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Create app.py
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app.py
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# app.py
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import json
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import numpy as np
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import torch
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from flask import Flask, request, render_template_string
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from transformers import CLIPProcessor, CLIPModel
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import faiss
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app = Flask(__name__)
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# Global variables for the model, processor, FAISS index, and image metadata.
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model = None
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processor = None
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index = None
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image_embeddings = None
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image_metadata = None
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def load_model():
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global model, processor
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print("Loading CLIP model and processor...")
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model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
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processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
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def load_data():
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global image_embeddings, image_metadata, index
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print("Loading image embeddings and metadata...")
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# Load precomputed embeddings and metadata from a JSON file.
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with open("data/embeddings.json", "r") as f:
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data = json.load(f)
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# Each item in data should have an "embedding" key and a "url" (and optionally an "id")
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image_embeddings = np.array([d["embedding"] for d in data]).astype('float32')
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image_metadata = data
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# Build a FAISS index using L2 distance. The dimension 'd' must match the embedding size.
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d = image_embeddings.shape[1]
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index = faiss.IndexFlatL2(d)
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index.add(image_embeddings)
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print(f"FAISS index built with {index.ntotal} embeddings.")
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@app.route("/", methods=["GET", "POST"])
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def search():
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results_html = ""
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query = ""
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if request.method == "POST":
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query = request.form.get("query", "")
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if query:
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# Encode the text query using CLIP's text encoder.
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inputs = processor(text=[query], return_tensors="pt", padding=True)
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with torch.no_grad():
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text_features = model.get_text_features(**inputs)
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text_features = text_features.cpu().numpy().astype("float32")
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# Query the FAISS index for the top k similar images.
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k = 10 # number of results to return
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distances, indices = index.search(text_features, k)
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# Build HTML image elements for each result.
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results = []
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for idx in indices[0]:
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meta = image_metadata[idx]
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results.append(
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f'<div style="margin:10px;"><img src="{meta["url"]}" alt="Image {meta.get("id", "")}" style="max-width:200px;"><br>ID: {meta.get("id", "N/A")}</div>'
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)
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results_html = "".join(results)
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# Simple HTML form with results displayed below.
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html = f"""
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="UTF-8">
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<title>Image Search with CLIP & FAISS</title>
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</head>
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<body>
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<h1>Image Search</h1>
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<form method="post">
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<input type="text" name="query" placeholder="Enter search text" value="{query}" required>
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<input type="submit" value="Search">
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</form>
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<div style="display:flex; flex-wrap: wrap; margin-top:20px;">
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{results_html}
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</div>
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</body>
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</html>
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"""
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return render_template_string(html)
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if __name__ == "__main__":
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load_model()
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load_data()
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# Run the Flask app on the port expected by Hugging Face Spaces.
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app.run(host="0.0.0.0", port=8080)
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