Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+import torch
+from transformers import CLIPProcessor, CLIPModel
+from sklearn.metrics.pairwise import cosine_similarity
+from datasets import load_dataset
+
+# Load model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_name = "openai/clip-vit-base-patch32"
+model = CLIPModel.from_pretrained(model_name).to(device)
+processor = CLIPProcessor.from_pretrained(model_name)
+
+# Load embeddings
+df = pd.read_parquet("nature_embeddings.parquet")
+EMBEDDINGS_MATRIX = np.array(df['embedding'].tolist())
+CAPTIONS = df['caption'].tolist()
+
+# Load dataset
+dataset = load_dataset("mertcobanov/nature-dataset")
+sample_indices = list(range(5000))
+sample_data = dataset['train'].select(sample_indices)
+
+def get_image_embedding(image):
+    inputs = processor(images=image, return_tensors="pt", padding=True).to(device)
+    with torch.no_grad():
+        outputs = model.vision_model(**inputs)
+        features = outputs.pooler_output
+        features = torch.nn.functional.normalize(features, p=2, dim=-1)
+    return features.cpu().numpy()
+
+def recommend(image):
+    query_embedding = get_image_embedding(image)
+    similarities = cosine_similarity(query_embedding, EMBEDDINGS_MATRIX)[0]
+    top_indices = np.argsort(similarities)[::-1]
+    top_indices = [idx for idx in top_indices if similarities[idx] < 0.9999][:3]
+    
+    results = []
+    for idx in top_indices:
+        img = sample_data[int(idx)]['image']
+        cap = CAPTIONS[idx]
+        sim = float(similarities[idx])
+        results.append((img, f"{cap} (similarity: {sim:.4f})"))
+    
+    return results[0][0], results[0][1], results[1][0], results[1][1], results[2][0], results[2][1]
+
+# Gradio interface
+with gr.Blocks(title="Nature Scene Recommender") as demo:
+    gr.Markdown("# 🌿 Nature Scene Recommender")
+    gr.Markdown("Upload a nature image and get the 3 most similar scenes!")
+    
+    with gr.Row():
+        input_image = gr.Image(type="pil", label="Upload Nature Image")
+        btn = gr.Button("Find Similar Scenes 🔍", variant="primary")
+    
+    gr.Markdown("### Top 3 Similar Scenes")
+    with gr.Row():
+        img1 = gr.Image(label="Match #1")
+        img2 = gr.Image(label="Match #2")
+        img3 = gr.Image(label="Match #3")
+    with gr.Row():
+        cap1 = gr.Textbox(label="Caption #1")
+        cap2 = gr.Textbox(label="Caption #2")
+        cap3 = gr.Textbox(label="Caption #3")
+    
+    btn.click(fn=recommend, inputs=input_image, 
+              outputs=[img1, cap1, img2, cap2, img3, cap3])
+
+demo.launch()