Delete app.py

app.py

@@ -1,227 +0,0 @@
-import gradio as gr
-import cloudpickle
-import codecs
-import string
-from bnltk.tokenize import Tokenizers
-
-# Global variables to store loaded models and components
-model = None
-tfidf_vectorizer = None
-tokenizer = None
-bangla_stopwords = None
-punctuation_marks = None
-
-def load_models_and_components():
-    """Load the saved model, vectorizer, and preprocessing components"""
-    global model, tfidf_vectorizer, tokenizer, bangla_stopwords, punctuation_marks
-    
-    try:
-        # Load the SVM Optimized model
-        with open('model.pkl', 'rb') as f:
-            model = cloudpickle.load(f)
-        
-        # Load the TF-IDF Vectorizer
-        with open('tfidf_VECt.pkl', 'rb') as f:
-            tfidf_vectorizer = cloudpickle.load(f)
-        
-        # Initialize tokenizer
-        tokenizer = Tokenizers()
-        
-        # Load stopwords
-        stopwords_list = "stopwords.txt"
-        bangla_stopwords = codecs.open(stopwords_list, 'r', encoding='utf-8').read().split()
-        
-        # Define punctuation marks
-        punctuation_marks = set(string.punctuation)
-        
-        return "Models and components loaded successfully!"
-        
-    except Exception as e:
-        return f"Error loading models: {str(e)}"
-
-def preprocess_text(text):
-    """Preprocess the input text similar to training data preprocessing"""
-    # Tokenize the sentence
-    words = tokenizer.bn_word_tokenizer(text)
-    
-    # Exclude punctuation marks
-    words_no_punct = [word for word in words if word not in punctuation_marks]
-    
-    # Exclude stopwords
-    words_clean = [word for word in words_no_punct if word not in bangla_stopwords]
-    
-    # Join words back into a string
-    return ' '.join(words_clean)
-
-def predict_sentiment(input_text):
-    """Predict sentiment for the input text"""
-    if not input_text.strip():
-        return "Please enter some text to analyze.", ""
-    
-    if model is None or tfidf_vectorizer is None:
-        return "Models not loaded. Please load models first.", ""
-    
-    try:
-        # Preprocess the input text
-        processed_text = preprocess_text(input_text)
-        
-        if not processed_text.strip():
-            return "After preprocessing, no valid words found. Please try different text.", ""
-        
-        # Transform using the loaded TF-IDF vectorizer
-        transformed_input = tfidf_vectorizer.transform([processed_text])
-        
-        # Predict using the loaded model
-        prediction = model.predict(transformed_input)[0]
-        
-        # Get prediction probability for confidence score
-        prediction_proba = model.predict_proba(transformed_input)[0]
-        confidence = max(prediction_proba) * 100
-        
-        # Determine sentiment
-        sentiment = "Positive 😊" if prediction == 1 else "Negative 😞"
-        
-        # Create detailed result
-        result = f"**Sentiment:** {sentiment}\n**Confidence:** {confidence:.2f}%"
-        
-        # Additional info
-        details = f"**Processed Text:** {processed_text}\n**Raw Prediction:** {prediction}\n**Probabilities:** Negative: {prediction_proba[0]:.3f}, Positive: {prediction_proba[1]:.3f}"
-        
-        return result, details
-        
-    except Exception as e:
-        return f"Error during prediction: {str(e)}", ""
-
-def create_gradio_interface():
-    """Create and configure the Gradio interface"""
-    
-    # Custom CSS for better styling
-    css = """
-    .gradio-container {
-        font-family: 'Arial', sans-serif;
-    }
-    .main-header {
-        text-align: center;
-        color: #2d3748;
-        margin-bottom: 20px;
-    }
-    .prediction-box {
-        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-        color: white;
-        padding: 15px;
-        border-radius: 10px;
-        margin: 10px 0;
-    }
-    """
-    
-    with gr.Blocks(css=css, title="Bengali Sentiment Analysis", theme=gr.themes.Ocean()) as demo:
-        gr.HTML("""
-        <div class="main-header">
-            <h1>🇧🇩 Bengali Sentiment Analysis</h1>
