import gradio as gr
import requests
import json
import os
import time
from collections import defaultdict
from PIL import Image
import io
import pandas as pd

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()

    # 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]))
        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 analyze_sentiment(text, request: gr.Request):
    rate_limit_ok, rate_limit_msg = check_rate_limit(request)
    if not rate_limit_ok:
        return f"❌ {rate_limit_msg}", None, None, None, None
    
    if not text or not text.strip():
        return "Error: Text input is required.", None, None, None, None

    try:
        response = requests.post(
            f"{BASE_URL}/ai/sentiment",
            headers=headers,
            json={"text": text.strip()}
        )
        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, None, None, None, None

        sentiment_data = result.get("sentiment", {})
        
        overall_emotion = sentiment_data.get("emotion", "N/A")
        overall_sentiment = sentiment_data.get("sentiment", "N/A")
        overall_score = sentiment_data.get("score", "N/A")
        
        sentences = sentiment_data.get("sentences", [])
        
        if sentences:
            sentence_df = pd.DataFrame(sentences)
            sentence_df = sentence_df[['text', 'emotion', 'sentiment', 'score']]
            sentence_df.rename(columns={'text': 'Sentence', 'emotion': 'Emotion', 'sentiment': 'Sentiment', 'score': 'Score'}, inplace=True)
        else:
            sentence_df = pd.DataFrame(columns=['Sentence', 'Emotion', 'Sentiment', 'Score'])
            
        status_message = "✅ Sentiment analysis complete."
        
        return status_message, overall_emotion, overall_sentiment, str(overall_score), sentence_df

    except requests.exceptions.RequestException as e:
        return f"Request failed: {str(e)}", None, None, None, None
    except Exception as e:
        return f"An unexpected error occurred: {str(e)}", None, None, None, None


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;'>🧩 Analyze Sentiment</h1>
            <p style='font-size:1.2em; margin-top: 0;'>Perform line-by-line sentiment analysis on any text with detailed emotion detection.</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/sentiment' target='_blank'>documentation</a>.</p>
        </div>
        """)
    with gr.Row():
            with gr.Column():
                gr.Markdown("#### Input Text")
                sentiment_text = gr.Textbox(
                    label="Text to Analyze",
                    lines=8,
                    placeholder="Enter the text you want to analyze here..."
                )
                sentiment_btn = gr.Button("Analyze Sentiment", variant="primary")
            
            with gr.Column():
                gr.Markdown("#### Overall Analysis")
                sentiment_status = gr.Textbox(label="Status", interactive=False)
                sentiment_emotion = gr.Textbox(label="Overall Emotion", interactive=False)
                sentiment_sentiment = gr.Textbox(label="Overall Sentiment", interactive=False)
                sentiment_score = gr.Textbox(label="Overall Score", interactive=False)

    gr.Markdown("#### Sentence-Level Breakdown")
    sentiment_sentences_df = gr.DataFrame(label="Sentence Analysis")

    sentiment_btn.click(
            analyze_sentiment,
            inputs=[sentiment_text],
            outputs=[sentiment_status, sentiment_emotion, sentiment_sentiment, sentiment_score, sentiment_sentences_df]
    )
demo.launch()