README.md

---
title: Mistral Fine-tuned Model
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: docker
app_port: 7860
---

# 🤖 Mistral Fine-tuned Model

Flask API with separate HTML/CSS/JS frontend for `KASHH-4/mistral_fine-tuned` model.

## 🚀 What This Is

A **Flask API server** with **separate frontend files**:
- Backend: Python Flask with CORS
- Frontend: HTML + CSS + JavaScript  
- Clean separation of concerns
- API-first design

## 📁 Project Structure

```
e:\EDI\hf-node-app\
├── app.py              # Main Gradio application
├── requirements.txt    # Python dependencies
├── README.md          # This file
└── .gitignore         # Git ignore rules
```

## 🔧 Deploy to Hugging Face Spaces

### Step 1: Create a Space

1. Go to https://huggingface.co/spaces
2. Click **"Create new Space"**
3. Configure:
   - **Owner:** KASHH-4 (or your account)
   - **Space name:** `mistral-api` (or any name)
   - **SDK:** Gradio
   - **Hardware:** CPU basic (Free)
   - **Visibility:** Public
4. Click **"Create Space"**

### Step 2: Upload Files

Upload these 3 files to your Space:
- `app.py`
- `requirements.txt`
- `README.md` (optional)

**Via Web UI:**
1. Click "Files" tab
2. Click "Add file" → "Upload files"
3. Drag and drop the files
4. Commit changes

**Via Git:**
```bash
git init
git remote add origin https://huggingface.co/spaces/KASHH-4/mistral-api
git add app.py requirements.txt README.md .gitignore
git commit -m "Initial deployment"
git push origin main
```

### Step 3: Wait for Deployment

- First build takes 5-10 minutes
- Watch the logs for "Running on..."
- Your Space will be live at: `https://kashh-4-mistral-api.hf.space`

## 🧪 Test Your Space

### Web Interface
Visit: `https://huggingface.co/spaces/KASHH-4/mistral-api`

### API Endpoint
```bash
curl -X POST "https://kashh-4-mistral-api.hf.space/api/predict" \
  -H "Content-Type: application/json" \
  -d '{"data":["Hello, how are you?"]}'
```

### From JavaScript/Node.js
```javascript
const response = await fetch('https://kashh-4-mistral-api.hf.space/api/predict', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({ data: ["Your prompt here"] })
});

const result = await response.json();
console.log(result.data[0]); // Generated text
```

### From Python
```python
import requests

response = requests.post(
    'https://kashh-4-mistral-api.hf.space/api/predict',
    json={'data': ['Your prompt here']}
)

print(response.json()['data'][0])
```

## 💰 Cost

**100% FREE** on HF Spaces:
- Free CPU tier (slower, ~10-30 sec per request)
- Sleeps after 48h inactivity (30 sec wake-up)
- Perfect for demos, personal projects, testing

**Optional Upgrades:**
- GPU T4 Small: $0.60/hour (much faster, 2-5 sec)
- GPU A10G: $3.15/hour (very fast, 1-2 sec)

Upgrade in: Space Settings → Hardware

## 🔧 Local Testing (Optional)

If you have Python installed and want to test locally before deploying:

```bash
# Install dependencies
pip install -r requirements.txt

# Run locally
python app.py

# Visit: http://localhost:7860
```

**Requirements:**
- Python 3.9+
- 16GB+ RAM (for model loading)
- GPU recommended but not required

## 📋 Model Configuration

The app is configured for `KASHH-4/mistral_fine-tuned`. To use a different model, edit `app.py`:

```python
MODEL_NAME = "your-org/your-model"
```

## 🆘 Troubleshooting

**Space stuck on "Building":**
- Check logs for errors
- Model might be too large for free CPU
- Try: Restart Space in Settings

**Space shows "Runtime Error":**
- Check if model exists and is public
- Verify model format is compatible with transformers
- Try smaller model first to test

**Slow responses:**
- Normal on free CPU tier
- Upgrade to GPU for faster inference
- Or use smaller model

## 📞 Support

Issues? Check the deployment guide in `huggingface-space/DEPLOYMENT-GUIDE.md`

---

## 🗑️ Cleanup Old Files

If you followed earlier Node.js instructions, delete unnecessary files:

See `CLEANUP.md` for full list of files to remove.

## License
MIT