-            <p>Analyze the sentiment of Bengali text using machine learning</p>
-        </div>
-        """)
-        
-        with gr.Row():
-            with gr.Column(scale=2):
-                # Input section
-                gr.Markdown("### 📝 Enter Bengali Text")
-                input_text = gr.Textbox(
-                    label="Bengali Text",
-                    placeholder="এখানে বাংলা টেক্সট লিখুন... (Enter Bengali text here...)",
-                    lines=4,
-                    max_lines=8
-                )
-                
-                with gr.Row():
-                    predict_btn = gr.Button("🔍 Analyze Sentiment", variant="primary", size="lg")
-                    clear_btn = gr.Button("🗑️ Clear", variant="secondary")
-                
-                # Load models button
-                gr.Markdown("### ⚙️ Model Management")
-                load_btn = gr.Button("📥 Load Models", variant="secondary")
-                load_status = gr.Textbox(label="Load Status", interactive=False)
-            
-            with gr.Column(scale=2):
-                # Output section
-                gr.Markdown("### 📊 Results")
-                output_sentiment = gr.Markdown(label="Sentiment Analysis Result")
-                output_details = gr.Textbox(
-                    label="Analysis Details",
-                    lines=6,
-                    interactive=False
-                )
-        
-        # Examples section
-        gr.Markdown("### 💡 Example Texts to Try")
-        gr.Examples(
-            examples=[
-                ["এই পণ্যটি অসাধারণ! আমি খুবই সন্তুষ্ট।"],
-                ["এই পণ্যটি কাজ করছে না। খুবই খারাপ।"],
-                ["দারুণ সার্ভিস! দ্রুত ডেলিভারি পেয়েছি।"],
-                ["প্রোডাক্ট কোয়ালিটি ভালো না। টাকার অপচয়।"],
-                ["চমৎকার অভিজ্ঞতা! আবার কিনব।"]
-            ],
-            inputs=[input_text],
-            label="Click on any example to try it"
-        )
-        
-        # Event handlers
-        predict_btn.click(
-            fn=predict_sentiment,
-            inputs=[input_text],
-            outputs=[output_sentiment, output_details]
-        )
-        
-        clear_btn.click(
-            fn=lambda: ("", "", ""),
-            outputs=[input_text, output_sentiment, output_details]
-        )
-        
-        load_btn.click(
-            fn=load_models_and_components,
-            outputs=[load_status]
-        )
-        
-        # Footer
-        gr.HTML("""
-        <div style="text-align: center; margin-top: 30px; padding: 20px; background-color: #f8f9fa; border-radius: 10px;">
-            <p><strong>Bengali Sentiment Analysis App</strong></p>
-            <p>Powered by SVM with TF-IDF features | Built with Gradio</p>
-            <p><em>Load the models first, then enter Bengali text to analyze sentiment</em></p>
-            <p><a href="https://drive.google.com/file/d/1VnvNSO2q-qd7SJya3mX7Er2U_5hEyBVl/view?usp=sharing" target="_blank" style="color: #667eea; text-decoration: none; font-weight: bold;">📄 Paper Link</a></p>
-        </div>
-        """)
-    
-    return demo
-
-def main():
-    """Main function to run the Gradio app"""
-    print("Starting Bengali Sentiment Analysis App...")
-    print("Make sure you have the following files in the specified paths:")
-    print("- model.pkl")
-    print("- tfidf_VECt.pkl") 
-    print("- stopwords.txt")
-    
-    # Create and launch the interface
-    demo = create_gradio_interface()
-    
-    # Launch the app
-    demo.launch(
-        share=True,  # Creates a public link
-        inbrowser=True,  # Opens in browser automatically
-        server_name="0.0.0.0",  # Makes it accessible from any IP
-        server_port=7860,  # Port number
-        show_error=True  # Shows detailed error messages
-    )
-
-if __name__ == "__main__":
-    # Install required packages if not already installed
-    try:
-        import gradio
-    except ImportError:
-        print("Installing Gradio...")
-        import subprocess
-        subprocess.check_call(["pip", "install", "gradio"])
-    
-    main()