used another function

app.py

@@ -3,14 +3,34 @@ import numpy as np
 import cv2
 import gradio as gr
 
+# Load the model
 model = load_model('sentimentality.h5')
 
+# Function to preprocess the input text
+def preprocess(text):
+    # Tokenize the text into a list of words
+    words = text.strip().lower().split()
+    # Load the vocabulary
+    with open('vocabulary.txt', 'r') as f:
+        vocab = f.read().splitlines()
+    # Convert the words to indices in the vocabulary
+    word_indices = [vocab.index(word) if word in vocab else 0 for word in words]
+    # Pad the sequence with zeros to a fixed length of 500
+    padded_indices = np.zeros(500, dtype=np.int32)
+    padded_indices[:len(word_indices)] = word_indices
+    # Convert the sequence to a tensor
+    tensor = np.expand_dims(padded_indices, axis=0)
+    return tensor
+
+# Function to make predictions using the loaded model
 def sentiment(text):
-    result = model(text)[0]
-    label = result['label']
-    score = round(result['score'], 3)
+    input_tensor = preprocess(text)
+    result = model.predict(input_tensor)[0]
+    label = 'positive' if result > 0.5 else 'negative'
+    score = round(float(result), 3)
     return f"{label} ({score})"
 
+# Create a Gradio interface
 input_text = gr.inputs.Textbox(label="Enter text here to be classified:")
 label = gr.outputs.Label(num_top_classes=2)
 gr.Interface(fn=sentiment, inputs=input_text, outputs=label,title = 'Sentiment-Analysis').launch()