Create app.py

app.py

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+#1. Importing Lib
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+import gradio as gr
+
+#2. Data Preprocessing
+df=pd.read_csv("mail_data (1).csv")
+
+df.loc[df["Category"]=="spam","Category",]=0
+df.loc[df["Category"]=="ham","Category",]=1
+
+# split data into dependent and independednt
+x=df["Message"]
+y=df["Category"]
+
+#3. Modeling Part
+x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)
+
+# Vectorization process for message content
+feature_extraction=TfidfVectorizer(min_df=1,stop_words="english",lowercase=True)
+
+x_train_features = feature_extraction.fit_transform(x_train)
+x_test_features  =  feature_extraction.transform(x_test)
+
+y_train = y_train.astype("int")
+y_test = y_test.astype("int")
+
+
+
+model=LogisticRegression()
+model.fit(x_train_features,y_train)
+
+
+
+x_predict=model.predict(x_train_features)
+x_accuracy=accuracy_score(x_predict,y_train)
+
+y_predict=model.predict(x_test_features)
+y_accuracy=accuracy_score(y_predict,y_test)
+
+
+#4. Gradio Part 
+
+# Function to predict whether the email is spam or ham
+def classify_email(email_text):
+    # Transform the input email text using the same vectorizer used during training
+    input_data_features = feature_extraction.transform([email_text])
+
+    # Predict using the trained model
+    prediction = model.predict(input_data_features)
+
+    # Return the result based on the prediction
+    if prediction[0] == 0:
+        return "Your email is Spam"
+    else:
+        return "Your email is Ham"
+
+# Create a Gradio interface for user input
+interface = gr.Interface(
+    fn=classify_email,  # Function to be called when user interacts
+    inputs=gr.Textbox(label="Enter your email text here", placeholder="Type your email...", lines=5),
+    outputs=gr.Textbox(label="Prediction"),
+    live=True  # Live prediction update
+)
+
+# Launch the interface
+interface.launch()
+