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'''import gradio as gr
from transformers import TFBertForSequenceClassification, BertTokenizer
import tensorflow as tf
# Load model and tokenizer from your HF model repo
model = TFBertForSequenceClassification.from_pretrained("shrish191/sentiment-bert")
tokenizer = BertTokenizer.from_pretrained("shrish191/sentiment-bert")
def classify_sentiment(text):
inputs = tokenizer(text, return_tensors="tf", padding=True, truncation=True)
predictions = model(inputs).logits
label = tf.argmax(predictions, axis=1).numpy()[0]
labels = {0: "Negative", 1: "Neutral", 2: "Positive"}
return labels[label]
demo = gr.Interface(fn=classify_sentiment,
inputs=gr.Textbox(placeholder="Enter a tweet..."),
outputs="text",
title="Tweet Sentiment Classifier",
description="Multilingual BERT-based Sentiment Analysis")
demo.launch()
'''
'''
import gradio as gr
from transformers import TFBertForSequenceClassification, BertTokenizer
import tensorflow as tf
# Load model and tokenizer from Hugging Face
model = TFBertForSequenceClassification.from_pretrained("shrish191/sentiment-bert")
tokenizer = BertTokenizer.from_pretrained("shrish191/sentiment-bert")
# Manually define the correct mapping
LABELS = {
0: "Neutral",
1: "Positive",
2: "Negative"
}
def classify_sentiment(text):
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})"
demo = gr.Interface(
fn=classify_sentiment,
inputs=gr.Textbox(placeholder="Type your tweet here..."),
outputs="text",
title="Sentiment Analysis on Tweets",
description="Multilingual BERT model fine-tuned for sentiment classification. Labels: Positive, Neutral, Negative."
)
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()
'''
'''import gradio as gr
from transformers import TFBertForSequenceClassification, BertTokenizer
import tensorflow as tf
import praw
import os
# Load model and tokenizer from Hugging Face
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 (credentials loaded securely from secrets)
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")
)
# Reddit post fetcher
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)}"
# Main sentiment function
def classify_sentiment(text_input, reddit_url):
if reddit_url.strip():
text = fetch_reddit_text(reddit_url)
elif text_input.strip():
text = text_input
else:
return "[!] Please enter some text or a Reddit post URL."
if text.lower().startswith("error") or "Unable to extract" in text:
return f"[!] {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 UI
demo = gr.Interface(
fn=classify_sentiment,
inputs=[
gr.Textbox(
label="Text Input (can be tweet or any content)",
placeholder="Paste tweet or type any content here...",
lines=4
),
gr.Textbox(
label="Reddit Post URL",
placeholder="Paste a Reddit post URL (optional)",
lines=1
),
],
outputs="text",
title="Sentiment Analyzer",
description="🔍 Paste any text (including tweet content) OR a Reddit post URL to analyze sentiment.\n\n💡 Tweet URLs are not supported directly due to platform restrictions. Please paste tweet content manually."
)
demo.launch()
'''
import gradio as gr
from transformers import TFBertForSequenceClassification, BertTokenizer, pipeline
import tensorflow as tf
import praw
import os
# Load main BERT model and tokenizer
model = TFBertForSequenceClassification.from_pretrained("shrish191/sentiment-bert")
tokenizer = BertTokenizer.from_pretrained("shrish191/sentiment-bert")
# Load fallback sentiment pipeline model
fallback_classifier = pipeline("text-classification", model="VinMir/GordonAI-sentiment_analysis")
# Label mapping for main model
LABELS = {
0: "Neutral",
1: "Positive",
2: "Negative"
}
# Reddit API setup (secure credentials from Hugging Face secrets)
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")
)
# Fetch content from Reddit URL
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 function
def classify_sentiment(text_input, reddit_url):
if reddit_url.strip():
text = fetch_reddit_text(reddit_url)
elif text_input.strip():
text = text_input
else:
return "[!] Please enter some text or a Reddit post URL."
if text.lower().startswith("error") or "Unable to extract" in text:
return f"[!] {text}"
try:
# Main BERT model prediction
inputs = tokenizer(text, return_tensors="tf", truncation=True, padding=True)
outputs = model(inputs)
probs = tf.nn.softmax(outputs.logits, axis=1)
confidence = float(tf.reduce_max(probs).numpy())
pred_label = tf.argmax(probs, axis=1).numpy()[0]
if confidence < 0.5:
# Use fallback model silently
fallback = fallback_classifier(text)[0]['label']
return f"Prediction: {fallback}"
return f"Prediction: {LABELS[pred_label]}"
except Exception as e:
return f"[!] Prediction error: {str(e)}"
# Gradio interface
demo = gr.Interface(
fn=classify_sentiment,
inputs=[
gr.Textbox(
label="Text Input (can be tweet or any content)",
placeholder="Paste tweet or type any content here...",
lines=4
),
gr.Textbox(
label="Reddit Post URL",
placeholder="Paste a Reddit post URL (optional)",
lines=1
),
],
outputs="text",
title="Sentiment Analyzer",
description="🔍 Paste any text (including tweet content) OR a Reddit post URL to analyze sentiment.\n\n💡 Tweet URLs are not supported directly due to platform restrictions. Please paste tweet content manually."
)
demo.launch()
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