+
+# Call the API for each segmented image and calculate cosine similarity
+calculate_cosine_similarity(segmented_images)