Create app.py

app.py

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+import gradio as gr
+import torch
+
+import torch.nn.functional as F
+from transformers import DistilBertTokenizer
+
+from PIL import Image
+import numpy as np
+import requests
+
+import clip_inferencing as inference
+
+device="cpu"
+valid_df = inference.load_df()
+image_embeddings = inference.load_image_embeddings()
+model = inference.load_model(model_path="model/best.pt")
+tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
+image_embeddings_n = F.normalize(image_embeddings, p=2, dim=-1)
+
+n=9
+image_filenames=valid_df['image'].values
+
+with gr.Blocks() as demo:
+    
+    def inference(query):
+        encoded_query = tokenizer([query])
+        batch = {
+                    key: torch.tensor(values).to(device)
+                    for key, values in encoded_query.items()
+                }
+        with torch.no_grad():
+            text_features = model.text_encoder(
+                input_ids=batch["input_ids"], attention_mask=batch["attention_mask"]
+            )
+            text_embeddings = model.text_projection(text_features)
+        
+        text_embeddings_n = F.normalize(text_embeddings, p=2, dim=-1)
+        dot_similarity = text_embeddings_n @ image_embeddings_n.T
+
+        values, indices = torch.topk(dot_similarity.squeeze(0), n * 5)
+        matches = [image_filenames[idx] for idx in indices[::5]]
+
+        resulting_images = []
+        for match in matches:
+            img_https_link = "https://raw.githubusercontent.com/bala1802/ERA_Session19/main/Images/" + match
+            resulting_images.append(np.array(Image.open(requests.get(img_https_link, stream=True).raw).convert('RGB')))
+
+            # resulting_images.append(np.array(Image.open(f"Images/{match}").convert('RGB')))
+        return resulting_images
+    
+    gr.Markdown(
+                """
+                    # CLIP Demo !!!
+                """
+                )
+    with gr.Column(variant="panel"):
+        with gr.Row():
+            text = gr.Textbox(
+                label="Enter your prompt",
+                max_lines=1,
+                placeholder="Extract the matching images ....",
+                container=False,
+            )
+            btn = gr.Button("Show Images", scale=0)
+
+        gallery = gr.Gallery(
+            label="Movies", show_label=False, elem_id="gallery"
+        , columns=[4], rows=[1], object_fit="contain", height="auto")
+    
+    btn.click(inference, text, gallery)
+
+if __name__ == "__main__":
+    demo.launch()