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title: "Data Analysis App"
emoji: "📊"
colorFrom: "indigo"
colorTo: "blue"
sdk: "streamlit"
sdk_version: "1.39.0"
app_file: src/streamlit_app.py
pinned: false
license: "mit"
---
# 📊 Streamlit Data Analysis App (Gemini + Open-Source)
This Streamlit app lets you **upload CSV or Excel datasets**, automatically clean and preprocess them, create **quick visualizations**, and even get **AI-generated insights** powered by Gemini or open-source models.
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## 🚀 Features
✅ Upload `.csv` or `.xlsx` datasets
✅ Automatic data cleaning & standardization
✅ Preprocessing pipeline (imputation, encoding, scaling)
✅ Quick visualizations (histogram, boxplot, correlation heatmap, etc.)
✅ Smart dataset summary and preview
✅ Optional **Gemini AI insights** for dataset interpretation
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## 🧠 LLM Integration (Optional)
You can enable AI-generated insights with **Gemini 2.0 Flash** or your own Hugging Face model.
### 🔑 To configure:
1. Go to your Space’s **Settings → Secrets** tab.
2. Add the following: GEMINI_API_KEY = your_gemini_api_key
HF_TOKEN = your_huggingface_token # optional
3. Save, then **Restart your Space**.
If you don’t add an API key, the app will still work for data cleaning and visualization.
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## 🛠️ Deployment Notes
- **Runtime:** Python SDK
- **SDK:** Streamlit
- **File formats supported:** `.csv`, `.xlsx`
- **Maximum file size:** 100 MB
- **Recommended visibility:** Public (for full file upload support)
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## ⚙️ Troubleshooting
### ❌ AxiosError: Request failed with status code 403
If you encounter this:
- Ensure your Space is **Public** (not Private).
- Ensure `sdk: streamlit` and `app_file:` are correctly declared in the YAML metadata above.
- Check that your **runtime** is “Python SDK”.
- Recheck your **Gemini API Key** or token secrets.
### ✅ Fix Checklist
| Issue | Fix |
|-------|------|
| App fails to start | Verify `app_file` matches your actual Python filename |
| 403 Error | Make the Space public |
| API not found | Add key to **Settings → Secrets** |
| File upload broken | Ensure `sdk: streamlit` and `runtime: python` |
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## 💡 Example Workflow
1. Upload your dataset (e.g., `global_freelancers_raw.csv`).
2. View the raw preview and cleaned data table.
3. Generate preprocessing pipelines (e.g., median imputation + one-hot encoding).
4. Visualize trends with histograms, boxplots, or heatmaps.
5. (Optional) Ask Gemini for AI insights about correlations, patterns, or recommendations.
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## 🧩 Tech Stack
- **Frontend:** Streamlit
- **Backend:** Python (Pandas, NumPy, Scikit-learn)
- **AI Models:** Gemini 2.0 Flash / open-source LLMs (Qwen, Mistral, etc.)
- **Visualization:** Matplotlib, Seaborn
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## 🧾 License
MIT License © 2025
You are free to use, modify, and share this app with attribution.
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