Create app.py

app.py

@@ -0,0 +1,39 @@
+import pandas as pd, numpy as np
+import os
+from transformers import CLIPProcessor, CLIPTextModel, CLIPModel
+
+import gradio as gr
+
+
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+df = {0: pd.read_csv('data.csv'), 1: pd.read_csv('data2.csv')}
+embeddings = {0: np.load('embeddings2.npy'), 1: np.load('embeddings.npy')}
+for k in [0, 1]:
+  embeddings[k] = np.divide(embeddings[k], np.sqrt(np.sum(embeddings[k]**2, axis=1, keepdims=True)))
+  
+def compute_text_embeddings(list_of_strings):
+    inputs = processor(text=list_of_strings, return_tensors="pt", padding=True)
+    return model.get_text_features(**inputs)
+    
+def compute(query):
+    corpus = 'Unsplash'
+    n_results=1
+
+    text_embeddings = compute_text_embeddings([query]).detach().numpy()
+    k = 0 if corpus == 'Unsplash' else 1
+    results = np.argsort((embeddings[k]@text_embeddings.T)[:, 0])[-1:-n_results-1:-1]
+    paths = [df[k].iloc[i]['path'] for i in results]
+    print(paths)
+    return paths
+
+title = "Draw to Search"
+iface = gr.Interface(
+  fn=predict, 
+  inputs=[gr.inputs.Textbox(label="text", lines=3)],
+  outputs='text',
+  title=title,
+  examples=[["Sunset"]]
+)
+iface.launch(debug=True)
+