uploaded_app and requirements file

app.py

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+import streamlit as st
+import torch
+import cv2
+import albumentations as A
+import torch.nn.functional as F
+import pandas as pd
+import numpy as np
+import pickle
+from pathlib import Path
+from model.clip_model import CLIPModel
+st.title("Product Image to description Prediction in E-commerce")
+
+def get_emebddings(file_path):
+    with open(file_path,'rb') as file:
+        data = pickle.load(file)
+
+    return data
+
+
+# def find_text_matches(model,text_emebddings,)
+
+
+embeddings_data_path = Path("./data/embeddings.pkl")
+image_caption_path = Path("./data/image_details.csv")
+model_path = Path('./model/best.pt')
+clip_model = CLIPModel().to('cpu')
+clip_model.load_state_dict(torch.load(model_path,map_location='cpu'))
+embeddings = get_emebddings(embeddings_data_path)
+caption_df = pd.read_csv(image_caption_path)
+# print(caption_df.head())
+
+
+
+def find_text_matches(model, text_emebddings, image_path,actual_captions,max_out=4):
+    item={}
+    image = cv2.imread(image_path)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    transform = A.Compose([
+                        A.Resize(224,224,always_apply=True),
+                        A.Normalize(max_pixel_value=255.0,always_apply=True)
+                            ])
+    trans_image = transform(image=image)['image']
+    item['image'] = torch.tensor(trans_image).permute(2,0,1).float().unsqueeze(0)
+    
+    #Prediction
+    with torch.no_grad():
+        image_features = model.image_encoder(item['image'].to('cpu'))
+        image_embeddings = model.image_projection(image_features)
+        image_embeddings_n = F.normalize(image_embeddings,p=2,dim=-1)
+        text_embeddings_n = F.normalize(text_emebddings,p=2,dim=-1)
+        dot_similarity = text_embeddings_n @ image_embeddings_n.T
+        values,indices = torch.topk(dot_similarity.T.cpu() ,k=20)
+        matches = [actual_captions[idx] for idx in indices[::5]]
+    return matches
+
+
+
+st.subheader("Select the Image from Given files path")
+images = ("./images/0108775015.jpg","./images/0120129014.jpg","./images/0187949019.jpg","./images/0203595036.jpg","./images/0212629031.jpg","./images/0212629048.jpg","./images/0237347052.jpg")
+image = st.selectbox("images",images)
+st.subheader("Selected Image")
+st.image(image)
+ok = st.button("Predict")
+if ok:
+    # st.write("true")
+    st.write("Predicted Product Description")
+    matches = find_text_matches(clip_model,embeddings,image,caption_df['caption'].values)
+    for i in matches:
+        st.write(i)
+
+
+

requirements.txt

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+opencv-python==4.8.0.74
+timm==0.9.7
+torch==2.0.0
+pandas==2.0.3
+numpy==1.23.5
+albumentations==1.3.1
+matplotlib==3.7.2
+tqdm==4.66.1
+transformers==4.33.0
+streamlit==1.28.2