Spaces:
Runtime error
Runtime error
File size: 7,332 Bytes
87a4736 a70cd5e f049331 a70cd5e 87a4736 ec6e4d2 87a4736 ff2634c 87a4736 ec6e4d2 87a4736 a70cd5e f049331 fbdc246 a70cd5e f049331 87a4736 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 |
import os
from googleapiclient.discovery import build
import pandas as pd
import numpy as np
from urllib.parse import urlparse, parse_qs
from transformers import pipeline
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification
from scipy.special import softmax
import gradio as gr
api_key = os.environ['api_key']
youtube_api = build('youtube','v3',developerKey=api_key)
#Get top 100 comments and make a dataframe
def get_comment_data(youtube_id):
request = youtube_api.commentThreads().list(part="snippet", videoId= youtube_id, maxResults=100, order="relevance", textFormat="plainText")
response = request.execute()
comments = [[comment['snippet']['topLevelComment']['snippet']['textDisplay'], comment['snippet']['topLevelComment']['snippet']['likeCount']] for comment in response['items']]
df = pd.DataFrame(comments, columns=['Comment_Text', 'Like_Count'])
return df
#Get title and thumbnail
def get_vid_details(youtube_link):
youtube_id = get_video_id(youtube_link)
request = youtube_api.videos().list(
part="snippet",
id= youtube_id
)
response = request.execute()
return response['items'][0]['snippet']['title'],response['items'][0]['snippet']['channelTitle'],response['items'][0]['snippet']['thumbnails']['high']['url']
#In case we ever want all comments
def get_all_comments(youtube_id):
comments = [[]]
next_page_token = None
while True:
request = youtube_api.commentThreads().list(part="snippet", videoId= youtube_id, maxResults=100, pageToken=next_page_token, order="relevance", textFormat="plainText")
response = request.execute()
for item in response['items']:
comments.append([item['snippet']['topLevelComment']['snippet']['textDisplay'], item['snippet']['topLevelComment']['snippet']['likeCount']])
if 'nextPageToken' in response:
next_page_token = response['nextPageToken']
else:
break
df = pd.DataFrame(comments, columns=['Comment_Text', 'Like_Count'])
return df
#Get all videos from a creator
def get_channel_videos(channel_id):
all_videos=[]
# Initial request to retrieve the channel's uploaded videos
request = youtube_api.search().list(
part='id',
channelId=channel_id,
maxResults=50 # Adjust as needed
)
while request is not None:
response = request.execute()
for item in response.get('items', []):
if item['id']['kind'] == 'youtube#video':
all_videos.append(item['id']['videoId'])
request = youtube_api.search().list_next(request, response)
return all_videos
#Pass a valid youtube video url or else function will not work
def get_video_id(url):
parsed_url = urlparse(url)
return parse_qs(parsed_url.query)['v'][0]
#Set up the model and tokenizer
MODEL = f"cardiffnlp/twitter-roberta-base-sentiment"
MODEL2 = "SamLowe/roberta-base-go_emotions"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
tokenizer2 = AutoTokenizer.from_pretrained(MODEL2)
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
model2 = AutoModelForSequenceClassification.from_pretrained(MODEL2)
classifier = pipeline(task="text-classification", model="SamLowe/roberta-base-go_emotions", top_k=None)
def generate_sentiments(df, progress=gr.Progress()):
#Set up lists to add to dataframe
pos_sent = []
neu_sent = []
neg_sent = []
feeling1 = []
feeling2 = []
feeling3 = []
for comment in progress.tqdm(df['Comment_Text'],desc="Analyzing Comments"):
#Encode the comment and run roberta on it
