Update app.py

app.py

@@ -1,22 +1,17 @@
-from fastapi import FastAPI
-from pydantic import BaseModel
+import gradio as gr
 import joblib
+import re
 import nltk
 from nltk.corpus import stopwords
 from nltk.stem import PorterStemmer
-import re
 
+# Download NLTK stopwords
 nltk.download('stopwords')
 
-app = FastAPI()
-
-# Load the model pipeline
+# Load the saved pipeline
 pipeline = joblib.load('spam_classifier_pipeline.joblib')
 
-class EmailRequest(BaseModel):
-    subject: str
-    body: str
-
+# Preprocessing function (must match your training preprocessing)
 def preprocess_text(text):
     text = text.lower()
     text = re.sub(r'[^a-zA-Z\s]', '', text)
@@ -27,12 +22,45 @@ def preprocess_text(text):
     words = [stemmer.stem(word) for word in words]
     return ' '.join(words)
 
-@app.post("/predict")
-async def predict(email: EmailRequest):
-    processed_text = preprocess_text(email.subject + ' ' + email.body)
-    prediction = pipeline.predict([processed_text])[0]
-    return {'prediction': ['ham', 'not_spam', 'spam'][prediction]}
+# Prediction function
+def classify_email(subject, body):
+    combined_text = preprocess_text(f"{subject} {body}")
+    prediction = pipeline.predict([combined_text])[0]
+    labels = ["ham", "not_spam", "spam"]
+    return labels[prediction]
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 📧 Spam Email Classifier")
+    gr.Markdown("Classify emails into **ham (personal)**, **not_spam (promotional)**, or **spam (junk)**")
+    
+    with gr.Row():
+        with gr.Column():
+            subject = gr.Textbox(label="Email Subject", 
+                                placeholder="e.g., 'Win a free prize!'")
+            body = gr.Textbox(label="Email Body", 
+                            placeholder="e.g., 'Click here to claim...'",
+                            lines=5)
+            submit_btn = gr.Button("Classify Email")
+        
+        with gr.Column():
+            output = gr.Label(label="Prediction")
+            examples = gr.Examples(
+                examples=[
+                    ["Meeting tomorrow", "Hi team, let's discuss the project at 10 AM."],
+                    ["Exclusive offer!", "Get 50% off on our new product. Limited time!"],
+                    ["You won $1,000,000!", "Claim your prize now by clicking this link!"],
+                    ["Newsletter", "This month's updates and new features"],
+                    ["Urgent: Account Suspension", "Your account will be closed unless you verify now"]
+                ],
+                inputs=[subject, body]
+            )
+    
+    submit_btn.click(
+        fn=classify_email,
+        inputs=[subject, body],
+        outputs=output
+    )
 
-@app.get("/")
-async def root():
-    return {"message": "Spam Classification API"}
+# For Hugging Face Spaces deployment
+demo.launch()