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# 🩺 Skin Disease Classifier — API Documentation (v2.0 — Dynamic AI Recommendations)

> ResNet50 (HAM10000) for classification + Groq (fast LLM inference) for personalized, dynamic risks & recommendations · Deployed on Hugging Face Spaces

![PyTorch](https://img.shields.io/badge/PyTorch-EE4C2C?style=flat&logo=pytorch&logoColor=white)
![FastAPI](https://img.shields.io/badge/FastAPI-009688?style=flat&logo=fastapi&logoColor=white)
![Groq](https://img.shields.io/badge/Groq-LLM-F55036?style=flat)
![HuggingFace](https://img.shields.io/badge/HuggingFace-FFD21E?style=flat&logo=huggingface&logoColor=black)

---

## 🆕 What's new in v2.0

This version replaces the old **static** recommendations with a **two-step, dynamic** flow:

1. **`POST /predict`** — classifies the image and returns a **set of follow-up questions specific to the predicted class** (e.g. Melanoma gets different questions than a Mole).
2. **`POST /recommend`** — takes the predicted class + the user's answers to those questions, sends them to **Groq**, and returns **personalized risks & recommendations in both English and Arabic**, plus an `urgency_level`.

This means recommendations are no longer hardcoded — they adapt to each patient's actual situation (how long the lesion has been there, whether it's bleeding, family history, etc).

---

## 📊 Model Overview (Classifier)

| Property | Value |
|---|---|
| Architecture | ResNet50 (pretrained on ImageNet, fine-tuned) |
| Final layer | Linear(2048 → 7) |
| Input size | 224 × 224 × 3 (RGB) |
| Normalization | mean=[0.485, 0.456, 0.406] · std=[0.229, 0.224, 0.225] |
| Dataset | HAM10000 — oversampled to 6,000 samples/class |
| Saved weights | `skin_resnet50.pth` |
| Recommendation engine | Groq — model `llama-3.3-70b-versatile` |

---

## 🔗 Base URL

```
https://refaat9900-skin-detection.hf.space
```

Swagger UI:
```
https://refaat9900-skin-detection.hf.space/docs
```

A working **demo web page** (`index.html`) that calls this exact API end-to-end — upload → predict → answer questions → recommend — is included alongside this documentation. Open it in any browser to see the full flow live.

---

## 🔑 Required Server Configuration

The server needs a **Groq API key** set as an environment variable named `GROK_API_KEY`.

> ⚠️ Despite the variable name `GROK_API_KEY` (kept for backward compatibility), this must be a key from **Groq** (console.groq.com) — **not** xAI's Grok (console.x.ai). These are two different companies and the keys are not interchangeable.

### On Hugging Face Spaces:
1. Go to your Space → **Settings** → **Variables and secrets**
2. Click **New secret**
3. Name: `GROK_API_KEY`
4. Value: *(your key from [console.groq.com/keys](https://console.groq.com/keys))*
5. Save — the Space will restart automatically

> ⚠️ Never hardcode the API key in `main.py` or commit it to a public repo. Always use Space secrets / environment variables.

---

## 📌 Endpoints

| Method | Path | Description |
|---|---|---|
| `GET` | `/` | Health check |
| `POST` | `/predict` | Upload image → get class + confidence + **follow-up questions** |
| `POST` | `/recommend` | Send class + answers → get **AI-generated risks & recommendations** (EN + AR) |

---

## 1️⃣ POST `/predict`

### Request
- `Content-Type: multipart/form-data`
- Field name: `file`
- Accepted: `image/jpeg`, `image/png`, `image/jpg`, `image/webp`

### Response

| Field | Type | Description |
|---|---|---|
| `predicted_class` | `string` | Predicted disease name (English) |
| `predicted_class_index` | `int` | Class index (0–6) — **save this, you need it for `/recommend`** |
| `confidence` | `float` | Confidence of top prediction (0–100%) |
| `description` | `string` | General medical description (English) |
| `all_probabilities` | `object[]` | Confidence for all 7 classes, sorted descending |
| `followup_questions` | `object[]` | **Questions to ask the user**, specific to the predicted class |

### `followup_questions` item structure
| Field | Type | Description |
|---|---|---|
| `id` | `string` | Stable identifier — **send this back exactly** in `/recommend` |
| `question_en` | `string` | Question text in English |
| `question_ar` | `string` | Question text in Arabic |
| `options` | `string[]` | Suggested answer choices (show as buttons/dropdown) |

