An end-to-end AI + Streamlit platform designed to automate the evaluation, monitoring, and progress tracking of research grant projects. This system combines NLP (SciBERT), ML models, automated alerts, project tracking, and interactive dashboards to support funding agencies, universities, and research organizations.
#Demo
🔍 1. Proposal Acceptance Prediction (AI Model) Uses SciBERT embeddings + XGBoost to predict grant proposal acceptance. Provides explainable insights (SHAP-based feature importance).
📈 2. Project Progress Tracking Add project updates (date, description, progress %). Generates a time-series line chart of progress. Detects: Stalled projects Missing updates Abnormal delays
💰 3. Budget Utilization & Analytics Dashboard Phase-wise budget graphs. Funding allocation insights. Visualization via Plotly.
📊 4. Smart Reporting Dashboard Includes: Number of active projects Category-wise performance Funding distribution Progress insights
🛠 5. Admin Panel Tools Manage proposals Add project updates View analytics & alerts (via Mail)
├── new_dashboard.py # Main Streamlit dashboard ├── app.py # Optional backend API ├── dataset/ │ ├── grants_db.csv │ └── awards_full_data.csv ├── models/ │ ├── scibert_model/ │ ├── xgboost_model.pkl │ └── tfidf_vectorizer.pkl ├── frontend/ # React/HTML assets │ ├── public/ │ ├── src/ ├── requirements.txt └── README.md
Frontend / Dashboard Streamlit Plotly HTML/CSS (UI enhancements) Machine Learning SciBERT (HuggingFace) XGBoost TF-IDF SHAP for explainability Backend Python Pandas, NumPy
1️⃣ Clone the repo git clone cd project-folder
2️⃣ Install dependencies pip install -r requirements.txt
3️⃣ Run Streamlit dashboard streamlit run new_dashboard.py