Update app.py

app.py

@@ -692,7 +692,7 @@ demo = gr.TabbedInterface(
 
 demo.launch()
 '''
-'''
+
 import gradio as gr
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 import torch
@@ -859,189 +859,9 @@ demo = gr.TabbedInterface(
 )
 
 demo.launch()
-'''
-import gradio as gr
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-import torch
-from scipy.special import softmax
-import praw
-import os
-import pytesseract
-from PIL import Image
-import cv2
-import numpy as np
-import re
-import matplotlib.pyplot as plt
-import pandas as pd
-from langdetect import detect
-
-# Install tesseract OCR (only runs once in Hugging Face Spaces)
-os.system("apt-get update && apt-get install -y tesseract-ocr")
-
-# Load main lightweight model (English)
-main_model_name = "distilbert-base-uncased-finetuned-sst-2-english"
-model = AutoModelForSequenceClassification.from_pretrained(main_model_name)
-tokenizer = AutoTokenizer.from_pretrained(main_model_name)
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-# Load multilingual fallback model (global languages)
-multi_model_name = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
-multi_tokenizer = AutoTokenizer.from_pretrained(multi_model_name)
-multi_model = AutoModelForSequenceClassification.from_pretrained(multi_model_name).to(device)
-multi_labels = ['Negative', 'Neutral', 'Positive']
 
-# Load Hinglish/Hindi fallback model
-hinglish_model_name = "iisc-dsi/hinglish-sentiment-model"
-hinglish_tokenizer = AutoTokenizer.from_pretrained(hinglish_model_name)
-hinglish_model = AutoModelForSequenceClassification.from_pretrained(hinglish_model_name).to(device)
-hinglish_labels = ['Negative', 'Neutral', 'Positive']
 
-# Reddit API setup
-reddit = praw.Reddit(
-    client_id=os.getenv("REDDIT_CLIENT_ID"),
-    client_secret=os.getenv("REDDIT_CLIENT_SECRET"),
-    user_agent=os.getenv("REDDIT_USER_AGENT", "sentiment-classifier-ui-finalyear2025-shrish191")
-)
-
-def fetch_reddit_text(reddit_url):
-    try:
-        submission = reddit.submission(url=reddit_url)
-        return f"{submission.title}\n\n{submission.selftext}"
-    except Exception as e:
-        return f"Error fetching Reddit post: {str(e)}"
-
-def multilingual_classifier(text):
-    encoded_input = multi_tokenizer(text, return_tensors='pt', truncation=True, padding=True).to(device)
-    with torch.no_grad():
-        output = multi_model(**encoded_input)
-    scores = softmax(output.logits.cpu().numpy()[0])
-    return f"Prediction: {multi_labels[np.argmax(scores)]}"
-
-def hinglish_classifier(text):
-    encoded_input = hinglish_tokenizer(text, return_tensors='pt', truncation=True, padding=True).to(device)
-    with torch.no_grad():
-        output = hinglish_model(**encoded_input)
-    scores = softmax(output.logits.cpu().numpy()[0])
-    return f"Prediction: {hinglish_labels[np.argmax(scores)]}"
-
-def clean_ocr_text(text):
-    text = text.strip()
-    text = re.sub(r'\s+', ' ', text)
-    text = re.sub(r'[^\x00-\x7F]+', '', text)
-    return text
-
-def classify_sentiment(text_input, reddit_url, image):
-    if reddit_url.strip():
-        text = fetch_reddit_text(reddit_url)
-    elif image is not None:
-        try:
-            img_array = np.array(image)
-            gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
-            thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)
-            text = pytesseract.image_to_string(thresh)
-            text = clean_ocr_text(text)
-        except Exception as e:
-            return f"[!] OCR failed: {str(e)}"
-    elif text_input.strip():
-        text = text_input
-    else:
-        return "[!] Please enter some text, upload an image, or provide a Reddit URL."
-
-    if text.lower().startswith("error") or "Unable to extract" in text:
-        return f"[!] {text}"
-
-    text = ' '.join(text.split()[:400])
 
-    try:
-        lang = detect(text)
-        if lang == 'en':
-            inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
-            with torch.no_grad():
-                outputs = model(**inputs)
-                scores = softmax(outputs.logits.cpu().numpy()[0])
-            labels = ['Negative', 'Positive']
-            label = labels[scores.argmax()]
-        elif lang == 'hi':
-            label = hinglish_classifier(text).split(": ")[-1]
-        else:
-            label = multilingual_classifier(text).split(": ")[-1]
-
-        return f"🌐 Detected Language: {lang.upper()} | Prediction: {label}"
-    except Exception as e:
-        return f"[!] Prediction error: {str(e)}"
-
-def analyze_subreddit(subreddit_name):
-    try:
-        subreddit = reddit.subreddit(subreddit_name)
-        posts = list(subreddit.hot(limit=20))
-
-        sentiments = []
-        titles = []
-
-        for post in posts:
-            text = f"{post.title}\n{post.selftext}"
-            text = ' '.join(text.split()[:400])
-            try:
-                lang = detect(text)
-                if lang == 'en':
-                    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
-                    with torch.no_grad():
-                        outputs = model(**inputs)
-                        scores = softmax(outputs.logits.cpu().numpy()[0])
-                    labels = ['Negative', 'Positive']
-                    sentiment = labels[scores.argmax()]
-                elif lang == 'hi':
-                    sentiment = hinglish_classifier(text).split(": ")[-1]
-                else:
-                    sentiment = multilingual_classifier(text).split(": ")[-1]
-            except:
-                sentiment = "Error"
-            sentiments.append(sentiment)
-            titles.append(post.title)
-
-        df = pd.DataFrame({"Title": titles, "Sentiment": sentiments})
-        sentiment_counts = df["Sentiment"].value_counts()
-
-        fig, ax = plt.subplots()
-        sentiment_counts.plot(kind="bar", ax=ax)
-        ax.set_title(f"Sentiment Distribution in r/{subreddit_name}")
-        ax.set_xlabel("Sentiment")
-        ax.set_ylabel("Number of Posts")
-
-        return fig, df
-    except Exception as e:
-        return f"[!] Error: {str(e)}", pd.DataFrame()
-
-main_interface = gr.Interface(
-    fn=classify_sentiment,
-    inputs=[
-        gr.Textbox(label="Text Input", placeholder="Paste content here...", lines=4),
-        gr.Textbox(label="Reddit Post URL", placeholder="Optional", lines=1),
-        gr.Image(label="Upload Image (optional)", type="pil")
-    ],
-    outputs="text",
-    title="Sentiment Analyzer",
-    description="🔍 Analyze sentiment of any text, Reddit post URL, or image content."
-)
-
-subreddit_interface = gr.Interface(
-    fn=analyze_subreddit,
-    inputs=gr.Textbox(label="Subreddit Name", placeholder="e.g., AskReddit"),
-    outputs=[
-        gr.Plot(label="Sentiment Distribution"),
-        gr.Dataframe(label="Post Titles and Sentiments", wrap=True)
-    ],
-    title="Subreddit Sentiment Analysis",
-    description="📊 Analyze top 20 posts of any subreddit."
-)
-
-demo = gr.TabbedInterface(
-    interface_list=[main_interface, subreddit_interface],
-    tab_names=["General Sentiment Analysis", "Subreddit Analysis"]
-)
-
-demo.launch()