function 3

app.py

@@ -1,36 +1,35 @@
-from tensorflow.keras.models import load_model 
-import numpy as np
-import cv2
 import gradio as gr
+import tensorflow as tf
+import numpy as np
 
-# Load the model
-model = load_model('sentimentality.h5')
-
-# Function to preprocess the input text
+# Define a function to preprocess the text input
 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
+    tokenizer = tf.keras.preprocessing.text.Tokenizer()
+    tokenizer.fit_on_texts([text])
+    text = tokenizer.texts_to_sequences([text])
+    text = tf.keras.preprocessing.sequence.pad_sequences(text, maxlen=500, padding='post', truncating='post')
+    return text
 
-# Function to make predictions using the loaded model
-def sentiment(text):
-    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})"
+# Load the pre-trained model
+model = tf.keras.models.load_model('sentimentality.h5')
+
+# Define a function to make a prediction on the input text
+def predict_sentiment(text):
+    # Preprocess the text
+    text = preprocess(text)
+    # Make a prediction using the loaded model
+    proba = model.predict(text)[0]
+    # Normalize the probabilities
+    proba /= proba.sum()
+    # Return the probability distribution
+    return {"Positive": float(proba[0]), "Negative": float(proba[1]), "Neutral": float(proba[2])}
 
 # 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()
+iface = gr.Interface(
+    fn=predict_sentiment, 
+    inputs=gr.inputs.Textbox(label="Enter text here"), 
+    outputs=gr.outputs.Bar(label="Sentiment Distribution", num_top_classes=3)
+)
+
+# Launch the interface
+iface.launch()