Create app.py

app.py

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+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
+
+# Load model and tokenizer
+model = tf.keras.models.load_model('sentiment_rnn.h5')
+
+# Load tokenizer
+with open('tokenizer.pkl', 'rb') as f:
+    tokenizer = pickle.load(f)
+
+# Initialize label encoder
+label_encoder = LabelEncoder()
+label_encoder.fit(["Happy", "Sad", "Neutral"])
+
+def predict_sentiment(text):
+    """
+    Predict sentiment for a given text
+    """
+    # Preprocess the text
+    sequence = tokenizer.texts_to_sequences([text])
+    padded = pad_sequences(sequence, maxlen=50)
+    
+    # Make prediction
+    prediction = model.predict(padded, verbose=0)[0]
+    predicted_class = np.argmax(prediction)
+    sentiment = label_encoder.inverse_transform([predicted_class])[0]
+    confidence = float(prediction[predicted_class])
+    
+    # Create confidence dictionary for all classes
+    confidences = {
+        "Happy": float(prediction[0]),
+        "Sad": float(prediction[1]),
+        "Neutral": float(prediction[2])
+    }
+    
+    return sentiment, confidences
+
+# 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)")
+    
+    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
+    )
+    
+    def analyze_text(text):
+        sentiment, confidences = predict_sentiment(text)
+        return sentiment, confidences
+    
+    submit_btn.click(
+        fn=analyze_text,
+        inputs=text_input,
+        outputs=[sentiment_output, confidence_output]
+    )
+    
+    text_input.submit(
+        fn=analyze_text,
+        inputs=text_input,
+        outputs=[sentiment_output, confidence_output]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()