### Example response

```json
{
  "predicted_class": "Melanoma",
  "predicted_class_index": 5,
  "confidence": 94.32,
  "description": "Melanoma is the most dangerous form of skin cancer...",
  "all_probabilities": [
    { "class_index": 5, "class_name": "Melanoma", "confidence": 94.32 },
    { "class_index": 4, "class_name": "Melanocytic Nevi (Moles)", "confidence": 3.11 }
  ],
  "followup_questions": [
    {
      "id": "duration",
      "question_en": "How long have you noticed this spot or change?",
      "question_ar": "البقعة أو التغيير ده موجود من قد إيه؟",
      "options": ["Less than 1 month", "1–6 months", "More than 6 months"]
    },
    {
      "id": "abcde_change",
      "question_en": "Has it changed in shape, border, color, or size recently (ABCDE rule)?",
      "question_ar": "حصل تغيير في الشكل أو الحدود أو اللون أو الحجم مؤخرًا؟",
      "options": ["Yes, significantly", "Slightly", "No"]
    },
    {
      "id": "bleeding_itching",
      "question_en": "Is it bleeding, itching, or crusting?",
      "question_ar": "بينزف أو بيحكك أو فيه قشور عليه؟",
      "options": ["Yes", "No"]
    },
    {
      "id": "family_history",
      "question_en": "Any personal or family history of melanoma?",
      "question_ar": "في تاريخ شخصي أو عائلي للميلانوما؟",
      "options": ["Yes", "No", "Not sure"]
    },
    {
      "id": "sun_history",
      "question_en": "History of severe sunburns or frequent tanning bed use?",
      "question_ar": "في تاريخ لحروق شمس شديدة أو استخدام متكرر لأجهزة التسمير؟",
      "options": ["Yes", "No"]
    }
  ]
}
```

> 💡 Each of the 7 classes has its **own set of 4–5 tailored questions** (Melanoma's questions differ from Mole's, which differ from Dermatofibroma's, etc).

---

## 2️⃣ POST `/recommend`

Call this **after** the user answers the `followup_questions` from `/predict`.

### Request body (JSON)

```json
{
  "predicted_class_index": 5,
  "answers": {
    "duration": "1–6 months",
    "abcde_change": "Yes, significantly",
    "bleeding_itching": "Yes",
    "family_history": "No",
    "sun_history": "Yes"
  }
}
```

| Field | Type | Required | Description |
|---|---|---|---|
| `predicted_class_index` | `int` | ✅ Yes | The index returned by `/predict` |
| `answers` | `object` | ✅ Yes | Key = question `id` from `/predict`, Value = the option the user picked |

> ⚠️ If you skip a question, just omit its key — the backend will mark it as "Not answered" when building the AI prompt.

### Response

| Field | Type | Description |
|---|---|---|
| `predicted_class` | `string` | Class name (English) |
| `risks_en` | `string[]` | 3–5 personalized risk points (English) |
| `risks_ar` | `string[]` | Same risks in Arabic |
| `recommendations_en` | `string[]` | 4–6 personalized action recommendations (English) |
| `recommendations_ar` | `string[]` | Same recommendations in Arabic |
| `urgency_level` | `string` | One of: `"low"`, `"moderate"`, `"high"`, `"urgent"` |

### Example response

```json
{
  "predicted_class": "Melanoma",
  "risks_en": [
    "Recent changes in shape, color, and size strongly increase concern for malignant melanoma",
    "Bleeding or crusting can indicate active tissue change requiring urgent biopsy",
    "History of severe sunburns is a known major risk factor for melanoma",
    "Melanoma can spread quickly to lymph nodes if not treated early"
  ],
  "risks_ar": [
    "التغييرات الأخيرة في الشكل واللون والحجم تزيد من احتمالية وجود ميلانوما خبيثة",
    "النزيف أو القشور قد يدل على تغير نشط في النسيج يحتاج خزعة عاجلة",
    "تاريخ حروق الشمس الشديدة عامل خطر معروف للميلانوما",
    "الميلانوما ممكن تنتشر بسرعة للغدد الليمفاوية لو ماتعالجتش بدري"
  ],
  "recommendations_en": [
    "⚠️ See a dermatologist or oncologist within the next few days — do not delay",
    "Avoid any further sun exposure or trauma to the area",
    "Take a clear photo dated today to track any further changes",
    "Ask your doctor about a biopsy given the recent ABCDE changes",
    "Inform close family members to get their skin checked too"
  ],
  "recommendations_ar": [
    "⚠️ يجب زيارة طبيب جلدية أو أورام في أقرب وقت ممكن — لا تتأخر",
    "تجنب أي تعرض إضافي للشمس أو إصابة في المنطقة",
    "خد صورة واضحة بتاريخ اليوم لتتابع أي تغييرات إضافية",
    "اسأل الطبيب عن إجراء خزعة بسبب التغييرات الأخيرة",
    "أخبر أفراد العائلة المقربين بعمل فحص للجلد كذلك"
  ],
  "urgency_level": "urgent"
}
```

