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requirements.txt

@@ -1,121 +1,6 @@
-import streamlit as st
-from PIL import Image
-import numpy as np
-import torch
-from sklearn.utils.extmath import softmax
-import open_clip
-
-#from transformers import CLIPProcessor, CLIPModel
-
-knnpath = '20241204-ams-no-env-open_clip_ViT-H-14-378-quickgelu.npz'
-clip_model_name = 'ViT-H-14-378-quickgelu'
-pretrained_name = 'dfn5b'
-
-categories = ['walkability', 'bikeability', 'pleasantness', 'greenness', 'safety']
-
-# Set page config
-st.set_page_config(
-    page_title="Percept",
-    layout="wide"
-)
-
-debug = True
-
-#st.write("Available models:", open_clip.list_models())
-
-@st.cache_resource
-def load_model():
-    """Load the OpenCLIP model and return model and processor"""
-    model, _, preprocess = open_clip.create_model_and_transforms(
-        clip_model_name, pretrained=pretrained_name
-    )
-    tokenizer = open_clip.get_tokenizer(clip_model_name)
-    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 knn_get_score(knn, k, cat, vec):
-    allvecs = knn[f'{cat}_vecs']
-    if debug: st.write('allvecs.shape', allvecs.shape)
-    scores = knn[f'{cat}_scores']
-    if debug: st.write('scores.shape', scores.shape)
-    # Compute cosine similiarity of vec against allvecs
-    # (both are already normalized)
-    cos_sim_table = vec @ allvecs.T
-    if debug: st.write('cos_sim_table.shape', cos_sim_table.shape)
-    # Get sorted array indices by similiarity in descending order
-    sortinds = np.flip(np.argsort(cos_sim_table, axis=1), axis=1)
-    if debug: st.write('sortinds.shape', sortinds.shape)
-    # Get corresponding scores for the sorted vectors
-    kscores = scores[sortinds][:k]
-    if debug: st.write('kscores.shape', kscores.shape)
-    # Get actual sorted similiarity scores
-    ksims = cos_sim_table[:, sortinds][:,:k]
-    if debug: st.write('ksims.shape', ksims.shape)
-    # Apply normalization after exponential formula
-    ksims = softmax(10**ksims)
-    # Weighted sum
-    kweightedscore = np.sum(kscores * ksims)
-    return kweightedscore
-
-
-@st.cache_resource
-def load_knn():
-    return np.load(knnpath)
-
-def main():
-    st.title("Percept: Human Perception of Street View Image Analyzer")
- 
-    try:
-        with st.spinner('Loading CLIP model... This may take a moment.'):
-            model, preprocess, tokenizer = load_model()
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        model = model.to(device)
-    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.")
-
-    with st.spinner('Loading KNN model... This may take a moment.'):
-        knn = load_knn()
-    if debug: st.write(knn['walkability_vecs'].shape)
-
-    file = st.file_uploader('Upload An Image')
-
-    if file:
-        try:
-            image = Image.open(file)
-
-            st.image(image, caption="Uploaded Image", width=640)
-                
-            # Process image
-            with st.spinner('Processing image...'):
-                processed_image = process_image(image, preprocess)
-                processed_image = processed_image.to(device)
-
-                # Encode into CLIP vector
-                with torch.no_grad():
-                    vec = model.encode_image(processed_image)
-                    
-                    # Normalize vector
-                    vec /= vec.norm(dim=-1, keepdim=True)
-                if debug: st.write(vec.shape)
-                vec = vec.numpy()
-                k = 40
-                for cat in ['walkability']:
-                    st.write(cat, 'rating =', knn_get_score(knn, k, cat, vec))
-
-        except Exception as e:
-            st.error(f"Error processing image: {str(e)}")
-
-if __name__ == "__main__":
-    main()
+streamlit
+torch
+open_clip-torch
+Pillow
+requests
+scikit-learn