Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import json
+from datetime import datetime
+import tempfile
+import os
+from gtts import gTTS
+from fpdf import FPDF
+from health_logic import generate_advice
+from tensorflow.keras.applications.efficientnet import preprocess_input as efficientnet_preprocess
+
+# Load model
+model = tf.keras.models.load_model("food_vision_model.keras")
+
+# Ensure class names are in the correct order used during training
+class_names = [
+    "akara", "banga_soup", "egusi_soup", "jollof_rice", "moi_moi",
+    "nkwobi", "okpa", "suya", "tuwo", "yam_porridge"
+]
+
+# Load food metadata
+with open("food_info.json", "r") as f:
+    food_info = json.load(f)
+
+# Language map for TTS
+LANG_CODE = {
+    "English": "en",
+    "Hausa": "en",  # gTTS doesn't support Hausa properly
+    "Yoruba": "en",
+    "Igbo": "en"
+}
+
+# Risk level helper
+def get_risk_level(score):
+    if score <= 30:
+        return "🟢 Low Risk"
+    elif score <= 70:
+        return "🟡 Medium Risk"
+    else:
+        return "🔴 High Risk"
+
+# PDF class with Unicode support
+class UnicodePDF(FPDF):
+    def header(self):
+        self.set_font("DejaVu", "B", 16)
+        self.cell(0, 10, "HoodHealth Pro+ Report", ln=True)
+
+    def footer(self):
+        self.set_y(-15)
+        self.set_font("DejaVu", "I", 8)
+        self.cell(0, 10, f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M')}", align="C")
+
+def generate_pdf(info, advice, risk_score, risk_level, flags, conditions, lang, display_name, image_path):
+    pdf = UnicodePDF()
+    pdf.add_font("DejaVu", "", "DejaVuSans.ttf", uni=True)
+    pdf.add_font("DejaVu", "B", "DejaVuSans.ttf", uni=True)
+    pdf.add_font("DejaVu", "I", "DejaVuSans.ttf", uni=True)
+    pdf.add_page()
+    pdf.set_font("DejaVu", "", 12)
+
+    pdf.image(image_path, x=10, y=40, w=60)
+    pdf.set_xy(75, 40)
+    pdf.multi_cell(0, 10, f"Food: {display_name}\nEthnicity: {info['ethnicity']}\nIngredients: {info['ingredients']}")
+
+    pdf.ln(35)
+    pdf.multi_cell(0, 10, f"Calories: {info['calories']} kcal\nCarbs: {info['carbs']}g\nProtein: {info['protein']}g\nFat: {info['fat']}g")
+    pdf.ln(5)
+    pdf.multi_cell(0, 10, f"Diet Type: {info['diet_type']}\nSubstitute: {info.get('substitute', 'None')}")
+    pdf.ln(10)
+
+    pdf.set_font("DejaVu", "B", 14)
+    pdf.cell(0, 10, "Health Analysis", ln=True)
+    pdf.set_font("DejaVu", "", 12)
+    pdf.multi_cell(0, 10, f"Conditions: {', '.join(conditions)}\nRisk Score: {risk_score}% ({risk_level})\nRisk Factors: {', '.join(flags) if flags else 'None'}\nAdvice: {advice}")
+    pdf.ln(5)
+    pdf.set_font("DejaVu", "I", 10)
+    pdf.multi_cell(0, 10, "*Note: This advice is not a medical diagnosis.*")
+
+    path = os.path.join(tempfile.gettempdir(), "report.pdf")
+    pdf.output(path)
+    return path
+
+# Main function
+def classify_food(image, conditions, language):
+    if not conditions:
+        return "⚠️ Please select at least one health condition.", None, None, None
+
+    # Preprocess image
+    image = image.convert("RGB").resize((224, 224))
+    img = tf.keras.preprocessing.image.img_to_array(image)
+    img = efficientnet_preprocess(img)
+    img = np.expand_dims(img, axis=0)
+
+    # Predict
+    preds = model.predict(img)[0]
+    idx = np.argmax(preds)
+    predicted_class = class_names[idx]
+    confidence = round(float(preds[idx]) * 100, 2)
+
+    info = food_info[predicted_class]
+    display_name = info.get("display_name", predicted_class.replace("_", " ").title())
+
+    # Advice logic
+    advice, risk_score, flags = generate_advice(info, conditions)
+    risk_level = get_risk_level(risk_score)
+
+    # Save audio
+    audio_path = os.path.join(tempfile.gettempdir(), "tts.mp3")
+    tts = gTTS(text=advice, lang=LANG_CODE.get(language, "en"))
+    tts.save(audio_path)
+
+    # Save image
+    image_path = os.path.join(tempfile.gettempdir(), "upload.jpg")
+    image.save(image_path)
+
+    # Generate PDF
+    pdf_path = generate_pdf(info, advice, risk_score, risk_level, flags, conditions, language, display_name, image_path)
+
+    # Output summary
+    result = (
+        f"🍽️ Food: {display_name}\n"
+        f"🌍 Ethnicity: {info['ethnicity']}\n"
+        f"🥦 Ingredients: {info['ingredients']}\n"
+        f"🔥 Calories: {info['calories']} kcal\n"
+        f"🍞 Carbs: {info['carbs']}g\n"
+        f"🥩 Protein: {info['protein']}g\n"
+        f"🧈 Fat: {info['fat']}g\n"
+        f"🌱 Diet Type: {info['diet_type']}\n"
+        f"🔁 Substitute: {info.get('substitute', 'None')}\n\n"
+        f"📈 Confidence: {confidence}%\n"
+        f"📊 Risk Score: {risk_score}% ({risk_level})\n"
+        f"⚠️ Risk Factors: {', '.join(flags) if flags else 'None'}\n"
+        f"✅ Advice: {advice}\n\n"
+        f"📝 *Generated by HoodHealth Pro+.*"
+    )
+
+    return result, audio_path, f"🗣️ Language: {language}", pdf_path
+
+# Gradio interface
+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="Prediction and Advice"),
+        gr.Audio(label="Hear Advice"),
+        gr.Textbox(label="Language Info"),
+        gr.File(label="Download PDF Report")
+    ],
+    title="🍲 HoodHealth Pro+",
+    description="Upload a food image and get nutrition details, health risk, personalized advice, audio guidance, and a downloadable PDF report."
+)
+
+if __name__ == "__main__":
+    interface.launch()