---

## 🔄 Full Flow Diagram

```
┌─────────────┐     image      ┌──────────────────┐
│   Frontend  │ ─────────────► │   POST /predict   │
│ (Flutter/JS)│                │  (ResNet50 model) │
└─────────────┘                └──────────────────┘
       ▲                                │
       │   class + confidence +         │
       │   followup_questions           ▼
       │                       Show questions to user
       │                       (buttons from `options`)
       │                                │
       │                                ▼
       │                       User answers all/some
       │                                │
       │      class_index + answers     ▼
       │                       ┌──────────────────┐
       └────────────────────── │  POST /recommend  │
        risks + recommendations│   (calls Groq)    │
        (EN + AR) + urgency    └──────────────────┘
```

---

## 🏷️ Disease classes (label mapping)

| Index | Class name (EN) |
|---|---|
| 0 | Actinic Keratoses |
| 1 | Basal Cell Carcinoma |
| 2 | Benign Keratosis-like Lesions |
| 3 | Dermatofibroma |
| 4 | Melanocytic Nevi (Moles) |
| 5 | Melanoma |
| 6 | Vascular Lesions |

---

## 💻 Integration Examples

### Flutter (Dart) — Full flow

```dart
import 'dart:io';
import 'dart:convert';
import 'package:http/http.dart' as http;
const String baseUrl = 'https://refaat9900-skin-detection.hf.space';
// Step 1: Predict
Future<Map<String, dynamic>> predictSkinDisease(File imageFile) async {
  final request = http.MultipartRequest('POST', Uri.parse('$baseUrl/predict'));
  request.files.add(await http.MultipartFile.fromPath('file', imageFile.path));
  final streamed = await request.send();
  final response = await http.Response.fromStream(streamed);
  if (response.statusCode != 200) {
    throw Exception('Predict failed: ${response.statusCode} — ${response.body}');
  }
  return jsonDecode(response.body) as Map<String, dynamic>;
}
// Step 2: Recommend (after user answers followup_questions)
Future<Map<String, dynamic>> getRecommendation(
  int predictedClassIndex,
  Map<String, String> answers,
) async {
  final response = await http.post(
    Uri.parse('$baseUrl/recommend'),
    headers: {'Content-Type': 'application/json'},
    body: jsonEncode({
      'predicted_class_index': predictedClassIndex,
      'answers': answers,
    }),
  );
  if (response.statusCode != 200) {
    throw Exception('Recommend failed: ${response.statusCode} — ${response.body}');
  }
  return jsonDecode(response.body) as Map<String, dynamic>;
}
// Usage example
void example() async {
  final file = File('/path/to/skin_image.jpg');
  // 1. Predict
  final prediction = await predictSkinDisease(file);
  print('Predicted: ${prediction['predicted_class']}');
  final classIndex = prediction['predicted_class_index'];
  final questions = prediction['followup_questions'] as List;
  // 2. Collect answers (in a real app, show these as UI — e.g. buttons per `options`)
  final Map<String, String> answers = {};
  for (final q in questions) {
    // Example: just pick the first option for demo purposes
    answers[q['id']] = q['options'][0];
  }
  // 3. Get personalized recommendation
  final result = await getRecommendation(classIndex, answers);
  print('Urgency: ${result['urgency_level']}');
  print('Risks (EN): ${result['risks_en']}');
  print('Risks (AR): ${result['risks_ar']}');
  print('Recommendations (EN): ${result['recommendations_en']}');
  print('Recommendations (AR): ${result['recommendations_ar']}');
}
```

#### pubspec.yaml

```yaml
dependencies:
  flutter:
    sdk: flutter
  http: ^1.2.1
  image_picker: ^1.0.7
```

---

### JavaScript (Fetch) — Full flow

```javascript
const BASE_URL = 'https://refaat9900-skin-detection.hf.space';
// Step 1: Predict
async function predictSkinDisease(imageFile) {
  const formData = new FormData();
  formData.append('file', imageFile);
  const res = await fetch(`${BASE_URL}/predict`, { method: 'POST', body: formData });
  if (!res.ok) throw new Error(`Predict failed: ${res.status}`);
  return await res.json();
}
// Step 2: Recommend
async function getRecommendation(predictedClassIndex, answers) {
  const res = await fetch(`${BASE_URL}/recommend`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ predicted_class_index: predictedClassIndex, answers }),
  });
  if (!res.ok) throw new Error(`Recommend failed: ${res.status}`);
  return await res.json();
}
// Usage
const fileInput = document.querySelector('#upload');
fileInput.addEventListener('change', async (e) => {
  const file = e.target.files[0];
  const prediction = await predictSkinDisease(file);
  // Render prediction.followup_questions as a form (radio buttons per `options`)
  renderQuestionsForm(prediction.followup_questions, async (answers) => {
    const result = await getRecommendation(prediction.predicted_class_index, answers);
    console.log('Urgency:', result.urgency_level);
    console.log('Risks (EN):', result.risks_en);
    console.log('Recommendations (AR):', result.recommendations_ar);
  });
});
```

