Update app.py

app.py

@@ -21,6 +21,7 @@ demo = gr.Interface(fn=classify_sentiment,
 
 demo.launch()
 '''
+'''
 import gradio as gr
 from transformers import TFBertForSequenceClassification, BertTokenizer
 import tensorflow as tf
@@ -53,5 +54,87 @@ demo = gr.Interface(
 )
 
 demo.launch()
+'''
+import gradio as gr
+from transformers import TFBertForSequenceClassification, BertTokenizer
+import tensorflow as tf
+import snscrape.modules.twitter as sntwitter
+import praw
+import os
+
+# Load model and tokenizer
+model = TFBertForSequenceClassification.from_pretrained("shrish191/sentiment-bert")
+tokenizer = BertTokenizer.from_pretrained("shrish191/sentiment-bert")
+
+# Label Mapping
+LABELS = {
+    0: "Neutral",
+    1: "Positive",
+    2: "Negative"
+}
+
+# Reddit API setup with environment variables
+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-script")
+)
+
+# Tweet text extractor
+def fetch_tweet_text(tweet_url):
+    try:
+        tweet_id = tweet_url.split("/")[-1]
+        for tweet in sntwitter.TwitterTweetScraper(tweet_id).get_items():
+            return tweet.content
+        return "Unable to extract tweet content."
+    except Exception as e:
+        return f"Error fetching tweet: {str(e)}"
+
+# Reddit post extractor
+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)}"
+
+# Sentiment classification logic
+def classify_sentiment(text_input, tweet_url, reddit_url):
+    if reddit_url.strip():
+        text = fetch_reddit_text(reddit_url)
+    elif tweet_url.strip():
+        text = fetch_tweet_text(tweet_url)
+    elif text_input.strip():
+        text = text_input
+    else:
+        return "[!] Please enter text or a post URL."
+
+    if text.lower().startswith("error") or "Unable to extract" in text:
+        return f"[!] Error: {text}"
+
+    try:
+        inputs = tokenizer(text, return_tensors="tf", truncation=True, padding=True)
+        outputs = model(inputs)
+        probs = tf.nn.softmax(outputs.logits, axis=1)
+        pred_label = tf.argmax(probs, axis=1).numpy()[0]
+        confidence = float(tf.reduce_max(probs).numpy())
+        return f"Prediction: {LABELS[pred_label]} (Confidence: {confidence:.2f})"
+    except Exception as e:
+        return f"[!] Prediction error: {str(e)}"
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=classify_sentiment,
+    inputs=[
+        gr.Textbox(label="Custom Text Input", placeholder="Type your tweet or message here..."),
+        gr.Textbox(label="Tweet URL", placeholder="Paste a tweet URL here (optional)"),
+        gr.Textbox(label="Reddit Post URL", placeholder="Paste a Reddit post URL here (optional)")
+    ],
+    outputs="text",
+    title="Multilingual Sentiment Analysis",
+    description="Analyze sentiment of text, tweets, or Reddit posts. Supports multiple languages using BERT!"
+)
+
+demo.launch()