Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.models import load_model
+
+# Load the saved model
+model = load_model('emotion_classifier_model.h5')
+
+# Load the tokenizer (You need to save the tokenizer too)
+import pickle
+with open('tokenizer.pickle', 'rb') as handle:
+    tokenizer = pickle.load(handle)
+
+# Define parameters for padding
+max_length = 200
+padding_type = 'post'
+trunc_type = 'post'
+
+# Define a function to predict emotions for a list of comments
+def predict_emotions(comments):
+    # Convert input text to sequences
+    sequences = tokenizer.texts_to_sequences(comments)
+    padded_sequences = pad_sequences(sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)
+
+    # Predict emotions
+    predictions = model.predict(padded_sequences)
+
+    # List of emotion labels
+    emotion_labels = ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 
+                      'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 
+                      'gratitude', 'grief', 'joy', 'love', 'nervousness', 'neutral', 'optimism', 'pride', 'realization', 
+                      'relief', 'remorse', 'sadness', 'surprise']
+
+    # Generate human-readable predictions
+    result = []
+    for prediction in predictions:
+        emotion_dict = {emotion: prob for emotion, prob in zip(emotion_labels, prediction)}
+        result.append(emotion_dict)
+    
+    return result
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict_emotions, 
+    inputs=gr.inputs.Textbox(label="Input Comment", lines=2, placeholder="Enter your comment here...", type="text", default="This is a wonderful day!"),
+    outputs=gr.outputs.Label(num_top_classes=3),
+    title="Reddit Emotion Classifier",
+    description="Select one or more testing comments and predict their emotion labels."
+)
+
+# Launch the app
+interface.launch()