---

### React — Full flow component

```jsx
import { useState } from 'react';
const BASE_URL = 'https://refaat9900-skin-detection.hf.space';
export default function SkinChecker() {
  const [prediction, setPrediction] = useState(null);
  const [answers, setAnswers] = useState({});
  const [recommendation, setRecommendation] = useState(null);
  const [loading, setLoading] = useState(false);
  const handleUpload = async (e) => {
    const file = e.target.files[0];
    if (!file) return;
    setLoading(true);
    const formData = new FormData();
    formData.append('file', file);
    const res = await fetch(`${BASE_URL}/predict`, { method: 'POST', body: formData });
    const data = await res.json();
    setPrediction(data);
    setAnswers({});
    setRecommendation(null);
    setLoading(false);
  };
  const submitAnswers = async () => {
    setLoading(true);
    const res = await fetch(`${BASE_URL}/recommend`, {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({
        predicted_class_index: prediction.predicted_class_index,
        answers,
      }),
    });
    const data = await res.json();
    setRecommendation(data);
    setLoading(false);
  };
  return (
    <div>
      <input type="file" accept="image/jpeg,image/png,image/webp" onChange={handleUpload} />
      {prediction && (
        <div>
          <h2>{prediction.predicted_class} — {prediction.confidence}%</h2>
          <p>{prediction.description}</p>
          <h3>A few quick questions</h3>
          {prediction.followup_questions.map((q) => (
            <div key={q.id}>
              <p>{q.question_en} / {q.question_ar}</p>
              {q.options.map((opt) => (
                <button
                  key={opt}
                  onClick={() => setAnswers((prev) => ({ ...prev, [q.id]: opt }))}
                  style={{ fontWeight: answers[q.id] === opt ? 'bold' : 'normal' }}
                >
                  {opt}
                </button>
              ))}
            </div>
          ))}
          <button onClick={submitAnswers} disabled={loading}>
            {loading ? 'Analyzing...' : 'Get Recommendation'}
          </button>
        </div>
      )}
      {recommendation && (
        <div>
          <h3>Urgency: {recommendation.urgency_level}</h3>
          <h4>Risks</h4>
          <ul>{recommendation.risks_en.map((r, i) => <li key={i}>{r}</li>)}</ul>
          <h4>التوصيات</h4>
          <ul>{recommendation.recommendations_ar.map((r, i) => <li key={i}>{r}</li>)}</ul>
        </div>
      )}
    </div>
  );
}
```

---

### cURL (terminal testing)

```bash
# Step 1: Predict
curl -X POST "https://refaat9900-skin-detection.hf.space/predict" \
  -F "file=@skin_image.jpg"
# Step 2: Recommend (use predicted_class_index & question ids from step 1's response)
curl -X POST "https://refaat9900-skin-detection.hf.space/recommend" \
  -H "Content-Type: application/json" \
  -d '{
    "predicted_class_index": 5,
    "answers": {
      "duration": "1–6 months",
      "abcde_change": "Yes, significantly",
      "bleeding_itching": "Yes",
      "family_history": "No",
      "sun_history": "Yes"
    }
  }'
```

---

## ⚠️ Error Responses

| Endpoint | HTTP code | Cause | Fix |
|---|---|---|---|
| `/predict` | `400` | Invalid file type or corrupted image | Send valid JPEG/PNG/WebP |
| `/predict` | `422` | Missing `file` field | Field name must be exactly `file` |
| `/recommend` | `400` | Invalid `predicted_class_index` | Must be an integer 0–6 |
| `/recommend` | `422` | Missing `predicted_class_index` or `answers` | Both fields are required in the JSON body |
| `/recommend` | `500` | `GROK_API_KEY` not set on server | Add it as a Space secret |
| `/recommend` | `502` | Groq API unreachable or returned a bad response | Check API key validity / Groq service status |

---

## 📋 Medical Disclaimer

> This API is intended for **research and educational purposes only**. The AI-generated risks and recommendations (including those from Groq) do **not** replace professional medical diagnosis. Always consult a qualified dermatologist for any skin-related health concerns.

