from PIL import Image, ImageDraw, ImageFont
import requests
import numpy as np
import gradio as gr
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.backends.backend_agg import FigureCanvasAgg
from io import BytesIO

import twitter

api = twitter.Api(consumer_key='IXpQTCB9vo9IGfOVAPBePE2Wi',
                    consumer_secret='qD1m4zaAiM6h2T7swBuWboORTXY4cA9eNcgDHlfFAuqKfNTiT3',
                    access_token_key='1529787212417605634-Io7LlY8AEdZEzOgiAYMb3hZyu9gsLL',
                    access_token_secret='QGo3eOn7xgPWHusmuP2JDZxkTMPJ51wtgO9wV3PY1b8wm')


def drawTweet(tweet,i):
    # Set the dimensions of the image
    width, height = 1000, 200
    
    # Create a blank image with a white background
    image = Image.new('RGBA', (width, height), 'white')

    # Get a drawing context
    draw = ImageDraw.Draw(image)

    # Set the font for the tweet text
    font = ImageFont.truetype('arial.ttf',  size=36, encoding='utf-16')

    user = tweet.user


    user_tag = user.screen_name
    text = tweet.text


    tweet_text = text

    words = tweet_text.split()
    # Insert a newline character after every 10 words
    formatted_string = ''
    for i, word in enumerate(words):
        formatted_string += word+' '
        if (i + 1) % 7 == 0:
            formatted_string += '\n'



    draw.multiline_text( (135,50), formatted_string , fill='black' , font=font, embedded_color=True)
    draw.text((135,10), f"@{user_tag}", fill='black',font=font)
    

    
    response = requests.get(user.profile_image_url_https)
    content = response.content

    f = BytesIO(content)

    avatar_size = (100, 100)
    avatar_image = Image.open(f)
    avatar_image = avatar_image.resize(avatar_size)
    image.paste(avatar_image, (10, 10))
    
    
    return image


def collect_tweets(topic):

    # Search for tweets matching the query
    tweets = api.GetSearch(term=f"{topic} -filter:retweets", lang='en', result_type="recent", count=100)
    
    # Filter out retweets
    
    tweets.sort(key=lambda tweet: tweet.favorite_count + tweet.retweet_count, reverse=True)

    images = []
    i = 1
    for tweet in tweets:
        img = drawTweet(tweet,i)
        images.append(img)
    
    sentiment_plot = sentiment_analysis(tweets,topic)

    return images,sentiment_plot

def sentiment_analysis(tweets,topic):
            
        tweet_procs = []
        for tweet in tweets:
                tweet_words = []
                for word in tweet.text.split(' '):
                        if word.startswith('@') and len(word) > 1:
                            word = '@user'
                        elif word.startswith('https'):
                            word = "http"
                        tweet_words.append(word)
                tweet_proc = " ".join(tweet_words)
                tweet_procs.append(tweet_proc)


        API_URL = "https://api-inference.huggingface.co/models/cardiffnlp/twitter-roberta-base-sentiment"
        headers = {"Authorization": "Bearer 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"}

        def query(payload):
            response = requests.post(API_URL, headers=headers, json=payload)
            return response.json()

        model_input = {
        "inputs": [tweet_procs[0]]
        }

        for i in range(1,len(tweets)):
            model_input["inputs"].append(tweet_procs[i])
            
        output = query({
        "inputs": model_input["inputs"]})

        negative = 0
        neutral = 0
        positive = 0

        for score in output:
            neg = 0
            neu = 0
            pos = 0
            for labels in score:
                if labels['label'] == 'LABEL_0':
                    neg += labels['score']
                elif labels['label'] == 'LABEL_1':
                    neu += labels['score'] 
                elif labels['label'] == 'LABEL_2':
                    pos += labels['score'] 
            sentiment = max(neg,neu,pos)
            if neg == sentiment:
                negative += 1
            elif neu == sentiment:
                neutral += 1
            elif pos == sentiment:
                positive += 1

        
        sns.barplot(x=["Negative Sentiment", "Neutral Sentiment", "Positive Sentiment"], y = [negative,neutral,positive])
        plt.title(f"Sentiment Analysis on Twitter regarding {topic}")
        canvas = FigureCanvasAgg(plt.gcf())
        canvas.draw()
        plot = np.array(canvas.buffer_rgba())
        return plot




# Create the Gradio app
app = gr.Interface(fn=collect_tweets, inputs=gr.Textbox(label="Enter a topic for tweets"),  outputs=[gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="50"), gr.Image(label="Sentiment Analysis Result")])

# Run the app
app.launch()