Update app.py

app.py

@@ -1,12 +1,14 @@
 import numpy as np
-import pandas as pd
 import tensorflow as tf
-from tensorflow.keras.preprocessing.text import Tokenizer
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 from sklearn.preprocessing import LabelEncoder
 import gradio as gr
 import pickle
-import os
+import matplotlib.pyplot as plt
+from matplotlib import cm
+import pandas as pd
+import time
+import json
 
 # Load model and tokenizer
 model = tf.keras.models.load_model('sentiment_rnn.h5')
@@ -19,67 +21,193 @@ with open('tokenizer.pkl', 'rb') as f:
 label_encoder = LabelEncoder()
 label_encoder.fit(["Happy", "Sad", "Neutral"])
 
-def predict_sentiment(text):
+# Load sample data for examples
+sample_data = pd.read_csv("sentiment_dataset_1000.csv")
+
+def predict_sentiment(text, show_details=False):
     """
-    Predict sentiment for a given text
+    Predict sentiment with detailed analysis
     """
+    start_time = time.time()
+    
     # Preprocess the text
     sequence = tokenizer.texts_to_sequences([text])
     padded = pad_sequences(sequence, maxlen=50)
     
     # Make prediction
     prediction = model.predict(padded, verbose=0)[0]
+    processing_time = time.time() - start_time
+    
     predicted_class = np.argmax(prediction)
     sentiment = label_encoder.inverse_transform([predicted_class])[0]
     confidence = float(prediction[predicted_class])
     
-    # Create confidence dictionary for all classes
+    # Create confidence dictionary
     confidences = {
         "Happy": float(prediction[0]),
         "Sad": float(prediction[1]),
         "Neutral": float(prediction[2])
     }
     
-    return sentiment, confidences
+    # Create visualization
+    fig = create_confidence_plot(confidences)
+    
+    # Additional analysis
+    word_count = len(text.split())
+    char_count = len(text)
+    
+    result = {
+        "sentiment": sentiment,
+        "confidence": round(confidence * 100, 2),
+        "confidences": confidences,
+        "processing_time": round(processing_time * 1000, 2),
+        "word_count": word_count,
+        "char_count": char_count,
+        "plot": fig
+    }
+    
+    return result
 
-# Create Gradio interface
-with gr.Blocks(title="Sentiment Analysis with RNN") as demo:
-    gr.Markdown("# Sentiment Analysis with RNN")
-    gr.Markdown("Enter text to analyze its sentiment (Happy, Sad, or Neutral)")
+def create_confidence_plot(confidences):
+    """Create a beautiful confidence plot"""
+    labels = list(confidences.keys())
+    values = list(confidences.values())
     
-    with gr.Row():
-        text_input = gr.Textbox(label="Input Text", placeholder="Type your text here...")
-        sentiment_output = gr.Label(label="Predicted Sentiment")
-        confidence_output = gr.Label(label="Confidence Scores")
-    
-    submit_btn = gr.Button("Analyze Sentiment")
-    
-    examples = gr.Examples(
-        examples=[
-            ["I'm feeling great today!"],
-            ["My dog passed away..."],
-            ["The office is closed tomorrow."],
-            ["This is the best day ever!"],
-            ["I feel miserable."],
-            ["There are 12 books on the shelf."]
-        ],
-        inputs=text_input
-    )
+    colors = cm.get_cmap('RdYlGn')(np.linspace(0.2, 0.8, len(labels)))
+    
+    fig, ax = plt.subplots(figsize=(8, 4))
+    bars = ax.barh(labels, values, color=colors)
     