	# 🩺 Skin Disease Classifier — API Documentation (v2.0 — Dynamic AI Recommendations)

	> ResNet50 (HAM10000) for classification + Groq (fast LLM inference) for personalized, dynamic risks & recommendations · Deployed on Hugging Face Spaces

	![PyTorch](https://img.shields.io/badge/PyTorch-EE4C2C?style=flat&logo=pytorch&logoColor=white)
	![FastAPI](https://img.shields.io/badge/FastAPI-009688?style=flat&logo=fastapi&logoColor=white)
	![Groq](https://img.shields.io/badge/Groq-LLM-F55036?style=flat)
	![HuggingFace](https://img.shields.io/badge/HuggingFace-FFD21E?style=flat&logo=huggingface&logoColor=black)

	---

	## 🆕 What's new in v2.0

	This version replaces the old static recommendations with a two-step, dynamic flow:

	1. `POST /predict` — classifies the image and returns a set of follow-up questions specific to the predicted class (e.g. Melanoma gets different questions than a Mole).
	2. `POST /recommend` — takes the predicted class + the user's answers to those questions, sends them to Groq, and returns personalized risks & recommendations in both English and Arabic, plus an `urgency_level`.

	This means recommendations are no longer hardcoded — they adapt to each patient's actual situation (how long the lesion has been there, whether it's bleeding, family history, etc).

	---

	## 📊 Model Overview (Classifier)

	\| Property \| Value \|
	\|---\|---\|
	\| Architecture \| ResNet50 (pretrained on ImageNet, fine-tuned) \|
	\| Final layer \| Linear(2048 → 7) \|
	\| Input size \| 224 × 224 × 3 (RGB) \|
	\| Normalization \| mean=[0.485, 0.456, 0.406] · std=[0.229, 0.224, 0.225] \|
	\| Dataset \| HAM10000 — oversampled to 6,000 samples/class \|
	\| Saved weights \| `skin_resnet50.pth` \|
	\| Recommendation engine \| Groq — model `llama-3.3-70b-versatile` \|

	---

	## 🔗 Base URL

	```
	https://refaat9900-skin-detection.hf.space
	```

	Swagger UI:
	```
	https://refaat9900-skin-detection.hf.space/docs
	```

	A working demo web page (`index.html`) that calls this exact API end-to-end — upload → predict → answer questions → recommend — is included alongside this documentation. Open it in any browser to see the full flow live.

	---

	## 🔑 Required Server Configuration

	The server needs a Groq API key set as an environment variable named `GROK_API_KEY`.

	> ⚠️ Despite the variable name `GROK_API_KEY` (kept for backward compatibility), this must be a key from Groq (console.groq.com) — not xAI's Grok (console.x.ai). These are two different companies and the keys are not interchangeable.

	### On Hugging Face Spaces:
	1. Go to your Space → Settings → Variables and secrets
	2. Click New secret
	3. Name: `GROK_API_KEY`
	4. Value: (your key from [console.groq.com/keys](https://console.groq.com/keys))
	5. Save — the Space will restart automatically

	> ⚠️ Never hardcode the API key in `main.py` or commit it to a public repo. Always use Space secrets / environment variables.

	---

	## 📌 Endpoints

	\| Method \| Path \| Description \|
	\|---\|---\|---\|
	\| `GET` \| `/` \| Health check \|
	\| `POST` \| `/predict` \| Upload image → get class + confidence + follow-up questions \|
	\| `POST` \| `/recommend` \| Send class + answers → get AI-generated risks & recommendations (EN + AR) \|

	---

	## 1️⃣ POST `/predict`

	### Request
	- `Content-Type: multipart/form-data`
	- Field name: `file`
	- Accepted: `image/jpeg`, `image/png`, `image/jpg`, `image/webp`

	### Response

	\| Field \| Type \| Description \|
	\|---\|---\|---\|
	\| `predicted_class` \| `string` \| Predicted disease name (English) \|
	\| `predicted_class_index` \| `int` \| Class index (0–6) — save this, you need it for `/recommend` \|
	\| `confidence` \| `float` \| Confidence of top prediction (0–100%) \|
	\| `description` \| `string` \| General medical description (English) \|
	\| `all_probabilities` \| `object[]` \| Confidence for all 7 classes, sorted descending \|
	\| `followup_questions` \| `object[]` \| Questions to ask the user, specific to the predicted class \|

	### `followup_questions` item structure

	\| Field \| Type \| Description \|
	\|---\|---\|---\|
	\| `id` \| `string` \| Stable identifier — send this back exactly in `/recommend` \|
	\| `question_en` \| `string` \| Question text in English \|
	\| `question_ar` \| `string` \| Question text in Arabic \|
	\| `options` \| `string[]` \| Suggested answer choices (show as buttons/dropdown) \|

