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import os
import praw
import gradio as gr
from transformers import TextClassificationPipeline, AutoModelForSequenceClassification, AutoTokenizer
client_id = os.environ["client_id"]
client_secret = os.environ["client_secret"]
user_agent = os.environ["user_agent"]
reddit = praw.Reddit(client_id =client_id,
client_secret =client_secret, user_agent =user_agent)
model_name = "ProsusAI/finbert"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels = 3)
pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, max_length=64, truncation=True, padding = 'max_length')
def reddit_analysis(subreddit_name, num_posts):
local_score = 0
local_titles = []
subreddit = reddit.subreddit(subreddit_name)
if int(num_posts) > 16:
return "Number of posts should be less than 15"
else:
for post in subreddit.new(limit=int(num_posts)):
prediction = pipe(post.title)
local_titles.append(post.title)
if prediction[0]["label"] == "negative":
local_score-= prediction[0]["score"]
elif prediction[0]["label"] == "positive":
local_score+= prediction[0]["score"]
titles_string = "\n".join(local_titles)
return local_score, titles_string
#print(post.title)
#print(post.selftext)
total_score = 0
text_list = []
def manual_analysis(text):
global total_score
prediction = pipe(text)
text_list.append(text)
if prediction[0]["label"] == "negative":
total_score-= prediction[0]["score"]
elif prediction[0]["label"] == "positive":
total_score+= prediction[0]["score"]
return prediction, total_score
with gr.Blocks() as demo:
with gr.Tab("Seperate Analysis"):
first_title = """<p><h1 align="center" style="font-size: 24px;">Analyse texts manually</h1></p>"""
gr.HTML(first_title)
with gr.Row():
with gr.Column():
text = gr.Textbox(label="text")
analyse = gr.Button("Analyse")
with gr.Column():
label_score = gr.Textbox(label="Label/Score")
average_score = gr.Textbox(label="Average Score")
analyse.click(fn=manual_analysis, inputs=text, outputs=[label_score, average_score], api_name="Calc1")
with gr.Tab("Mass Analysis"):
second_title = """<p><h1 align="center" style="font-size: 24px;">Analyse latest posts from Reddit</h1></p>"""
gr.HTML(second_title)
with gr.Row():
with gr.Column():
subreddit_name = gr.Textbox(label="Subreddit Name")
num_post = gr.Textbox(label="Number of Posts")
analyse = gr.Button("Analyse")
with gr.Column():
average_score = gr.Textbox(label="Average Score")
tifu_titles = gr.Textbox(label="Tifu Titles")
analyse.click(fn=reddit_analysis, inputs=[subreddit_name, num_post], outputs=[average_score, tifu_titles], api_name="Calc2")
demo.launch() |