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app.py

@@ -9,23 +9,65 @@ import open_clip
 knnpath = '20241204-ams-no-env-open_clip_ViT-H-14-378-quickgelu.npz'
 clip_model_name = 'ViT-H-14-378-quickgelu'
 
+
+# Set page config
+st.set_page_config(
+    page_title="Percept",
+    layout="wide"
+)
+
 #model, preprocess = open_clip.create_model_from_pretrained('hf-hub:laion/CLIP-ViT-g-14-laion2B-s12B-b42K')
 #tokenizer = open_clip.get_tokenizer('hf-hub:laion/CLIP-ViT-g-14-laion2B-s12B-b42K')
 
 model, preprocess = open_clip.create_model_from_pretrained(clip_model_name)
 tokenizer = open_clip.get_tokenizer(clip_model_name)
 
-st.write(model)
-#clip_model = CLIPModel.from_pretrained()
-#clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+@st.cache_resource
+def load_model():
+    """Load the OpenCLIP model and return model and processor"""
+    model, _, preprocess = open_clip.create_model_and_transforms(
+        'ViT-H-14',
+        pretrained='laion2b_s32b_b79k',
+        quickgelu=True
+    )
+    tokenizer = open_clip.get_tokenizer('ViT-H-14')
+    return model, preprocess, tokenizer
+
+def process_image(image, preprocess):
+    """Process image and return tensor"""
+    if isinstance(image, str):
+        # If image is a URL
+        response = requests.get(image)
+        image = Image.open(BytesIO(response.content))
+    # Ensure image is in RGB mode
+    if image.mode != 'RGB':
+        image = image.convert('RGB')
+    processed_image = preprocess(image).unsqueeze(0)
+    return processed_image
+
+def main():
+    st.title("OpenCLIP Image Analyzer (ViT-H-14)")
+    
+    try:
+        # Load model (uses st.cache_resource)
+        with st.spinner('Loading model... This may take a moment.'):
+            model, preprocess, tokenizer = load_model()
+	except Exception as e:
+		st.error(f"Error loading model: {str(e)}")
+		st.info("Please make sure you have enough memory and the correct dependencies installed.")
 
+	knn = np.load(modelpath)
+	st.write(knn['walkability_vecs'].shape)
 
-knn = np.load(modelpath)
-st.write(knn['walkability_vecs'].shape)
+	file = st.file_uploader('Upload An Image')
 
-file = st.file_uploader('Upload An Image')
+	if file:
+		try:
+			with Image.open(file) as img:
+				st.write(file)
+				st.write(img.size)
+		except Exception as e:
+			st.error(f"Error processing image: {str(e)}")
 
-if file:
-    with Image.open(file) as img:
-        st.write(file)
-        st.write(img.size)
+if __name__ == "__main__":
+    main()