RGGH/Faiss_Flask.py

## Faiss_Flask.py
from flask import Flask, request, jsonify, render_template_string
from transformers import AutoTokenizer, AutoModel
import numpy as np
import faiss
import torch

app = Flask(__name__)

# Initialize the model and tokenizer
model_name = 'bert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

def get_embedding(text: str) -> np.ndarray:
    """Generate embedding for a given text using a pre-trained model."""
    inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
    embeddings = outputs.last_hidden_state.mean(dim=1)
    return embeddings.numpy()

# Sample texts for demonstration
sample_texts = [
    "The quick brown fox jumps over the lazy dog.",
    "Artificial intelligence is transforming industries.",
    "FAISS is a library for efficient similarity search.",
    "Natural language processing enables machines to understand text.",
    "Transformers models are state-of-the-art for NLP tasks."
]
embeddings = np.vstack([get_embedding(text) for text in sample_texts])
index = faiss.IndexFlatL2(embeddings.shape[1])
index.add(embeddings)

@app.route('/', methods=['GET', 'POST'])
def home():
    if request.method == 'POST':
        query_text = request.form.get('text')
        if not query_text:
            return render_template_string(HTML_TEMPLATE, results=[], error="No text provided")

        query_embedding = get_embedding(query_text)
        k = 3  # Number of nearest neighbors
        distances, indices = index.search(query_embedding, k)

        results = [{"text": sample_texts[idx], "distance": float(dist)} for idx, dist in zip(indices[0], distances[0])]
        return render_template_string(HTML_TEMPLATE, results=results, error=None)

    return render_template_string(HTML_TEMPLATE, results=[], error=None)

HTML_TEMPLATE = """
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Text Similarity Search</title>
    <!-- Bootstrap CSS CDN -->
    <link href="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css" rel="stylesheet">
</head>
<body>
    <div class="container mt-4">
        <h1 class="mb-4">Text Similarity Search</h1>
        <form method="post" class="mb-4">
            <div class="form-group">
                <label for="text">Enter text:</label>
                <input type="text" id="text" name="text" class="form-control" required>
            </div>
            <button type="submit" class="btn btn-primary">Search</button>
        </form>
        {% if error %}
            <div class="alert alert-danger" role="alert">
                {{ error }}
            </div>
        {% endif %}
        {% if results %}
            <h2>Results:</h2>
            <ul class="list-group">
            {% for result in results %}
                <li class="list-group-item"><strong>{{ result.text }}</strong> - Distance: {{ result.distance }}</li>
            {% endfor %}
            </ul>
        {% endif %}
    </div>
    <!-- Bootstrap JS and dependencies -->
    <script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.10.2/dist/umd/popper.min.js" integrity="sha384-7+zCNj/IqJ95wo16oMtfsKbZ9ccEh31eOz1HGyDuCQ6wgnyJNSYdrPa03rtR1zdB" crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/js/bootstrap.min.js" integrity="sha384-QJHtvGhmr9XOIpI6YVutG+2QOK9T+ZnN4kzFN1RtK3zEFEIsxhlmWl5/YESvpZ13" crossorigin="anonymous"></script>
</body>
</html>
"""
if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8000)
	from flask import Flask, request, jsonify, render_template_string
	from transformers import AutoTokenizer, AutoModel
	import numpy as np
	import faiss
	import torch

	app = Flask(__name__)

	# Initialize the model and tokenizer
	model_name = 'bert-base-uncased'
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModel.from_pretrained(model_name)

	def get_embedding(text: str) -> np.ndarray:
	"""Generate embedding for a given text using a pre-trained model."""
	inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True)
	with torch.no_grad():
	outputs = model(**inputs)
	embeddings = outputs.last_hidden_state.mean(dim=1)
	return embeddings.numpy()

	# Sample texts for demonstration
	sample_texts = [
	"The quick brown fox jumps over the lazy dog.",
	"Artificial intelligence is transforming industries.",
	"FAISS is a library for efficient similarity search.",
	"Natural language processing enables machines to understand text.",
	"Transformers models are state-of-the-art for NLP tasks."
	]
	embeddings = np.vstack([get_embedding(text) for text in sample_texts])
	index = faiss.IndexFlatL2(embeddings.shape[1])
	index.add(embeddings)

	@app.route('/', methods=['GET', 'POST'])
	def home():
	if request.method == 'POST':
	query_text = request.form.get('text')
	if not query_text:
	return render_template_string(HTML_TEMPLATE, results=[], error="No text provided")

	query_embedding = get_embedding(query_text)
	k = 3 # Number of nearest neighbors
	distances, indices = index.search(query_embedding, k)

	results = [{"text": sample_texts[idx], "distance": float(dist)} for idx, dist in zip(indices[0], distances[0])]
	return render_template_string(HTML_TEMPLATE, results=results, error=None)

	return render_template_string(HTML_TEMPLATE, results=[], error=None)

	HTML_TEMPLATE = """
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>Text Similarity Search</title>
	<!-- Bootstrap CSS CDN -->
	<link href="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css" rel="stylesheet">
	</head>
	<body>
	<div class="container mt-4">
	<h1 class="mb-4">Text Similarity Search</h1>
	<form method="post" class="mb-4">
	<div class="form-group">
	<label for="text">Enter text:</label>
	<input type="text" id="text" name="text" class="form-control" required>
	</div>
	<button type="submit" class="btn btn-primary">Search</button>
	</form>
	{% if error %}
	<div class="alert alert-danger" role="alert">
	{{ error }}
	</div>
	{% endif %}
	{% if results %}
	<h2>Results:</h2>
	<ul class="list-group">
	{% for result in results %}
	<li class="list-group-item"><strong>{{ result.text }}</strong> - Distance: {{ result.distance }}</li>
	{% endfor %}
	</ul>
	{% endif %}
	</div>
	<!-- Bootstrap JS and dependencies -->
	<script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.10.2/dist/umd/popper.min.js" integrity="sha384-7+zCNj/IqJ95wo16oMtfsKbZ9ccEh31eOz1HGyDuCQ6wgnyJNSYdrPa03rtR1zdB" crossorigin="anonymous"></script>
	<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/js/bootstrap.min.js" integrity="sha384-QJHtvGhmr9XOIpI6YVutG+2QOK9T+ZnN4kzFN1RtK3zEFEIsxhlmWl5/YESvpZ13" crossorigin="anonymous"></script>
	</body>
	</html>
	"""
	if __name__ == '__main__':
	app.run(host='0.0.0.0', port=8000)