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import gradio as gr
import transformers
from transformers import pipeline
import tf_keras as keras
import pandas as pd
import tempfile
import os
# Load pre-trained spam classifier
spam_classifier = pipeline(
"text-classification",
model="mrm8488/bert-tiny-finetuned-sms-spam-detection"
)
def classify_batch(file):
"""Process uploaded CSV/TXT file with multiple emails"""
try:
results = []
# Check if file exists
if not file.name:
raise gr.Error("No file uploaded")
# --- CSV File Handling ---
if file.name.endswith('.csv'):
df = pd.read_csv(file)
# Check for required email column
if 'email' not in df.columns:
raise gr.Error("CSV file must contain a column named 'email'")
emails = df['email'].tolist()
# --- Text File Handling ---
elif file.name.endswith('.txt'):
with open(file.name, 'r') as f:
emails = f.readlines()
# --- Unsupported Format ---
else:
raise gr.Error("Unsupported file format. Only CSV/TXT accepted")
# Process emails (common for both formats)
emails = emails[:100] # Limit to 100 emails
for email in emails:
# Handle empty lines in text files
if not email.strip():
continue
prediction = spam_classifier(email.strip())[0]
results.append({
"email": email.strip()[:50] + "...",
"label": "SPAM" if prediction["label"] == "LABEL_1" else "HAM",
"confidence": f"{prediction['score']:.4f}"
})
return pd.DataFrame(results)
except gr.Error as e:
raise e # Show pop-up for expected errors
except Exception as e:
raise gr.Error(f"Processing error: {str(e)}")
def classify_text(text):
result = spam_classifier(text)[0]
return {
"Spam": result["score"] if result["label"] == "LABEL_1" else 1 - result["score"],
"Ham": result["score"] if result["label"] == "LABEL_0" else 1 - result["score"]
}
with gr.Blocks(title="Spam Classifier Pro") as demo:
gr.Markdown("# 📧 Welcome to Spamedar!")
with gr.Tab("✉️ Single Email"):
gr.Interface(
description="<h2>Copy your email to find out if it's a is Spam or Ham👇<h2>",
fn=classify_text,
inputs=gr.Textbox(label="Input Email", lines=3),
outputs=gr.Label(label="Classification"),
examples=[
["Urgent: Verify your account details now!"],
["Hey, can we meet tomorrow to discuss the project?"],
["WINNER! You've been selected for a $1000 Walmart Gift Card!"],
["Your account needs verification. Click here to confirm your details."],
["Meeting rescheduled to Friday 2 PM"]
]
)
current_dir = os.getcwd()
with gr.Tab("📨 Multiple Emails"):
gr.Markdown("## Upload email batch (CSV or TXT)")
file_input = gr.File(label="Upload File", file_types=[".csv", ".txt"])
clear_btn = gr.Button("Clear Selection", variant="secondary")
output_table = gr.Dataframe(
headers=["email", "label", "confidence"],
datatype=["str", "str", "number"],
interactive=False,
label="Classification Results"
)
download_btn = gr.DownloadButton(label="Download Results")
def process_file(file):
"""Process file and return (display_df, download_path)"""
try:
if file is None:
return pd.DataFrame(), None
results_df = classify_batch(file)
with tempfile.NamedTemporaryFile(suffix=".csv", delete=False) as f:
results_df.to_csv(f.name, index=False)
return results_df, f.name
except Exception as e:
raise gr.Error(f"Error processing file: {str(e)}")
def clear_selection():
###clear file input and results function
return None, pd.DataFrame(), None
file_input.upload(
fn=process_file,
inputs=file_input,
outputs=[output_table, download_btn] # Update both components
)
clear_btn.click(
fn=clear_selection,
outputs=[file_input, output_table, download_btn]
)
example_files= [
os.path.join(os.getcwd(), "sample_emails.csv"),
os.path.join(os.getcwd(), "batch_emails.txt"),
]
if all(os.path.exists(f) for f in example_files):
gr.Examples(
examples=[[f] for f in example_files],
inputs=file_input,
outputs=[output_table, download_btn],
fn=process_file,
cache_examples=True,
label="Click any example below to test:"
)
else:
print("Warning: Example files missing. Place these in your project root:")
print("- sample_emails.csv")
print("- batch_emails.txt")
if __name__ == "__main__":
demo.launch(share=True) |