Update app.py

app.py

@@ -1,33 +1,37 @@
-import os
-import gradio as gr
+import numpy as np
+import pandas as pd
 import tensorflow as tf
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.preprocessing.text import Tokenizer
+import gradio as gr
 
-# Mount Google Drive (if necessary)
-# from google.colab import drive
-# drive.mount('/content/drive')
+# Load the trained model
+model_path = "/content/drive/MyDrive/Twitter_data/sentiment_analysis_model.h5"
+model = tf.keras.models.load_model(model_path)
 
-# Path to the saved model
-model_path = '/content/sentiment_analysis_model.h5'
+# Load the tokenizer
+tokenizer_path = "/content/drive/MyDrive/Twitter_data/tokenizer.pickle"
+with open(tokenizer_path, 'rb') as handle:
+    tokenizer = pickle.load(handle)
 
-# Check if the model file exists
-if not os.path.exists(model_path):
-    raise FileNotFoundError(f"No file or directory found at {model_path}")
+# Define a function for sentiment classification
+def classify_sentiment(text):
+    # Preprocess the text
+    text_sequence = tokenizer.texts_to_sequences([text])
+    padded_sequence = pad_sequences(text_sequence, maxlen=100)
 
-# Load the model
-model = tf.keras.models.load_model(model_path)
+    # Make prediction using the trained model
+    prediction = model.predict(padded_sequence)
+
+    # Convert prediction to class label
+    predicted_label = np.argmax(prediction)
 
-# Define the prediction function
-def predict(text):
-    # Preprocess the text (implement your own preprocessing if needed)
-    # Example preprocessing
-    text = text.lower()
-    
-    # Make a prediction
-    prediction = model.predict([text])
-    return prediction
+    # Map class label to sentiment
+    sentiment_mapping = {0: "Negative", 1: "Neutral", 2: "Positive"}
+    sentiment = sentiment_mapping[predicted_label]
 
-# Create the Gradio interface
-iface = gr.Interface(fn=predict, inputs="text", outputs="label")
+    return sentiment
 
-# Launch the interface
+# Create Gradio interface
+iface = gr.Interface(fn=classify_sentiment, inputs="text", outputs="text", title="Sentiment Analysis", description="Enter a sentence to classify its sentiment.")
 iface.launch()