Upload 3 files

app.py

@@ -1,41 +1,41 @@
-import pandas as pd
-import gradio as gr
-from sklearn.model_selection import train_test_split
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.naive_bayes import MultinomialNB
-
-# Load and clean the dataset
-data = pd.read_csv(r"spam.csv")
-data.drop_duplicates(inplace=True)
-data['Category'] = data['Category'].replace(['ham', 'spam'], ['Not spam', 'spam'])
-
-# Prepare data
-X = data['Message']
-y = data['Category']
-
-# Split into training and testing sets
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
-
-# Convert text data to numerical features
-vectorizer = CountVectorizer(stop_words='english')
-X_train_features = vectorizer.fit_transform(X_train)
-X_test_features = vectorizer.transform(X_test)
-
-# Train the model
-model = MultinomialNB()
-model.fit(X_train_features, y_train)
-
-# Define prediction function
-def predict_spam(message):
-    message_features = vectorizer.transform([message])
-    prediction = model.predict(message_features)[0]
-    return f"Prediction: {prediction}"
-
-# Launch Gradio interface
-gr.Interface(
-    fn=predict_spam,
-    inputs="text",
-    outputs="text",
-    title="📩 Spam Detection with Gradio",
-    description="Enter a message and the model will predict whether it's spam or not."
-).launch()
+import pandas as pd
+import gradio as gr
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
+
+# Load and clean the dataset
+data = pd.read_csv(r"C:\Users\merys\Downloads\spam.csv")
+data.drop_duplicates(inplace=True)
+data['Category'] = data['Category'].replace(['ham', 'spam'], ['Not spam', 'spam'])
+
+# Prepare data
+X = data['Message']
+y = data['Category']
+
+# Split into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+# Convert text data to numerical features
+vectorizer = CountVectorizer(stop_words='english')
+X_train_features = vectorizer.fit_transform(X_train)
+X_test_features = vectorizer.transform(X_test)
+
+# Train the model
+model = MultinomialNB()
+model.fit(X_train_features, y_train)
+
+# Define prediction function
+def predict_spam(message):
+    message_features = vectorizer.transform([message])
+    prediction = model.predict(message_features)[0]
+    return f"Prediction: {prediction}"
+
+# Launch Gradio interface
+gr.Interface(
+    fn=predict_spam,
+    inputs="text",
+    outputs="text",
+    title="📩 Spam Detection with Gradio",
+    description="Enter a message and the model will predict whether it's spam or not."
+).launch()

requirements.txt

@@ -0,0 +1,3 @@
+pandas
+scikit-learn
+gradio