correct the spamDetectorModel.py#322
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student-smritipandey wants to merge 1 commit intoCode-A2Z:mainfrom
Open
correct the spamDetectorModel.py#322student-smritipandey wants to merge 1 commit intoCode-A2Z:mainfrom
student-smritipandey wants to merge 1 commit intoCode-A2Z:mainfrom
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@Avdhesh-Varshney add the model + working video, please review it |
Avdhesh-Varshney
requested changes
Oct 5, 2025
| # Download & load model in one line (cached) | ||
| # --------------------------- | ||
| @st.cache_resource | ||
| def load_model_and_tokenizer(username="smritipandey02", notebook="spam-detection", out="kaggle_models"): |
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Plz import and use our built-in function from https://github.com/Code-A2Z/jarvis/blob/main/src/helpers/kaggle.py here
| # --------------------------- | ||
| # Streamlit UI | ||
| # --------------------------- | ||
| def spam_app(): |
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Follow all the guidelines from the README.md file
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ISSUE: #70
This project is a text classification model that detects whether a given SMS/text message is Spam or Ham (Not Spam) using Deep Learning. The model was trained on a labeled dataset of SMS messages and deployed with a user-friendly Streamlit web application.
🔹 Project Workflow
Dataset used: https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset
Data Preprocessing
Cleaned and tokenized SMS messages
Converted text to sequences using Tokenizer
Applied padding to maintain equal input length (MAX_LEN = 100)
Model Architecture
Built using TensorFlow/Keras
Embedding layer for word representation
LSTM / Dense layers for sequential learning
Final sigmoid output layer for binary classification (Spam vs Ham)
Training & Evaluation
Optimizer: Adam
Loss function: Binary Crossentropy
Metrics: Accuracy
Achieved high classification performance on test data
Deployment
Model saved as spam_classifier.h5 / spam_classifier.keras
Tokenizer saved as tokenizer.pkl
Deployed using Streamlit for real-time predictions
🔹 Features
✅ Detects spam messages with high accuracy
✅ Returns prediction confidence score
✅ Interactive web UI using Streamlit
✅ Supports any user-entered SMS/text message
🔹 Example Predictions
Input: "Congratulations! You have won a $500 gift voucher. Click the link to claim."
→ 🚨 Spam detected! (Confidence: 99%)
Input: "Hey, are we still meeting tomorrow at 5?"
→ ✅ Ham (Not Spam) (Confidence: 100%)
🔹 Technologies Used
Python
TensorFlow / Keras
NLTK / Text Preprocessing
Streamlit
Pickle
Working Video 📷
https://github.com/student-smritipandey/Spam-Detector-Model/blob/main/model.mp4