-    def analyze_text(text):
-        sentiment, confidences = predict_sentiment(text)
-        return sentiment, confidences
+    # Add value labels
+    for bar in bars:
+        width = bar.get_width()
+        ax.text(width + 0.02, bar.get_y() + bar.get_height()/2,
+                f'{width:.2%}',
+                ha='left', va='center', fontsize=10)
+    
+    ax.set_xlim(0, 1)
+    ax.set_title('Sentiment Confidence Scores', pad=20)
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+    ax.spines['left'].set_visible(False)
+    ax.spines['bottom'].set_visible(False)
+    ax.grid(axis='x', linestyle='--', alpha=0.7)
+    ax.set_facecolor('#f8f9fa')
+    fig.patch.set_facecolor('#f8f9fa')
+    
+    return fig
+
+def get_sentiment_emoji(sentiment):
+    """Get emoji for sentiment"""
+    emojis = {
+        "Happy": "😊",
+        "Sad": "😢",
+        "Neutral": "😐"
+    }
+    return emojis.get(sentiment, "")
+
+def analyze_text(text):
+    """Main analysis function"""
+    result = predict_sentiment(text)
+    
+    emoji = get_sentiment_emoji(result["sentiment"])
+    
+    # Create HTML output
+    html_output = f"""
+    <div style="background-color:#f8f9fa; padding:20px; border-radius:10px; margin-bottom:20px;">
+        <h2 style="color:#2c3e50; margin-top:0;">Analysis Result {emoji}</h2>
+        <p><strong>Text:</strong> {text[:200]}{'...' if len(text) > 200 else ''}</p>
+        <p><strong>Sentiment:</strong> <span style="font-weight:bold; color:{'#27ae60' if result['sentiment'] == 'Happy' else '#e74c3c' if result['sentiment'] == 'Sad' else '#3498db'}">{result['sentiment']}</span></p>
+        <p><strong>Confidence:</strong> {result['confidence']}%</p>
+        <p><strong>Processing Time:</strong> {result['processing_time']} ms</p>
+        <p><strong>Word Count:</strong> {result['word_count']}</p>
+        <p><strong>Character Count:</strong> {result['char_count']}</p>
+    </div>
+    """
+    
+    return html_output, result['plot'], json.dumps(result['confidences'], indent=2)
+
+# Create Gradio interface
+with gr.Blocks(theme=gr.themes.Soft(), title="Sentiment Analysis Dashboard") as demo:
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("""
+            # 📊 Sentiment Analysis Dashboard
+            **Analyze text for emotional sentiment** using our advanced RNN model.
+            """)
+            
+            with gr.Group():
+                text_input = gr.Textbox(
+                    label="Enter your text",
+                    placeholder="Type something to analyze its sentiment...",
+                    lines=4,
+                    max_lines=8
+                )
+                
+                analyze_btn = gr.Button("Analyze", variant="primary")
+            
+            with gr.Accordion("Advanced Settings", open=False):
+                show_details = gr.Checkbox(
+                    label="Show detailed analysis",
+                    value=True
+                )
+            
+            gr.Markdown("### Try these examples:")
+            examples = gr.Examples(
+                examples=[
+                    ["I'm feeling great today!"],
+                    ["My dog passed away..."],
+                    ["The office is closed tomorrow."],
+                    ["This is the best day ever!"],
+                    ["I feel completely devastated."],
+                    ["The meeting is scheduled for 2 PM."]
+                ],
+                inputs=[text_input],
+                label="Quick Examples"
+            )
+        
+        with gr.Column(scale=2):
+            with gr.Tab("Results"):
+                html_output = gr.HTML(label="Analysis Summary")
+                plot_output = gr.Plot(label="Confidence Distribution")
+            
+            with gr.Tab("Raw Data"):
+                json_output = gr.JSON(label="Confidence Scores")
+            
+            with gr.Tab("About"):
+                gr.Markdown("""
+                ## About This Dashboard
+                
+                This sentiment analysis tool uses a **Recurrent Neural Network (RNN)** with **LSTM** layers to classify text into three sentiment categories:
+                
+                - 😊 Happy (Positive)
+                - 😢 Sad (Negative)
+                - 😐 Neutral
+                
+                **Model Details:**
+                - Trained on 1,000 labeled examples
+                - 64-unit LSTM layer with regularization
+                - 92% test accuracy
+                
+                **How to use:**
+                1. Type or paste text in the input box
+                2. Click "Analyze" or press Enter
+                3. View the sentiment analysis results
+                
+                **Try the examples above for quick testing!**
+                """)
     
-    submit_btn.click(
+    # Event handlers
+    analyze_btn.click(
         fn=analyze_text,
-        inputs=text_input,
-        outputs=[sentiment_output, confidence_output]
+        inputs=[text_input],
+        outputs=[html_output, plot_output, json_output]
     )
     
     text_input.submit(
         fn=analyze_text,
-        inputs=text_input,
-        outputs=[sentiment_output, confidence_output]
+        inputs=[text_input],
+        outputs=[html_output, plot_output, json_output]
     )
 
 # Launch the app