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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. | |
| --- | |
| ## 🚀 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 | |
| --- | |
| ## 🧠 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. | |
| --- | |
| ## 🛠️ 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) | |
| --- | |
| ## ⚙️ 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` | | |
| --- | |
| ## 💡 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. | |
| --- | |
| ## 🧩 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 | |
| --- | |
| ## 🧾 License | |
| MIT License © 2025 | |
| You are free to use, modify, and share this app with attribution. | |
| --- | |