updated and restructured app.py

app.py

@@ -1,37 +1,15 @@
-import gradio as gr
-import tensorflow as tf
-from tensorflow import keras
+from tensorflow.keras.models import load_model 
+import numpy as np
+import cv2
 
-# Load the sentiment analysis model
-model = keras.models.load_model("sentimentality.h5")
+model = load_model('sentimentality.h5')
 
-# Load the tokenizer and max length used during training
-tokenizer = keras.preprocessing.text.tokenizer_from_json(open("tokenizer.json").read())
-max_len = 100
+def sentiment(text):
+    result = model(text)[0]
+    label = result['label']
+    score = round(result['score'], 3)
+    return f"{label} ({score})"
 
-def predict_sentiment(text):
-    # Preprocess the text
-    text = [text]
-    text = tf.keras.preprocessing.sequence.pad_sequences(tokenizer.texts_to_sequences(text), maxlen=max_len)
-
-    # Make a prediction
-    prediction = model.predict(text)[0][0]
-
-    # Return the probabilities of each sentiment
-    positive_prob = round(prediction * 100, 2)
-    negative_prob = round((1 - prediction) * 100, 2)
-    neutral_prob = 100 - positive_prob - negative_prob
-
-    return f"Positive: {positive_prob}%\nNegative: {negative_prob}%\nNeutral: {neutral_prob}%"
-
-# Define the interface of the Gradio app
-iface = gr.Interface(
-    fn=predict_sentiment,
-    inputs=gr.inputs.Textbox(label="Enter text here:"),
-    outputs=gr.outputs.Textbox(label="Sentiment probabilities:"),
-    title="Sentiment Analysis",
-    description="Enter some text and get the probabilities of the sentiment.",
-)
-
-# Run the Gradio app
-iface.launch()
+input_text = gr.inputs.Textbox(label="Enter text here to be classified:")
+label = gr.outputs.Label(num_top_classes=2)
+gr.Interface(fn=predict_from_img, inputs=image, outputs=label,title = 'Sentiment-Analysis').launch()