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+# استخدام نسخة بايثون رسمية
+FROM python:3.9
+
+# تحديد مجلد العمل داخل السيرفر
+WORKDIR /code
+
+# نسخ ملف المتطلبات أولاً لتسريع التحميل
+COPY ./requirements.txt /code/requirements.txt
+
+# تثبيت المكتبات
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# نسخ باقي ملفات المشروع (بما فيها الموديلات)
+COPY . .
+
+# تشغيل FastAPI باستخدام Uvicorn على المنفذ 7860 (الافتراضي لهجنغ فيس)
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

food101/.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

food101/README.md

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+---
+license: other
+base_model: google/mobilenet_v2_1.0_224
+tags:
+- generated_from_trainer
+datasets:
+- food101
+metrics:
+- accuracy
+model-index:
+- name: mobilenet-finetuned-food101
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: food101
+      type: food101
+      config: default
+      split: train[:5000]
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.821
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# mobilenet-finetuned-food101
+
+This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) on the food101 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5518
+- Accuracy: 0.821
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 128
+- eval_batch_size: 128
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 512
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 30
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 6    | 1.9575          | 0.153    |
+| 1.9536        | 2.0   | 12   | 1.8509          | 0.265    |
+| 1.9536        | 3.0   | 18   | 1.7003          | 0.451    |
+| 1.7915        | 4.0   | 24   | 1.5181          | 0.578    |
+| 1.4994        | 5.0   | 30   | 1.3609          | 0.631    |
+| 1.4994        | 6.0   | 36   | 1.2321          | 0.669    |
+| 1.2203        | 7.0   | 42   | 1.0696          | 0.69     |
+| 1.2203        | 8.0   | 48   | 0.9676          | 0.723    |
+| 1.0215        | 9.0   | 54   | 0.8888          | 0.729    |
+| 0.8462        | 10.0  | 60   | 0.8380          | 0.74     |
+| 0.8462        | 11.0  | 66   | 0.7461          | 0.778    |
+| 0.744         | 12.0  | 72   | 0.6724          | 0.792    |
+| 0.744         | 13.0  | 78   | 0.7314          | 0.769    |
+| 0.6496        | 14.0  | 84   | 0.6831          | 0.77     |
+| 0.6143        | 15.0  | 90   | 0.5937          | 0.81     |
+| 0.6143        | 16.0  | 96   | 0.6217          | 0.793    |
+| 0.5468        | 17.0  | 102  | 0.5965          | 0.788    |
+| 0.5468        | 18.0  | 108  | 0.5944          | 0.813    |
+| 0.5428        | 19.0  | 114  | 0.5869          | 0.812    |
+| 0.5193        | 20.0  | 120  | 0.5565          | 0.82     |
+| 0.5193        | 21.0  | 126  | 0.6155          | 0.803    |
+| 0.4902        | 22.0  | 132  | 0.5685          | 0.817    |
+| 0.4902        | 23.0  | 138  | 0.6097          | 0.789    |
+| 0.4869        | 24.0  | 144  | 0.6002          | 0.8      |
+| 0.4745        | 25.0  | 150  | 0.5569          | 0.814    |
+| 0.4745        | 26.0  | 156  | 0.5414          | 0.821    |
+| 0.4653        | 27.0  | 162  | 0.5806          | 0.807    |
+| 0.4653        | 28.0  | 168  | 0.5663          | 0.807    |
+| 0.4543        | 29.0  | 174  | 0.5412          | 0.825    |
+| 0.4575        | 30.0  | 180  | 0.5518          | 0.821    |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0

food101/config.json

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+{
+  "_name_or_path": "google/mobilenet_v2_1.0_224",
+  "architectures": [
+    "MobileNetV2ForImageClassification"
+  ],
+  "classifier_dropout_prob": 0.2,
+  "depth_divisible_by": 8,
+  "depth_multiplier": 1.0,
+  "expand_ratio": 6,
+  "finegrained_output": true,
+  "first_layer_is_expansion": true,
+  "hidden_act": "relu6",
+  "id2label": {
+    "0": "beignets",
+    "1": "bruschetta",
+    "2": "chicken_wings",
+    "3": "hamburger",
+    "4": "pork_chop",
+    "5": "prime_rib",
+    "6": "ramen"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "beignets": 0,
+    "bruschetta": 1,
+    "chicken_wings": 2,
+    "hamburger": 3,
+    "pork_chop": 4,
+    "prime_rib": 5,
+    "ramen": 6
+  },
+  "layer_norm_eps": 0.001,
+  "min_depth": 8,
+  "model_type": "mobilenet_v2",
+  "num_channels": 3,
+  "output_stride": 32,
+  "problem_type": "single_label_classification",
+  "semantic_loss_ignore_index": 255,
+  "tf_padding": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2"
+}

food101/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ce7b82d2fd228b55d70a63370bb3468087cfdfee4d5ea0e06344e6a1c605bdb4
+size 9105836

food101/preprocessor_config.json

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+{
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "MobileNetV2ImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 256
+  },
+  "use_square_size": false
+}

food101/training_args.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4203d7b2f4ed0167633d8dfdc236108a64820595d999f93debac4848b22adb03
+size 4600

