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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+mobilenet_model.h5.keras filter=lfs diff=lfs merge=lfs -text

food_info.json

@@ -0,0 +1,181 @@
+{
+    "Tuwo Shinkafa": {
+        "ingredients": [
+            "rice flour",
+            "water",
+            "salt",
+            "okra soup",
+            "meat"
+        ],
+        "calories": 350,
+        "risks": {
+            "diabetes": "high",
+            "hypertension": "medium",
+            "weight_loss": "medium"
+        },
+        "advice": "Use millet flour instead of white rice flour, reduce meat fat.",
+        "substitute": "Tuwo masara with ugu soup",
+        "diet_type": "non-vegan"
+    },
+    "Yam Porridge": {
+        "ingredients": [
+            "yam",
+            "palm oil",
+            "pepper",
+            "onion",
+            "leafy greens"
+        ],
+        "calories": 400,
+        "risks": {
+            "diabetes": "medium",
+            "hypertension": "high",
+            "weight_loss": "high"
+        },
+        "advice": "Use less palm oil, add more greens.",
+        "substitute": "Boiled yam with vegetable sauce",
+        "diet_type": "vegan"
+    },
+    "Jollof Rice": {
+        "ingredients": [
+            "white rice",
+            "tomato",
+            "pepper",
+            "oil",
+            "salt"
+        ],
+        "calories": 420,
+        "risks": {
+            "diabetes": "high",
+            "hypertension": "high",
+            "weight_loss": "high"
+        },
+        "advice": "Use brown rice, reduce oil and salt.",
+        "substitute": "Ofada rice with vegetable stew",
+        "diet_type": "non-vegan"
+    },
+    "Moi Moi": {
+        "ingredients": [
+            "beans",
+            "oil",
+            "pepper",
+            "onion",
+            "egg"
+        ],
+        "calories": 280,
+        "risks": {
+            "diabetes": "low",
+            "hypertension": "medium",
+            "weight_loss": "medium"
+        },
+        "advice": "Steam instead of baking with oil, reduce egg if needed.",
+        "substitute": "Boiled beans with vegetables",
+        "diet_type": "vegan"
+    },
+    "Suya": {
+        "ingredients": [
+            "beef",
+            "oil",
+            "pepper",
+            "spices",
+            "salt"
+        ],
+        "calories": 450,
+        "risks": {
+            "diabetes": "medium",
+            "hypertension": "high",
+            "weight_loss": "high"
+        },
+        "advice": "Limit portion size, avoid adding extra salt.",
+        "substitute": "Grilled chicken with pepper sauce",
+        "diet_type": "non-vegan"
+    },
+    "Egusi Soup": {
+        "ingredients": [
+            "egusi",
+            "palm oil",
+            "leafy greens",
+            "meat",
+            "seasoning"
+        ],
+        "calories": 500,
+        "risks": {
+            "diabetes": "medium",
+            "hypertension": "high",
+            "weight_loss": "high"
+        },
+        "advice": "Use less palm oil, add more vegetables, and lean meat.",
+        "substitute": "Vegetable soup with crayfish",
+        "diet_type": "non-vegan"
+    },
+    "Akara": {
+        "ingredients": [
+            "beans",
+            "pepper",
+            "onion",
+            "oil",
+            "salt"
+        ],
+        "calories": 330,
+        "risks": {
+            "diabetes": "medium",
+            "hypertension": "medium",
+            "weight_loss": "high"
+        },
+        "advice": "Fry with less oil or bake, avoid excess salt.",
+        "substitute": "Baked bean cakes",
+        "diet_type": "vegan"
+    },
+    "Pap (Akamu)": {
+        "ingredients": [
+            "corn",
+            "water",
+            "milk",
+            "sugar"
+        ],
+        "calories": 250,
+        "risks": {
+            "diabetes": "high",
+            "hypertension": "low",
+            "weight_loss": "low"
+        },
+        "advice": "Reduce sugar and use skimmed milk.",
+        "substitute": "Unsweetened pap with soya milk",
+        "diet_type": "vegan"
+    },
+    "Eba and Egusi": {
+        "ingredients": [
+            "garri",
+            "egusi",
+            "palm oil",
+            "meat",
+            "vegetables"
+        ],
+        "calories": 600,
+        "risks": {
+            "diabetes": "high",
+            "hypertension": "high",
+            "weight_loss": "high"
+        },
+        "advice": "Reduce portion, use more vegetables, and less oil.",
+        "substitute": "Wheat swallow with okra soup",
+        "diet_type": "non-vegan"
+    },
+    "Okra Soup": {
+        "ingredients": [
+            "okra",
+            "oil",
+            "pepper",
+            "leafy greens",
+            "meat"
+        ],
+        "calories": 300,
+        "risks": {
+            "diabetes": "low",
+            "hypertension": "medium",
+            "weight_loss": "low"
+        },
+        "advice": "Use little oil and lean meat, add ugu.",
+        "substitute": "Steamed okra with fish",
+        "diet_type": "non-vegan"
+    }
+}

