import gradio as gr import requests import json import os import time from collections import defaultdict BASE_URL = "https://api.jigsawstack.com/v1" headers = { "x-api-key": os.getenv("JIGSAWSTACK_API_KEY") } # Rate limiting configuration request_times = defaultdict(list) MAX_REQUESTS = 10 # Maximum requests per time window TIME_WINDOW = 60 # Time window in seconds def check_rate_limit(request: gr.Request): """Check if the current request exceeds rate limits""" if not request: return True, "Rate limit check failed - no request info" ip = request.client.host now = time.time() # Clean up old timestamps outside the time window request_times[ip] = [t for t in request_times[ip] if now - t < TIME_WINDOW] # Check if rate limit exceeded if len(request_times[ip]) >= MAX_REQUESTS: time_remaining = int(TIME_WINDOW - (now - request_times[ip][0])) return False, f"Rate limit exceeded. You can make {MAX_REQUESTS} requests per {TIME_WINDOW} seconds. Try again in {time_remaining} seconds." # Add current request timestamp request_times[ip].append(now) return True, "" def generate_embedding(input_type, text_content, url, content_type, token_overflow_mode, request: gr.Request): """Generate embeddings using JigsawStack Embedding API with rate limiting""" # Check rate limit first rate_limit_ok, rate_limit_msg = check_rate_limit(request) if not rate_limit_ok: return rate_limit_msg, "" # Validate inputs if input_type == "Text" and not text_content: return "Error: Please provide text content.", "" if input_type == "URL" and not url: return "Error: Please provide a URL.", "" try: payload = { "type": content_type, "token_overflow_mode": token_overflow_mode } if input_type == "Text": payload["text"] = text_content.strip() elif input_type == "URL": payload["url"] = url.strip() response = requests.post( f"{BASE_URL}/embedding", headers=headers, json=payload ) response.raise_for_status() result = response.json() if not result.get("success"): error_msg = f"Error: API call failed - {result.get('message', 'Unknown error')}" return error_msg, "" embedding = result.get("embeddings", []) embedding_str = json.dumps(embedding, indent=2) return "Embedding generated successfully!", embedding_str except requests.exceptions.RequestException as e: return f"Request failed: {str(e)}", "" except Exception as e: return f"An unexpected error occurred: {str(e)}", "" with gr.Blocks() as demo: gr.Markdown("""
Generate vector embeddings from various content types including text, images, audio, and PDF files.
Supported types: text, text-other, image, audio, pdf
Rate limit: {MAX_REQUESTS} requests per {TIME_WINDOW} seconds per IP