main.py

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+from fastapi import FastAPI, UploadFile, File
+import joblib
+import pandas as pd
+import numpy as np
+from pydantic import BaseModel
+import os
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+import io
+
+app = FastAPI()
+
+# ==============================
+# المتغيرات العالمية للمودلات
+# ==============================
+
+maternal_model = None
+genetic_model = None
+food_model = None
+food_processor = None
+
+# ==============================
+# تحميل المودلات عند تشغيل السيرفر
+# ==============================
+
+@app.on_event("startup")
+def load_models():
+    global maternal_model, genetic_model, food_model, food_processor
+    
+    # تحميل موديل الأم
+    try:
+        if os.path.exists("random_forest_model.joblib"):
+            maternal_model = joblib.load("random_forest_model.joblib")
+            print("✅ Maternal model loaded successfully")
+        else:
+            print("❌ File 'random_forest_model.joblib' NOT found!")
+    except Exception as e:
+        print(f"❌ Error loading Maternal model: {e}")
+
+    # تحميل موديل الوراثة
+    try:
+        model_name = "thaqafni_model.pkl"
+        if os.path.exists(model_name):
+            genetic_model = joblib.load(model_name)
+            print(f"✅ Genetic model '{model_name}' loaded successfully")
+        else:
+            print(f"❌ File '{model_name}' NOT found!")
+    except Exception as e:
+        print(f"❌ Error loading Genetic model: {e}")
+
+    # تحميل مودل التعرف على الطعام
+    try:
+        food_path = "food101"
+
+        if os.path.exists(food_path):
+            food_processor = AutoImageProcessor.from_pretrained(food_path)
+            food_model = AutoModelForImageClassification.from_pretrained(food_path)
+
+            print("✅ Food model loaded successfully")
+
+        else:
+            print("❌ Folder 'food101' NOT found!")
+
+    except Exception as e:
+        print(f"❌ Error loading Food model: {e}")
+
+# ==============================
+# نماذج البيانات
+# ==============================
+
+class MaternalInput(BaseModel):
+    age: int
+    systolic_bp: int
+    diastolic_bp: int
+    bs: float
+    body_temp: float
+    heart_rate: int
+
+class GeneticInput(BaseModel):
+    age: int
+    family_history: int
+    hemoglobin: float
+    fetal_hemoglobin: float
+    sweat_chloride: float
+    sickled_rbc_percent: float
+
+# ==============================
+# الصفحة الرئيسية
+# ==============================
+
+@app.get("/")
+def home():
+    return {
+        "status": "online",
+        "maternal_model": "Ready" if maternal_model else "Not Loaded",
+        "genetic_model": "Ready" if genetic_model else "Not Loaded",
+        "food_model": "Ready" if food_model else "Not Loaded"
+    }
+
+# ==============================
+# مودل مخاطر الأم
+# ==============================
+
+@app.post("/predict_maternal")
+async def predict_maternal(data: MaternalInput):
+
+    if not maternal_model:
+        return {"error": "Maternal model is not available"}
+
+    features = np.array([[ 
+        data.age,
+        data.systolic_bp,
+        data.diastolic_bp,
+        data.bs,
+        data.body_temp,
+        data.heart_rate
+    ]])
+
+    prediction = maternal_model.predict(features)
+
+    return {
+        "risk_level": int(prediction[0])
+    }
+
+# ==============================
+# مودل الأمراض الوراثية
+# ==============================
+
+@app.post("/predict_genetic")
+async def predict_genetic(data: GeneticInput):
+
+    if not genetic_model:
+        return {"error": "Genetic model is not available"}
+
+    input_data = pd.DataFrame([[
+        data.age,
+        data.family_history,
+        data.hemoglobin,
+        data.fetal_hemoglobin,
+        data.sweat_chloride,
+        data.sickled_rbc_percent
+    ]],
+    columns=[
+        'Age',
+        'Family_History',
+        'Hemoglobin',
+        'Fetal_Hemoglobin',
+        'Sweat_Chloride',
+        'Sickled_RBC_Percent'
+    ])
+
+    prediction = genetic_model.predict(input_data)[0]
+
+    probabilities = genetic_model.predict_proba(input_data)[0]
+    confidence = float(np.max(probabilities) * 100)
+
+    ar_map = {
+        "Thalassemia": "ثلاسيميا",
+        "Normal": "سليم - طبيعي",
+        "Sickle Cell Anemia": "فقر الدم المنجلي",
+        "Cystic Fibrosis": "تليف كيسي",
+        "High Risk": "معرض لخطورة عالية"
+    }
+
+    return {
+        "diagnosis": prediction,
+        "diagnosis_ar": ar_map.get(prediction, "غير معروف"),
+        "confidence": f"{confidence:.2f}%",
+        "status": "success"
+    }
+
+# ==============================
+# مودل التعرف على ال��عام
+# ==============================
+
+@app.post("/predict_food")
+async def predict_food(file: UploadFile = File(...)):
+
+    if not food_model:
+        return {"error": "Food model is not available"}
+
+    try:
+        image_bytes = await file.read()
+
+        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+
+        inputs = food_processor(images=image, return_tensors="pt")
+
+        with torch.no_grad():
+            outputs = food_model(**inputs)
+
+        logits = outputs.logits
+        predicted_class_id = logits.argmax(-1).item()
+
+        food_name = food_model.config.id2label[predicted_class_id]
+
+        return {
+            "food_id": predicted_class_id,
+            "food_name": food_name,
+            "status": "success"
+        }
+
+    except Exception as e:
+        return {
+            "error": str(e)
+        }

random_forest_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:602ace1ea6d6d37d5b0d043e731343d1b6bb714311c2107b02b383d194ab74af
+size 2721105

requirements.txt

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+fastapi
+uvicorn
+joblib
+scikit-learn
+pandas
+numpy
+xgboost
+transformers
+torch
+Pillow
+python-multipart
+torch==2.2.2+cpu --index-url https://download.pytorch.org/whl/cpu

thaqafni_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d8964035e6a668d7fe541570253a35b73b85a344ffadd5d2a00d65c43a9681e4
+size 2389345