Update app.py

Gradio sentiment signal

app.py

@@ -1,23 +1,28 @@
 import gradio as gr
-import tensorflow as tf
-import numpy as np
+from textblob import TextBlob
 
-# Load the pre-trained model
-model = tf.keras.applications.MobileNetV2()
-labels_path = tf.keras.utils.get_file(
-    'ImageNetLabels.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt')
-imagenet_labels = np.array(open(labels_path).read().splitlines())
+def analyze_sentiment(text):
+    blob = TextBlob(text)
+    sentiment = blob.sentiment.polarity
+    if sentiment > 0:
+        color = "green"
+        label = "Positive"
+    elif sentiment < 0:
+        color = "red"
+        label = "Negative"
+    else:
+        color = "yellow"
+        label = "Neutral"
+    return color, label
 
-# Define the prediction function
-def classify_image(image):
-    image = tf.keras.applications.mobilenet_v2.preprocess_input(image)
-    predictions = model.predict(np.expand_dims(image, axis=0))
-    return {imagenet_labels[i]: float(predictions[0][i]) for i in range(1000)}
+def sentiment_analysis(text):
+    color, label = analyze_sentiment(text)
+    return "<h2 style='color:{}; text-align:center;'>{} Sentiment</h2>".format(color, label)
 
-# Create a Gradio interface
-inputs = gr.inputs.Image(shape=(224, 224))
-outputs = gr.outputs.Label(num_top_classes=3)
-interface = gr.Interface(fn=classify_image, inputs=inputs, outputs=outputs, capture_session=True)
+iface = gr.Interface(
+    fn=sentiment_analysis, 
+    inputs=gr.inputs.Textbox(lines=5, label="Enter Text"), 
+    outputs="html"
+)
 
-# Launch the interface
-interface.launch()
+iface.launch()