Create app.py

app.py

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+from PIL import Image, ImageDraw, ImageFont
+from textwrap import wrap
+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()
+
+