app.py

# Importing Libraries
import os
import sys
import io
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['GRADIO_HOT_RELOAD'] = 'false'
os.environ['WRAPT_DISABLE_EXTENSIONS'] = 'true'
os.environ['PYTHONWARNINGS'] = 'ignore'

import re
import nltk
import pickle
import string
import warnings
import numpy as np
import gradio as gr
from keras.models import load_model
from keras.preprocessing.sequence import pad_sequences

# Suppress all warnings
warnings.filterwarnings('ignore')
warnings.simplefilter('ignore')

# Constants
MAX_LEN = 100
MODEL_PATH = "sentiment_analysis_best.keras"
TOKENIZER_PATH = "tokenizer.pkl"

nltk.download('stopwords', quiet=True)

# Expand common English contractions
def expand_contractions(text):
    contractions = {
        "i'm": "i am", "you're": "you are", "he's": "he is",
        "she's": "she is", "it's": "it is", "we're": "we are",
        "they're": "they are", "i've": "i have", "you've": "you have",
        "we've": "we have", "they've": "they have", "i'll": "i will",
        "you'll": "you will", "he'll": "he will", "she'll": "she will",
        "we'll": "we will", "they'll": "they will", "i'd": "i would",
        "you'd": "you would", "he'd": "he would", "she'd": "she would",
        "we'd": "we would", "they'd": "they would", "don't": "do not",
        "doesn't": "does not", "didn't": "did not", "can't": "cannot",
        "couldn't": "could not", "won't": "will not", "wouldn't": "would not",
        "shouldn't": "should not", "isn't": "is not", "aren't": "are not",
        "wasn't": "was not", "weren't": "were not", "hasn't": "has not",
        "haven't": "have not", "hadn't": "had not", "mightn't": "might not",
        "mustn't": "must not", "needn't": "need not", "shan't": "shall not"
    }
    for contraction, expansion in contractions.items():
        text = re.sub(r'\b' + contraction + r'\b', expansion, text, flags=re.IGNORECASE)
    return text

# Preprocessing Function
def preprocess(text):
    negations = {"not", "no", "nor", "never", "n't", "nobody", "nothing", "neither", "nowhere", "none"}
    important_words = {"am", "is", "are", "was", "were", "be", "been", "being"}
    
    try:
        from nltk.corpus import stopwords
        stop_words = set(stopwords.words("english")) - negations - important_words
    except:
        stop_words = set()
    
    text = text.lower()
    text = expand_contractions(text)
    text = re.sub(r"\d+", "", text)
    text = text.translate(str.maketrans('', '', string.punctuation))
    words = [w for w in text.split() if w not in stop_words or w in negations or w in important_words]
    
    return " ".join(words)

# Load Train Model and Tokenizer
def load_resources():
    try:
        model = load_model(MODEL_PATH)
        print(f"✓ Model loaded successfully from {MODEL_PATH}")
        
        with open(TOKENIZER_PATH, "rb") as f:
            tokenizer = pickle.load(f)
        print(f"✓ Tokenizer loaded successfully from {TOKENIZER_PATH}")
        
        return model, tokenizer
    except FileNotFoundError as e:
        print(f"✗ Error: Model or Tokenizer file not found!")
        print(f"  Make sure {MODEL_PATH} AND {TOKENIZER_PATH} are in the same directory.")
        raise e
    except Exception as e:
        print(f"✗ Error loading resources: {e}")
        raise e

# Load model and tokenizer globally
model, tokenizer = load_resources()

# Prediction Function
def predict_sentiment(text):
    if not text or not text.strip():
        return "⚠️ Neutral", "33.33%", "Please enter some text to analyze!"
    
    processed_text = preprocess(text)
    
    if not processed_text.strip():
        return "⚠️ Neutral", "33.33%", "Text is empty after preprocessing. Try adding more words."
    
    seq = tokenizer.texts_to_sequences([processed_text])
    padded = pad_sequences(seq, maxlen=MAX_LEN, padding='post')
    
    pred = model.predict(padded, verbose=0)
    label_idx = np.argmax(pred, axis=1)[0]
    confidence = pred[0][label_idx]
    
    labels = ["😞 Negative", "😊 Positive", "😐 Neutral"]
    sentiment = labels[label_idx]
    confidence_percentage = f"{confidence * 100:.2f}%"
    
    detailed_results = f"""
### 📊 Detailed Analysis:

**Original Text:** {text}

**Processed Text:** {processed_text}

**Prediction Probabilities:**
- 😞 Negative: {pred[0][0] * 100:.2f}%
- 😊 Positive: {pred[0][1] * 100:.2f}%
- 😐 Neutral: {pred[0][2] * 100:.2f}%