tokens = tokenizer.tokenize(comment)
if len(tokens) > 514:
tokens = tokens[:512]
comment = tokenizer.convert_tokens_to_string(tokens)
model_outputs = classifier(comment)
top_three_feelings = ""
#Top three sentiments, RoBERTa-based model
sentiment1 = list(model_outputs[0][0].values())[0]
sentiment2 = list(model_outputs[0][1].values())[0]
sentiment3 = list(model_outputs[0][2].values())[0]
feeling1.append(sentiment1)
feeling2.append(sentiment2)
feeling3.append(sentiment3)
encoded_comment = tokenizer(comment, return_tensors='pt')
output = model(**encoded_comment)
result = output[0][0].detach().numpy()
#Convert the numbers to be between 0 and 1 to do analysis with it
result = softmax(result)
#Add results to the lists
pos_sent.append(result[2])
neu_sent.append(result[1])
neg_sent.append(result[0])
#Add sentiments to the dataframe
new_df = df.copy()
new_df['Positive_Sentiment'] = pos_sent
new_df['Neural_Sentiment'] = neu_sent
new_df['Negative_Sentiment'] = neg_sent
new_df['Feeling 1'] = feeling1
new_df['Feeling 2'] = feeling2
new_df['Feeling 3'] = feeling3
return new_df
def addWeights(df,progress=gr.Progress()):
df1 = generate_sentiments(df,progress)
total_weights = df1['Like_Count'].sum()
df1['Weights'] = df1['Like_Count'] / total_weights
return df1
def three_most_common_words(words_list):
word_counts = {}
# Count the occurrences of each word
for word in words_list:
word_counts[word] = word_counts.get(word, 0) + 1
# Get the three most common words and their frequencies
most_common_words = sorted(word_counts.items(), key=lambda x: x[1], reverse=True)[:3]
return [item[0] for item in most_common_words]
def getWeightSentimentAll(df, progress=gr.Progress()):
df1 = addWeights(df,progress)
#Start at default 0.5, add the results of positive sentiment and subtract negative sentiment
total_sum = 0
for value1, value2 in zip(df1['Neural_Sentiment'], df1['Weights']):
total_sum += value1 * value2
weighted_avg = (df1['Positive_Sentiment'] * df1['Weights']).sum() * 0.2699488 + total_sum * 0.53425314 - (df1['Negative_Sentiment'] * df1['Weights']).sum() * 0.3747967 + 0.5
df['Weighted Average'] = weighted_avg
most_common_words = three_most_common_words(list(df1['Feeling 1']) + list(df1['Feeling 2']) + list(df1['Feeling 3']))
return str(int(weighted_avg*100)) + "%", *most_common_words
def rate(youtube_url, progress=gr.Progress()):
try:
vid_id = get_video_id(youtube_url)
vid_df = get_comment_data(vid_id)
vid_sent = getWeightSentimentAll(vid_df,progress)
return vid_sent
except:
raise gr.Error("Process failed. Ensure link is a valid YouTube URL")
with gr.Blocks() as app:
gr.Markdown("""
# Game Review Analysis Using Youtube
### Insert a YouTube URL to analyze the comments and get the population's review on the game!
"""
)
with gr.Tab("Video Rating"):
input = gr.Textbox(label="YouTube URL", placeholder = "Place link here")
output = gr.Textbox(label = "Community's Rating of the Game")
with gr.Row():
feeling1 = gr.Textbox(label="Top 3 Feelings")
feeling2 = gr.Textbox(label="")
feeling3 = gr.Textbox(label="")
rate_btn = gr.Button("Rate!")
rate_btn.click(fn=rate, inputs=input,outputs=[output,feeling1,feeling2,feeling3])
with gr.Tab("Video Details"):
input = gr.Textbox(label="Youtube URL", placeholder = "Place link here")
title = gr.Textbox(label="Title")
channel_name = gr.Textbox(label="Channel Name")
thumbnail = gr.Image(label="Thumbnail")
info_btn = gr.Button("Get Video Info!")
info_btn.click(fn=get_vid_details, inputs=input, outputs=[title,channel_name,thumbnail])
app.launch() |