---
title: Nest
emoji: 👁
colorFrom: pink
colorTo: blue
sdk: streamlit
sdk_version: 1.41.1
app_file: app.py
pinned: false
---

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
# Project Submission

## Files Overview
1. **model\merge.ipynb** - Combines datasets into a single file.
2. **model\clean.ipynb** - Cleans and preprocesses the data.
3. **app.py** - Runs the main(streamlit) application.
4. **model\biobert.ipynb** - Implements BioBERT for feature extraction.
5. **model\biobert_embeddings.pt** - Generates, stores and processes embeddings.
6. **data\filtered_combined.xlsx** - Stores data post filteration and combining datasets for analysis.

## How to Reproduce the Results

### Step 1: Install Dependencies
Ensure you have Python installed. Run the following command to install required libraries:
```bash
pip install -r requirements.txt
```

### Step 2: Run the Application
Use the following command to execute the main application:
```bash
streamlit run app.py
```

### Application Screenshot
![Application Screenshot](image.jpg)

---

### t-SNE Plot
t-SNE (t-Distributed Stochastic Neighbor Embedding) is used to visualize high-dimensional embeddings in a lower-dimensional space, helping to identify clusters or patterns in the data.

![t-SNE Plot](model/tsne_visualization.png)

---

### Cosine Similarity Matrix
The cosine similarity matrix shows the similarity scores between different clinical trial embeddings, where higher scores indicate more similar trials.

![Cosine Similarity Matrix](model/cosine_similarity.png)

### Step 3: Reproducing the Functionality
The solution uses the following libraries for key functionalities:
- **NumPy and Pandas** for data preprocessing and manipulation.
- **scikit-learn** for machine learning pipelines and evaluation.
- **matplotlib** for visualizing results.
- **torch** for deep learning model implementation and training.
- **transformers** for leveraging pre-trained models and tokenization.
- **tqdm** for progress bar implementation to monitor loops and processes.

### Packaging the Solution
The final submission includes:
1. **Codebase** - All Python scripts mentioned above.
2. **Detailed PPT** - Explains the methodology, results, and conclusions.
3. **requirements.txt** - Lists all dependencies for reproducibility.