Update app.py

app.py

@@ -1,61 +1,64 @@
-import re
-import nltk
-import pickle
-import string
-import numpy as np
-import gradio as gr
-
-from nltk.corpus import stopwords
-from keras.models import load_model
-from keras.preprocessing.sequence import pad_sequences
-
-nltk.download('stopwords')
-
-# Load Model and Tokenizer
-model = load_model("sentiment_analysis_best.keras")
-
-with open("tokenizer.pkl", "rb") as f:
-    tokenizer = pickle.load(f)
-
-MAX_LEN = 100
-
-negations = {"not", "no", "nor", "never", "n't"}
-stop_words = set(stopwords.words("english")) - negations
-
-# Preprocessing Function
-def preprocess(text):
-    text = text.lower()
-    text = re.sub(r"\d+", "", text)
-    text = text.translate(str.maketrans('', '', string.punctuation))
-    words = [w for w in text.split() if w not in stop_words]
-    return " ".join(words)
-
-# Prediction Function
-def predict_sentiment(text):
-    text = preprocess(text)
-    seq = tokenizer.texts_to_sequences([text])
-    pad = pad_sequences(seq, maxlen=MAX_LEN, padding='post')
-    pred = model.predict(pad)
-
-    label_idx = np.argmax(pred, axis=1)[0]
-    confidence = pred[0][label_idx] * 100
-
-    labels = ["Negative", "Positive", "Neutral"]
-
-    return f"{labels[label_idx]} {confidence:.2f}%"
-
-# Gradio  Interface
-interface = gr.Interface(
-    fn=predict_sentiment,
-    inputs=gr.Textbox(lines=3, placeholder="Type your sentence here..."),
-    outputs=gr.Textbox(label="Prediction"),
-    title="Sentiment Analysis System",
-    description="Outputs sentiment analysis along with model confidence.",
-    examples=[
-        ["I really love this product"],
-        ["This is the worst experience ever"],
-        ["It is okay, not good not bad"]
-    ]
-)
-
+import os 
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
+
+import re
+import nltk
+import pickle
+import string
+import numpy as np
+import gradio as gr
+
+from nltk.corpus import stopwords
+from keras.models import load_model
+from keras.preprocessing.sequence import pad_sequences
+
+nltk.download('stopwords')
+
+# Load Model and Tokenizer
+model = load_model("sentiment_analysis_best.keras")
+
+with open("tokenizer.pkl", "rb") as f:
+    tokenizer = pickle.load(f)
+
+MAX_LEN = 100
+
+negations = {"not", "no", "nor", "never", "n't"}
+stop_words = set(stopwords.words("english")) - negations
+
+# Preprocessing Function
+def preprocess(text):
+    text = text.lower()
+    text = re.sub(r"\d+", "", text)
+    text = text.translate(str.maketrans('', '', string.punctuation))
+    words = [w for w in text.split() if w not in stop_words]
+    return " ".join(words)
+
+# Prediction Function
+def predict_sentiment(text):
+    text = preprocess(text)
+    seq = tokenizer.texts_to_sequences([text])
+    pad = pad_sequences(seq, maxlen=MAX_LEN, padding='post')
+    pred = model.predict(pad)
+
+    label_idx = np.argmax(pred, axis=1)[0]
+    confidence = pred[0][label_idx] * 100
+
+    labels = ["Negative", "Positive", "Neutral"]
+
+    return f"{labels[label_idx]} {confidence:.2f}%"
+
+# Gradio  Interface
+interface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=3, placeholder="Type your sentence here..."),
+    outputs=gr.Textbox(label="Prediction"),
+    title="Sentiment Analysis System",
+    description="Outputs sentiment analysis along with model confidence.",
+    examples=[
+        ["I really love this product"],
+        ["This is the worst experience ever"],
+        ["It is okay, not good not bad"]
+    ]
+)
+
 interface.launch()