Create app.py

app.py

@@ -0,0 +1,89 @@
+import praw
+import gradio as gr
+from transformers import TextClassificationPipeline, AutoModelForSequenceClassification, AutoTokenizer
+
+
+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)
+    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()