	### Example response

	```json
	{
	"predicted_class": "Melanoma",
	"predicted_class_index": 5,
	"confidence": 94.32,
	"description": "Melanoma is the most dangerous form of skin cancer...",
	"all_probabilities": [
	{ "class_index": 5, "class_name": "Melanoma", "confidence": 94.32 },
	{ "class_index": 4, "class_name": "Melanocytic Nevi (Moles)", "confidence": 3.11 }
	],
	"followup_questions": [
	{
	"id": "duration",
	"question_en": "How long have you noticed this spot or change?",
	"question_ar": "البقعة أو التغيير ده موجود من قد إيه؟",
	"options": ["Less than 1 month", "1–6 months", "More than 6 months"]
	},
	{
	"id": "abcde_change",
	"question_en": "Has it changed in shape, border, color, or size recently (ABCDE rule)?",
	"question_ar": "حصل تغيير في الشكل أو الحدود أو اللون أو الحجم مؤخرًا؟",
	"options": ["Yes, significantly", "Slightly", "No"]
	},
	{
	"id": "bleeding_itching",
	"question_en": "Is it bleeding, itching, or crusting?",
	"question_ar": "بينزف أو بيحكك أو فيه قشور عليه؟",
	"options": ["Yes", "No"]
	},
	{
	"id": "family_history",
	"question_en": "Any personal or family history of melanoma?",
	"question_ar": "في تاريخ شخصي أو عائلي للميلانوما؟",
	"options": ["Yes", "No", "Not sure"]
	},
	{
	"id": "sun_history",
	"question_en": "History of severe sunburns or frequent tanning bed use?",
	"question_ar": "في تاريخ لحروق شمس شديدة أو استخدام متكرر لأجهزة التسمير؟",
	"options": ["Yes", "No"]
	}
	]
	}
	```

	> 💡 Each of the 7 classes has its own set of 4–5 tailored questions (Melanoma's questions differ from Mole's, which differ from Dermatofibroma's, etc).

	---

	## 2️⃣ POST `/recommend`

	Call this after the user answers the `followup_questions` from `/predict`.

	### Request body (JSON)

	```json
	{
	"predicted_class_index": 5,
	"answers": {
	"duration": "1–6 months",
	"abcde_change": "Yes, significantly",
	"bleeding_itching": "Yes",
	"family_history": "No",
	"sun_history": "Yes"
	}
	}
	```

	\| Field \| Type \| Required \| Description \|
	\|---\|---\|---\|---\|
	\| `predicted_class_index` \| `int` \| ✅ Yes \| The index returned by `/predict` \|
	\| `answers` \| `object` \| ✅ Yes \| Key = question `id` from `/predict`, Value = the option the user picked \|

	> ⚠️ If you skip a question, just omit its key — the backend will mark it as "Not answered" when building the AI prompt.

	### Response

	\| Field \| Type \| Description \|
	\|---\|---\|---\|
	\| `predicted_class` \| `string` \| Class name (English) \|
	\| `risks_en` \| `string[]` \| 3–5 personalized risk points (English) \|
	\| `risks_ar` \| `string[]` \| Same risks in Arabic \|
	\| `recommendations_en` \| `string[]` \| 4–6 personalized action recommendations (English) \|
	\| `recommendations_ar` \| `string[]` \| Same recommendations in Arabic \|
	\| `urgency_level` \| `string` \| One of: `"low"`, `"moderate"`, `"high"`, `"urgent"` \|

	### Example response

	```json
	{
	"predicted_class": "Melanoma",
	"risks_en": [
	"Recent changes in shape, color, and size strongly increase concern for malignant melanoma",
	"Bleeding or crusting can indicate active tissue change requiring urgent biopsy",
	"History of severe sunburns is a known major risk factor for melanoma",
	"Melanoma can spread quickly to lymph nodes if not treated early"
	],
	"risks_ar": [
	"التغييرات الأخيرة في الشكل واللون والحجم تزيد من احتمالية وجود ميلانوما خبيثة",
	"النزيف أو القشور قد يدل على تغير نشط في النسيج يحتاج خزعة عاجلة",
	"تاريخ حروق الشمس الشديدة عامل خطر معروف للميلانوما",
	"الميلانوما ممكن تنتشر بسرعة للغدد الليمفاوية لو ماتعالجتش بدري"
	],
	"recommendations_en": [
	"⚠️ See a dermatologist or oncologist within the next few days — do not delay",
	"Avoid any further sun exposure or trauma to the area",
	"Take a clear photo dated today to track any further changes",
	"Ask your doctor about a biopsy given the recent ABCDE changes",
	"Inform close family members to get their skin checked too"
	],
	"recommendations_ar": [
	"⚠️ يجب زيارة طبيب جلدية أو أورام في أقرب وقت ممكن — لا تتأخر",
	"تجنب أي تعرض إضافي للشمس أو إصابة في المنطقة",
	"خد صورة واضحة بتاريخ اليوم لتتابع أي تغييرات إضافية",
	"اسأل الطبيب عن إجراء خزعة بسبب التغييرات الأخيرة",
	"أخبر أفراد العائلة المقربين بعمل فحص للجلد كذلك"
	],
	"urgency_level": "urgent"
	}
	```

