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Update app.py
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app.py
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import gradio as gr
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import os
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import time
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from collections import defaultdict
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token = os.environ["TOKEN"]
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model = os.environ["MODEL"]
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request_log = defaultdict(list)
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current_time = time.time()
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request_log[
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return "Rate limit exceeded. Please try again later."
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request_log[
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# Load the model from Hugging Face Spaces
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demo = gr.load(model, src="spaces", token=token)
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import gradio as gr
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from flask import Flask, request
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import os
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import time
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from collections import defaultdict
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token = os.environ["TOKEN"]
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model = os.environ["MODEL"]
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# Create Flask app
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app = Flask(__name__)
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# Rate-limiting configuration
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RATE_LIMIT_WINDOW = 60 # Time window in seconds
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RATE_LIMIT_REQUESTS = 5 # Max requests per user per window
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# Dictionary to store request logs
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request_log = defaultdict(list)
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# Function to apply rate limiting
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def rate_limiter():
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user_ip = request.remote_addr # Retrieve the user's IP address
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current_time = time.time()
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# Clean up old requests outside the rate limit window
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request_log[user_ip] = [
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timestamp for timestamp in request_log[user_ip]
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if current_time - timestamp < RATE_LIMIT_WINDOW
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]
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# Check if the user exceeded the allowed requests
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if len(request_log[user_ip]) >= RATE_LIMIT_REQUESTS:
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return "❌ Rate limit exceeded. Please wait and try again later."
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# Log the current request timestamp
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request_log[user_ip].append(current_time)
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return None # Indicate no rate limit violation
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# Load the Gradio model from Hugging Face Spaces
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demo = gr.load(model, src="spaces", token=token)
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# Route to serve the Gradio interface with rate limiting
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@app.route("/", methods=["GET", "POST"])
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def gradio_interface():
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# Apply rate limiting
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rate_limit_message = rate_limiter()
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if rate_limit_message:
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return rate_limit_message # Return rate limit error if exceeded
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# Launch Gradio app
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return demo.launch(prevent_thread_lock=True)
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if __name__ == "__main__":
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app.run(host="0.0.0.0", port=7860) # Run the Flask app
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