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 = 20  # Maximum requests per time window
TIME_WINDOW = 3600   # Time window in seconds (1 hour)

def get_real_ip(request: gr.Request):
    """Extract real IP address using x-forwarded-for header or fallback"""
    if not request:
        return "unknown"
    
    forwarded = request.headers.get("x-forwarded-for")
    if forwarded:
        ip = forwarded.split(",")[0].strip()  # First IP in the list is the client's
    else:
        ip = request.client.host  # fallback
    return ip

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 = get_real_ip(request)
    now = time.time()
    
    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]))
        time_remaining_minutes = round(time_remaining / 60, 1)      
        time_window_minutes = round(TIME_WINDOW / 60, 1)
        return False, f"Rate limit exceeded. You can make {MAX_REQUESTS} requests per {time_window_minutes} minutes. Try again in {time_remaining_minutes} minutes."
    
    
    # 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("""
    <div style='text-align: center; margin-bottom: 24px;'>
        <h1 style='font-size:2.2em; margin-bottom: 0.2em;'>🧩 Generate vector embeddings</h1>
        <p style='font-size:1.2em; margin-top: 0;'>Generate vector embeddings from various content types.</p>
        <p style='font-size:1em; margin-top: 0.5em;'>For more details and API usage, see the <a href='https://jigsawstack.com/docs/api-reference/ai/embedding?slug=docs&slug=api-reference&slug=ai&slug=embedding' target='_blank'>documentation</a>.</p>
    </div>
    """)
    
    with gr.Row():
        with gr.Column():
            gr.Markdown("#### Content Input")
            input_type = gr.Radio([
                "Text",
                "URL"
            ], value="Text", label="Select Input Type")
            
            text_content = gr.Textbox(
                label="Text Content",
                placeholder="Enter the text content to generate embeddings for...",
                visible=True,
                lines=5
            )
            url = gr.Textbox(
                label="URL",
                placeholder="Enter the URL of the resource...",
                visible=False
            )
            
            content_type = gr.Dropdown(
                choices=["text", "text-other", "image", "audio", "pdf"],
                value="text",
                label="Content Type",
                info="Select the type of content being processed"
            )
            
            token_overflow_mode = gr.Radio(
                choices=["error", "truncate"],
                value="error",
                label="Token Overflow Mode",
                info="How to handle input that exceeds token limits"
            )
            
            generate_btn = gr.Button("Generate Embedding", variant="primary")
            
        with gr.Column():
            gr.Markdown("#### Embedding Result")
            status_message = gr.Textbox(label="Status", interactive=False)
            embedding_result = gr.Textbox(
                label="Vector Embedding",
                interactive=False,
                lines=15,
                max_lines=25
            )
    
    def toggle_inputs(selected):
        if selected == "Text":
            return gr.update(visible=True), gr.update(visible=False)
        else:  # URL
            return gr.update(visible=False), gr.update(visible=True)
    
    input_type.change(toggle_inputs, inputs=[input_type], outputs=[text_content, url])
    
    generate_btn.click(
        generate_embedding,
        inputs=[input_type, text_content, url, content_type, token_overflow_mode],
        outputs=[status_message, embedding_result]
    )

demo.launch()