Update app.py

app.py

@@ -1 +1,54 @@
-a
+import torch
+import requests
+import numpy as np
+import pandas as pd
+import gradio as gr
+from io import BytesIO
+from PIL import Image as PILIMAGE
+from transformers import CLIPProcessor, CLIPModel, CLIPTokenizer
+
+#Selecting device based on availability of GPUs
+device = "cuda" if torch.cuda.is_available() else "cpu"
+    
+#Defining model, processor and tokenizer
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
+
+    
+#Loading the data
+photos = pd.read_csv("./items_data.csv")
+photo_features = np.load("./features.npy")
+photo_ids = pd.read_csv("./photo_ids.csv")
+photo_ids = list(photo_ids['Uniq Id'])
+    
+def find_best_matches(text):
+    
+    #Inference
+    with torch.no_grad():
+        # Encode and normalize the description using CLIP
+        inputs = tokenizer([text],  padding=True, return_tensors="pt")
+        inputs = processor(text=[text], images=None, return_tensors="pt", padding=True)
+    text_encoded =  model.get_text_features(**inputs).detach().numpy()
+  
+    
+    # Finding Cosine similarity
+    similarities = list((text_encoded @ photo_features.T).squeeze(0))
+    
+    #Block of code for displaying top 3 best matches (images)
+    matched_images = []
+    for i in range(3):
+      idx = sorted(zip(similarities, range(photo_features.shape[0])), key=lambda x: x[0], reverse=True)[i][1]
+      photo_id = photo_ids[idx]
+      photo_data = photos[photos["photo_id"] == photo_id].iloc[0]
+      response = requests.get(photo_data["photo_image_url"] + "?w=640")
+      img = PILIMAGE.open(BytesIO(response.content))
+      matched_images.append(img)
+    return matched_images
+    
+    
+#Gradio app     
+iface = gr.Interface(fn=find_best_matches, inputs=[gr.inputs.Textbox(lines=1, label="Text query", placeholder="Introduce the search text...",)],
+            theme = "dark",
+            outputs=gr.outputs.Carousel([gr.outputs.Image(type="pil"), gr.outputs.Image(type="pil"), gr.outputs.Image(type="pil")]),
+            enable_queue=True).launch()