Create app.py

app.py

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+import gradio as gr
+from tensorflow.keras.models import load_model
+import prepro
+import numpy as np
+import nltk
+
+
+def classify(text):
+    nltk.download('stopwords')
+    model= load_model('nlp3.h5')
+    X= prepro.preprocess(text)
+    prediction = model.predict(np.array(X))
+    # return prediction
+    if(prediction<=0.4):
+        return "Looks like you are reading negative content. Some words sound negative in context."
+    elif(prediction>0.4 and prediction<=0.6):
+        return "Sounds Neutral. Speaks generally and not biased towards any value."
+    else :       
+        return "Sounds Positive. Giving a good impression to start reading this stuff. "
+
+
+iface= gr.Interface(
+    inputs=[gr.inputs.Textbox(lines=5, label="Context", placeholder="Type a sentence or paragraph here.")],
+    outputs=[gr.outputs.Textbox(label="Prediction")],
+    fn=classify, 
+    title='WATCHA-READIN',
+    theme='dark-peach'       
+)
+
+iface.launch(share=True)