gradio_app.py

@@ -0,0 +1,65 @@
+
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import json
+from gtts import gTTS
+import os
+import tempfile
+from health_logic import generate_advice
+
+# Load model
+model = tf.keras.models.load_model("mobilenet_model_finetuned.h5")
+
+# Load metadata
+with open("food_info.json", "r") as f:
+    food_info = json.load(f)
+class_names = list(food_info.keys())
+
+def classify_food(image, conditions, language):
+    image = image.convert("RGB").resize((224, 224))
+    img_array = tf.keras.preprocessing.image.img_to_array(image) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    predictions = model.predict(img_array)
+    predicted_index = np.argmax(predictions)
+    predicted_class = class_names[predicted_index]
+    info = food_info[predicted_class]
+
+    advice, risk_score, flags = generate_advice(info, conditions)
+
+    # Construct text output
+    result = f"🍽️ Food: {predicted_class}\n"
+    result += f"📊 Risk Score: {risk_score}%\n"
+    result += f"⚠️ Risk Factors: {', '.join(flags) if flags else 'None'}\n"
+    result += f"✅ Advice: {advice}\n"
+    result += f"🌱 Substitute: {info.get('substitute', 'None')}\n"
+
+    # TTS
+    tts = gTTS(text=advice, lang=language.lower()[:2])
+    tmp_path = os.path.join(tempfile.gettempdir(), "tts.mp3")
+    tts.save(tmp_path)
+
+    return result, tmp_path
+
+interface = gr.Interface(
+    fn=classify_food,
+    inputs=[
+        gr.Image(type="pil", label="Upload Food Image"),
+        gr.CheckboxGroup(
+            label="Select Health Conditions",
+            choices=["Normal", "Diabetic", "Hypertensive", "Weight Loss", "Malnourished", "Pregnant/Nursing", "Cholesterol Watch"]
+        ),
+        gr.Dropdown(label="Language for TTS", choices=["English", "Hausa", "Yoruba", "Igbo"], value="English")
+    ],
+    outputs=[
+        gr.Textbox(label="Result"),
+        gr.Audio(label="Hear Advice")
+    ],
+    title="🍲 HoodHealth Pro+",
+    description="Upload food image. Get nutrition, risk score & health advice with voice in your language."
+)
+
+if __name__ == "__main__":
+    interface.launch()

health_logic.py

@@ -0,0 +1,44 @@
+
+def generate_advice(food_data, conditions):
+    ingredients = [i.lower() for i in food_data.get("ingredients", [])]
+    risk_score = 0
+    advice_list = []
+    flags = []
+
+    for condition in conditions:
+        if condition == "Diabetic":
+            if "sugar" in ingredients or food_data["carbs"] > 40:
+                risk_score += 25
+                flags.append("High carbs/sugar")
+                advice_list.append("Reduce starchy content, avoid sugary drinks.")
+        if condition == "Hypertensive":
+            if "salt" in ingredients or food_data["fat"] > 25:
+                risk_score += 20
+                flags.append("High salt/fat")
+                advice_list.append("Use less salt and oil, consider boiling or steaming.")
+        if condition == "Weight Loss":
+            if food_data["calories"] > 350:
+                risk_score += 15
+                flags.append("High calories")
+                advice_list.append("Reduce portion size, avoid fried foods.")
+        if condition == "Malnourished":
+            if food_data["protein"] < 10:
+                risk_score += 10
+                flags.append("Low protein")
+                advice_list.append("Add beans, eggs, or fish to meals for better nutrition.")
+        if condition == "Pregnant/Nursing":
+            if food_data["iron"] < 10:
+                risk_score += 15
+                flags.append("Low iron")
+                advice_list.append("Add leafy greens, beans, or iron supplements.")
+        if condition == "Cholesterol Watch":
+            if "palm oil" in ingredients or food_data["fat"] > 25:
+                risk_score += 20
+                flags.append("Saturated fat")
+                advice_list.append("Reduce palm oil and fried food intake.")
+        if condition == "Normal":
+            advice_list.append("Maintain a balanced diet with local ingredients.")
+
+    risk_score = min(risk_score, 100)
+    final_advice = " ".join(set(advice_list))
+    return final_advice, risk_score, flags

mobilenet_model.h5.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65ed628417a0ab197788f64df99aa9231256c6304b288e9a3aed858775c60195
+size 9768203

requirements.txt.txt

@@ -0,0 +1,5 @@
+tensorflow
+gradio
+gtts
+Pillow
+numpy