**Final Sentiment:** {sentiment}
**Confidence:** {confidence_percentage}
"""
    return sentiment, confidence_percentage, detailed_results

# GRADIO INTERFACE
def create_gradio_interface():
    """Create and configure Gradio interface"""
    
    examples = [
        ["I'm so happy with my purchase! Highly recommended!"],
        ["I don't like this at all. Very disappointing."],
        ["I absolutely love this product! It's amazing!"],
        ["This is the worst experience I've ever had."],
        ["Fantastic! Best decision I ever made!"],
        ["I'm not sure how I feel about this."],
        ["It's okay, nothing special really."],
        ["Amazing work! Best I've ever seen!"],
        ["This is the worst experience ever"],
        ["This is terrible and I hate it"],
        ["It works fine, no complaints."],
        ["Not bad, but could be better."],
        ["He is no good boy"],
        ["I'm doing great"],
        ["I'm not normal"],
        ["Both of you"],
        ["I am fine"],
        ["I am good"],
        ["I'm okay"]
    ]
    
    with gr.Blocks(title="Sentiment Analysis") as interface:
        gr.Markdown("""
        # 🎭 Sentiment Analysis - AI Powered
        ### Analyze the sentiment of your text using Deep Learning (LSTM Model)
        
        **Instructions:** Enter any text in English and the model will predict whether it's Positive, Negative, or Neutral.
        """)
        
        with gr.Row():
            with gr.Column(scale=1):
                text_input = gr.Textbox(
                    label="📝 Enter Your Text",
                    placeholder="Type your text here... (e.g., 'I love this product!')",
                    lines=5,
                    max_lines=10
                )
                
                with gr.Row():
                    analyze_btn = gr.Button("🔍 Analyze Sentiment", variant="primary", size="lg")
                    clear_btn = gr.ClearButton([text_input], value="🗑️ Clear", size="lg")
            
            with gr.Column(scale=1):
                sentiment_output = gr.Textbox(
                    label="🎯 Predicted Sentiment",
                    interactive=False
                )
                confidence_output = gr.Textbox(
                    label="📈 Confidence Score",
                    interactive=False
                )
        
        detailed_output = gr.Markdown(
            label="📊 Detailed Analysis",
            value="Results will appear here after analysis..."
        )
        
        gr.Markdown("### 💡 Try These Examples:")
        gr.Examples(
            examples=examples,
            inputs=text_input,
            outputs=[sentiment_output, confidence_output, detailed_output],
            fn=predict_sentiment,
            cache_examples=False
        )
        
        gr.Markdown("""
        ---
        **Model Information:**
        - Architecture: Bidirectional LSTM with Embedding Layer
        - Classes: Negative (0), Positive (1), Neutral (2)
        - Max Sequence Length: 100 tokens
        
        **Tips for Best Results:**
        - Use clear, complete sentences
        - The model works best with English text
        - Longer texts provide more context for accurate predictions
        """)
        
        analyze_btn.click(
            fn=predict_sentiment,
            inputs=text_input,
            outputs=[sentiment_output, confidence_output, detailed_output]
        )
        
        text_input.submit(
            fn=predict_sentiment,
            inputs=text_input,
            outputs=[sentiment_output, confidence_output, detailed_output]
        )
    
    return interface

# Context manager to suppress stderr temporarily
class SuppressStderr:
    def __enter__(self):
        self.original_stderr = sys.stderr
        sys.stderr = io.StringIO()
        return self
    
    def __exit__(self, exc_type, exc_val, exc_tb):
        sys.stderr = self.original_stderr

# MAIN EXECUTION
if __name__ == "__main__":
    print("\n" + "=" * 70)
    print("🚀 Starting Sentiment Analysis Gradio Interface...")
    print("=" * 70 + "\n")
    
    # Create interface
    interface = create_gradio_interface()
    
    # Launch with stderr suppression to hide asyncio warnings
    print("⏳ Launching server...")
    
    with SuppressStderr():
        # Launch configuration
        interface.launch(
            server_name="0.0.0.0",
            server_port=7860,
            share=False,
            show_error=False,
            ssr_mode=False,
            theme=gr.themes.Soft(),
            quiet=False,
            prevent_thread_lock=False
        )
    
    print("\n" + "=" * 70)
    print("✅ Interface is LIVE and ready to use!")
    print("  🌐 Local URL: http://localhost:7860")
    print("  ⚡ Server is running smoothly")
    print("  🛑 Press Ctrl+C to stop")
    print("=" * 70)