Update README.md

README.md

@@ -1,19 +1,91 @@
 ---
-title: Data Analysis
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
+title: "Data Analysis App"
+emoji: "📊"
+colorFrom: "indigo"
+colorTo: "blue"
+sdk: "streamlit"
+sdk_version: "1.39.0"
+app_file: "streamlit_data_analysis_app.py"
 pinned: false
-short_description: Streamlit template space
+license: "mit"
 ---
 
-# Welcome to Streamlit!
+# 📊 Streamlit Data Analysis App (Gemini + Open-Source)
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+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.
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+---
+
+## 🚀 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.
+
+---