	---

	## 🔄 Full Flow Diagram

	```
	┌─────────────┐ image ┌──────────────────┐
	│ Frontend │ ─────────────► │ POST /predict │
	│ (Flutter/JS)│ │ (ResNet50 model) │
	└─────────────┘ └──────────────────┘
	▲ │
	│ class + confidence + │
	│ followup_questions ▼
	│ Show questions to user
	│ (buttons from `options`)
	│ │
	│ ▼
	│ User answers all/some
	│ │
	│ class_index + answers ▼
	│ ┌──────────────────┐
	└────────────────────── │ POST /recommend │
	risks + recommendations│ (calls Groq) │
	(EN + AR) + urgency └──────────────────┘
	```

	---

	## 🏷️ Disease classes (label mapping)

	\| Index \| Class name (EN) \|
	\|---\|---\|
	\| 0 \| Actinic Keratoses \|
	\| 1 \| Basal Cell Carcinoma \|
	\| 2 \| Benign Keratosis-like Lesions \|
	\| 3 \| Dermatofibroma \|
	\| 4 \| Melanocytic Nevi (Moles) \|
	\| 5 \| Melanoma \|
	\| 6 \| Vascular Lesions \|

	---

	## 💻 Integration Examples

	### Flutter (Dart) — Full flow

	```dart
	import 'dart:io';
	import 'dart:convert';
	import 'package:http/http.dart' as http;

	const String baseUrl = 'https://refaat9900-skin-detection.hf.space';

	// Step 1: Predict
	Future<Map<String, dynamic>> predictSkinDisease(File imageFile) async {
	final request = http.MultipartRequest('POST', Uri.parse('$baseUrl/predict'));
	request.files.add(await http.MultipartFile.fromPath('file', imageFile.path));

	final streamed = await request.send();
	final response = await http.Response.fromStream(streamed);

	if (response.statusCode != 200) {
	throw Exception('Predict failed: ${response.statusCode} — ${response.body}');
	}
	return jsonDecode(response.body) as Map<String, dynamic>;
	}

	// Step 2: Recommend (after user answers followup_questions)
	Future<Map<String, dynamic>> getRecommendation(
	int predictedClassIndex,
	Map<String, String> answers,
	) async {
	final response = await http.post(
	Uri.parse('$baseUrl/recommend'),
	headers: {'Content-Type': 'application/json'},
	body: jsonEncode({
	'predicted_class_index': predictedClassIndex,
	'answers': answers,
	}),
	);

	if (response.statusCode != 200) {
	throw Exception('Recommend failed: ${response.statusCode} — ${response.body}');
	}
	return jsonDecode(response.body) as Map<String, dynamic>;
	}

	// Usage example
	void example() async {
	final file = File('/path/to/skin_image.jpg');

	// 1. Predict
	final prediction = await predictSkinDisease(file);
	print('Predicted: ${prediction['predicted_class']}');

	final classIndex = prediction['predicted_class_index'];
	final questions = prediction['followup_questions'] as List;

	// 2. Collect answers (in a real app, show these as UI — e.g. buttons per `options`)
	final Map<String, String> answers = {};
	for (final q in questions) {
	// Example: just pick the first option for demo purposes
	answers[q['id']] = q['options'][0];
	}

	// 3. Get personalized recommendation
	final result = await getRecommendation(classIndex, answers);
	print('Urgency: ${result['urgency_level']}');
	print('Risks (EN): ${result['risks_en']}');
	print('Risks (AR): ${result['risks_ar']}');
	print('Recommendations (EN): ${result['recommendations_en']}');
	print('Recommendations (AR): ${result['recommendations_ar']}');
	}
	```

	#### pubspec.yaml

	```yaml
	dependencies:
	flutter:
	sdk: flutter
	http: ^1.2.1
	image_picker: ^1.0.7
	```

	---

	### JavaScript (Fetch) — Full flow

	```javascript
	const BASE_URL = 'https://refaat9900-skin-detection.hf.space';

	// Step 1: Predict
	async function predictSkinDisease(imageFile) {
	const formData = new FormData();
	formData.append('file', imageFile);

	const res = await fetch(`${BASE_URL}/predict`, { method: 'POST', body: formData });
	if (!res.ok) throw new Error(`Predict failed: ${res.status}`);
	return await res.json();
	}

	// Step 2: Recommend
	async function getRecommendation(predictedClassIndex, answers) {
	const res = await fetch(`${BASE_URL}/recommend`, {
	method: 'POST',
	headers: { 'Content-Type': 'application/json' },
	body: JSON.stringify({ predicted_class_index: predictedClassIndex, answers }),
	});
	if (!res.ok) throw new Error(`Recommend failed: ${res.status}`);
	return await res.json();
	}

	// Usage
	const fileInput = document.querySelector('#upload');
	fileInput.addEventListener('change', async (e) => {
	const file = e.target.files[0];
	const prediction = await predictSkinDisease(file);

	// Render prediction.followup_questions as a form (radio buttons per `options`)
	renderQuestionsForm(prediction.followup_questions, async (answers) => {
	const result = await getRecommendation(prediction.predicted_class_index, answers);
	console.log('Urgency:', result.urgency_level);
	console.log('Risks (EN):', result.risks_en);
	console.log('Recommendations (AR):', result.recommendations_ar);
	});
	});
	```

