chatgpt attempt

app.py

@@ -1,21 +1,41 @@
 import gradio as gr
+import numpy as np
 import tensorflow as tf
-from fastai.vision.all import *
+from tensorflow.keras.datasets import imdb
 
-# Load the model
-learn = tf.keras.models.load_model('sentimentality.h5')
+word_to_index = imdb.get_word_index()
 
-# Prediction function
-def make_prediction(user_sentence):
-  
-  prediction = learn.predict(user_sentence)
-  # dict = {'1': 'Negative', '2': 'Neutral', '3': 'Positive'}
-  # return dict[prediction[0]]
-  return prediction
+# Load the pre-trained model from file
+loaded_model = tf.keras.models.load_model('sentimentality.h5')
 
-title = "Sentiment Analysis MyAnimeList Reviews with fastai"
-description = "<p style='text-align: center'>Identifier si un commentaire dans MyAnimeList est positif, neutre ou négatif.<br/> Permet de connaître rapidement le sentiment globale que dégage un avis sur le site.</p>"
+# Define a function to make predictions
+def predict_sentiment(user_input, number_of_words, words_per_view):
+    # Encode the input text
+    words = user_input.split()
+    encoded_word = np.zeros(words_per_view).astype(int)
+    encoded_word[words_per_view - len(words) - 1] = 1
+    for i, word in enumerate(words):
+        index = words_per_view - len(words) + i
+        encoded_word[index] = word_to_index.get(word, 0) + 3
+    encoded_word = np.expand_dims(encoded_word, axis=0)
+    
+    # Make the prediction
+    prediction = loaded_model.predict(encoded_word)[0][0]
+    
+    # Return the sentiment label
+    if prediction > 0.5:
+        return "Positive"
+    else:
+        return "Negative"
 
-app = gr.Interface(fn=make_prediction, title=title, description=description, inputs=gr.TextArea(), outputs='text')
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=["textbox", gr.inputs.Number(label="Number of words", default=3000, min_value=1, max_value=10000), gr.inputs.Number(label="Words per view", default=30, min_value=1, max_value=100)],
+    outputs="text",
+    title="Sentiment Analysis",
+    description="Enter a text and get the sentiment prediction"
+)
 
-app.launch()
+# Launch the interface
+iface.launch()