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