Rename gradioapp.py to app.py

gradioapp.py → app.py

@@ -1,66 +1,60 @@
-import pandas as pd
-import numpy as np
-import re
-import nltk
-from nltk.tokenize import word_tokenize
-from nltk.corpus import stopwords
-from nltk.stem import WordNetLemmatizer
-from sklearn.feature_extraction.text import CountVectorizer
-import pickle
-import gradio as gr
-
-# Download NLTK data
-nltk.download("punkt")
-nltk.download("stopwords")
-nltk.download("wordnet")
-
-# Load vectorizer and model
-with open("count_vectorizer_spam.pkl", "rb") as f:
-    vectorizer = pickle.load(f)
-
-with open("nb_model_spam.pkl", "rb") as f:
-    nb_model = pickle.load(f)
-
-# Preprocessing function
-def preprocess_text(text):
-    words = word_tokenize(text)
-    words_without_punct = [word for word in words if word.isalnum()]
-    clean_text = ' '.join(words_without_punct)
-    clean_text = clean_text.lower()
-    
-    stop_words = set(stopwords.words('english'))
-    words = word_tokenize(clean_text)
-    filtered_words = [word for word in words if word.lower() not in stop_words]
-    clean_text_without_stopwords = ' '.join(filtered_words)
-    
-    lemmatizer = WordNetLemmatizer()
-    words = word_tokenize(clean_text_without_stopwords)
-    lemmatized_words = [lemmatizer.lemmatize(word) for word in words]
-    lemmatized_text = ' '.join(lemmatized_words)
-    
-    text = re.sub(r'[^a-z\s]', '', lemmatized_text)
-    return text
-
-# Prediction function for Gradio
-def predict_spam(text):
-    if text.strip() == "":
-        return "Please enter an email!"
-    
-    cleaned_text = preprocess_text(text)
-    X_input = vectorizer.transform([cleaned_text])
-    prediction = nb_model.predict(X_input)[0]
-    
-    return "Spam" if prediction == 1 else "Non-Spam"
-
-# Create Gradio interface
-iface = gr.Interface(
-    fn=predict_spam,
-    inputs=gr.Textbox(lines=5, placeholder="Enter email here..."),
-    outputs="text",
-    title="Spam Detection",
-    description="Enter an email message to detect if it's Spam or Non-Spam.",
-    allow_flagging=False
-)
-
-# Launch the app
-iface.launch()
+import pandas as pd
+import numpy as np
+import re
+import nltk
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+from sklearn.feature_extraction.text import CountVectorizer
+import pickle
+import gradio as gr
+
+nltk.download("punkt")
+nltk.download("stopwords")
+nltk.download("wordnet")
+
+with open("count_vectorizer_spam.pkl", "rb") as f:
+    vectorizer = pickle.load(f)
+
+with open("nb_model_spam.pkl", "rb") as f:
+    nb_model = pickle.load(f)
+
+def preprocess_text(text):
+    words = word_tokenize(text)
+    words_without_punct = [word for word in words if word.isalnum()]
+    clean_text = ' '.join(words_without_punct)
+    clean_text = clean_text.lower()
+    
+    stop_words = set(stopwords.words('english'))
+    words = word_tokenize(clean_text)
+    filtered_words = [word for word in words if word.lower() not in stop_words]
+    clean_text_without_stopwords = ' '.join(filtered_words)
+    
+    lemmatizer = WordNetLemmatizer()
+    words = word_tokenize(clean_text_without_stopwords)
+    lemmatized_words = [lemmatizer.lemmatize(word) for word in words]
+    lemmatized_text = ' '.join(lemmatized_words)
+    
+    text = re.sub(r'[^a-z\s]', '', lemmatized_text)
+    return text
+
+def predict_spam(text):
+    if text.strip() == "":
+        return "Please enter an email!"
+    
+    cleaned_text = preprocess_text(text)
+    X_input = vectorizer.transform([cleaned_text])
+    prediction = nb_model.predict(X_input)[0]
+    
+    return "Spam" if prediction == 1 else "Non-Spam"
+
+iface = gr.Interface(
+    fn=predict_spam,
+    inputs=gr.Textbox(lines=5, placeholder="Enter email here..."),
+    outputs="text",
+    title="Spam Detection",
+    description="Enter an email message to detect if it's Spam or Non-Spam.",
+    allow_flagging=False
+)
+
+iface.launch()