Update README.md

README.md

@@ -1,8 +1,8 @@
 ---
-title: "USTP Student Handbook Assistant"
-emoji: "📘"
-colorFrom: "purple"
-colorTo: "indigo"
+title: "Data Analysis App"
+emoji: "📊"
+colorFrom: "indigo"
+colorTo: "blue"
 sdk: "streamlit"
 sdk_version: "1.39.0"
 app_file: src/streamlit_app.py
@@ -10,53 +10,82 @@ pinned: false
 license: "mit"
 ---
 
-# 📘 USTP Student Handbook Assistant (2023 Edition)
+# 📊 Streamlit Data Analysis App (Gemini + Open-Source)
 
-This Streamlit app lets students, faculty, and staff **ask questions about the USTP Student Handbook (2023 Edition)** and get **accurate, page-referenced answers** directly from the document — powered by **FAISS**, **Sentence Transformers**, and **open-source LLMs** such as Mistral, Mixtral, and Qwen.
+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
-✅ Reads and indexes the *USTP Student Handbook 2023 Edition* PDF  
-✅ Fast semantic search with **FAISS vector database**  
-✅ Accurate citation with **printed page numbers**, not raw PDF indices  
-✅ Choose between **multiple open-source models** (Mistral, Mixtral, Qwen, etc.)  
-✅ Offline-safe — works even without API tokens  
-✅ Automatic local embedding with **MiniLM** for fast responses  
-✅ Caches index for instant re-use  
+✅ 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 enhance the assistant’s responses with **Hugging Face Inference API** or run it completely **offline** using local models.
+You can enable AI-generated insights with **Gemini 2.0 Flash** or your own Hugging Face model.
 
 ### 🔑 To configure:
-1. Create a `.env` file in the app root directory.  
-2. Add your Hugging Face token (optional): HF_TOKEN = your_huggingface_token
-3. Save the file and **restart the app**.
+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 provide a token, the app will automatically use a **local SentenceTransformer model** for embeddings.
+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  
-- **App file:** `src/streamlit_app.py`  
-- **PDF file:** Must be named `USTP Student Handbook 2023 Edition.pdf` and placed in the same directory.  
-- **Recommended visibility:** **Public** (for demo and student access)  
-- **Supported models:**  
-- `mistralai/Mistral-7B-Instruct-v0.3`  
-- `mistralai/Mixtral-8x7B-Instruct-v0.1`  
-- `Qwen/Qwen2.5-14B-Instruct`  
+- **File formats supported:** `.csv`, `.xlsx`  
+- **Maximum file size:** 100 MB  
+- **Recommended visibility:** Public (for full file upload support)  
 
 ---
 
 ## ⚙️ Troubleshooting
 
-### ⚠️ “Permission denied: '/.streamlit'”
-If deploying in a restricted environment:
-- Set the working directory to a writable path (e.g., `/home/appuser/app`).
-- Or run:
-```bash
-mkdir -p ~/.streamlit
+### ❌ 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.
+
+---