	---

	### React — Full flow component

	```jsx
	import { useState } from 'react';

	const BASE_URL = 'https://refaat9900-skin-detection.hf.space';

	export default function SkinChecker() {
	const [prediction, setPrediction] = useState(null);
	const [answers, setAnswers] = useState({});
	const [recommendation, setRecommendation] = useState(null);
	const [loading, setLoading] = useState(false);

	const handleUpload = async (e) => {
	const file = e.target.files[0];
	if (!file) return;
	setLoading(true);
	const formData = new FormData();
	formData.append('file', file);
	const res = await fetch(`${BASE_URL}/predict`, { method: 'POST', body: formData });
	const data = await res.json();
	setPrediction(data);
	setAnswers({});
	setRecommendation(null);
	setLoading(false);
	};

	const submitAnswers = async () => {
	setLoading(true);
	const res = await fetch(`${BASE_URL}/recommend`, {
	method: 'POST',
	headers: { 'Content-Type': 'application/json' },
	body: JSON.stringify({
	predicted_class_index: prediction.predicted_class_index,
	answers,
	}),
	});
	const data = await res.json();
	setRecommendation(data);
	setLoading(false);
	};

	return (
	<div>
	<input type="file" accept="image/jpeg,image/png,image/webp" onChange={handleUpload} />

	{prediction && (
	<div>
	<h2>{prediction.predicted_class} — {prediction.confidence}%</h2>
	<p>{prediction.description}</p>

	<h3>A few quick questions</h3>
	{prediction.followup_questions.map((q) => (
	<div key={q.id}>
	<p>{q.question_en} / {q.question_ar}</p>
	{q.options.map((opt) => (
	<button
	key={opt}
	onClick={() => setAnswers((prev) => ({ ...prev, [q.id]: opt }))}
	style={{ fontWeight: answers[q.id] === opt ? 'bold' : 'normal' }}
	>
	{opt}
	</button>
	))}
	</div>
	))}

	<button onClick={submitAnswers} disabled={loading}>
	{loading ? 'Analyzing...' : 'Get Recommendation'}
	</button>
	</div>
	)}

	{recommendation && (
	<div>
	<h3>Urgency: {recommendation.urgency_level}</h3>
	<h4>Risks</h4>
	<ul>{recommendation.risks_en.map((r, i) => <li key={i}>{r}</li>)}</ul>
	<h4>التوصيات</h4>
	<ul>{recommendation.recommendations_ar.map((r, i) => <li key={i}>{r}</li>)}</ul>
	</div>
	)}
	</div>
	);
	}
	```

	---

	### cURL (terminal testing)

	```bash
	# Step 1: Predict
	curl -X POST "https://refaat9900-skin-detection.hf.space/predict" \
	-F "file=@skin_image.jpg"

	# Step 2: Recommend (use predicted_class_index & question ids from step 1's response)
	curl -X POST "https://refaat9900-skin-detection.hf.space/recommend" \
	-H "Content-Type: application/json" \
	-d '{
	"predicted_class_index": 5,
	"answers": {
	"duration": "1–6 months",
	"abcde_change": "Yes, significantly",
	"bleeding_itching": "Yes",
	"family_history": "No",
	"sun_history": "Yes"
	}
	}'
	```

	---

	## ⚠️ Error Responses

	\| Endpoint \| HTTP code \| Cause \| Fix \|
	\|---\|---\|---\|---\|
	\| `/predict` \| `400` \| Invalid file type or corrupted image \| Send valid JPEG/PNG/WebP \|
	\| `/predict` \| `422` \| Missing `file` field \| Field name must be exactly `file` \|
	\| `/recommend` \| `400` \| Invalid `predicted_class_index` \| Must be an integer 0–6 \|
	\| `/recommend` \| `422` \| Missing `predicted_class_index` or `answers` \| Both fields are required in the JSON body \|
	\| `/recommend` \| `500` \| `GROK_API_KEY` not set on server \| Add it as a Space secret \|
	\| `/recommend` \| `502` \| Groq API unreachable or returned a bad response \| Check API key validity / Groq service status \|

	---

	## 📋 Medical Disclaimer

	> This API is intended for research and educational purposes only. The AI-generated risks and recommendations (including those from Groq) do not replace professional medical diagnosis. Always consult a qualified dermatologist for any skin-related health concerns.