Update app.py

app.py

@@ -3,35 +3,32 @@ 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
+import os
+import tempfile
 from health_logic import generate_advice
-from tensorflow.keras.applications.efficientnet import preprocess_input as efficientnet_preprocess
+from deep_translator import GoogleTranslator
+from datetime import datetime
+from fpdf import FPDF
 
 # Load model
 model = tf.keras.models.load_model("food_vision_model.keras")
 
-class_names = [
-    "akara", "banga_soup", "egusi_soup", "jollof_rice", "moi_moi",
-    "nkwobi", "okpa", "suya", "tuwo", "yam_porridge"
-]
-
-# Load food metadata
+# Load metadata
 with open("food_info.json", "r") as f:
     food_info = json.load(f)
 
-# Language map for TTS
+class_names = list(food_info.keys())
+
+# Language code mapping for gTTS
 LANG_CODE = {
     "English": "en",
-    "Hausa": "en",  # gTTS doesn't support Hausa properly
-    "Yoruba": "en",
-    "Igbo": "en"
+    "Hausa": "ha",
+    "Yoruba": "yo",
+    "Igbo": "ig"
 }
 
-# Risk level helper
+# Risk color level
 def get_risk_level(score):
     if score <= 30:
         return "🟢 Low Risk"
@@ -40,121 +37,147 @@ def get_risk_level(score):
     else:
         return "🔴 High Risk"
 
-# PDF class with Unicode support
+# PDF Generator using Unicode-safe font
 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 __init__(self):
+        super().__init__()
+        self.add_font("DejaVu", "", "DejaVuSans.ttf", uni=True)
+        self.add_font("DejaVu", "B", "DejaVuSans.ttf", uni=True)
+        self.set_font("DejaVu", size=12)
 
 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.set_font("DejaVu", "B", 16)
+    pdf.cell(0, 10, "HoodHealth Pro+ Report", ln=True)
+
+    pdf.set_font("DejaVu", size=12)
+    pdf.cell(0, 10, f"Date: {datetime.now().strftime('%Y-%m-%d %H:%M')}", ln=True)
+    pdf.ln(10)
+
+    if os.path.exists(image_path):
+        pdf.image(image_path, x=10, y=40, w=60)
 
-    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.multi_cell(0, 10,
+        f"🍽️ Food: {display_name}\n"
+        f"🌍 Ethnicity: {info['ethnicity']}\n"
+        f"🥦 Ingredients: {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.multi_cell(0, 10,
+        f"🔥 Calories: {info['calories']} kcal\n"
+        f"🍞 Carbs: {info['carbs']}g\n"
+        f"🥩 Protein: {info['protein']}g\n"
+        f"🧈 Fat: {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.multi_cell(0, 10,
+        f"🌱 Diet Type: {info['diet_type']}\n"
+        f"🔁 Substitute: {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.set_font("DejaVu", size=12)
+
+    pdf.multi_cell(0, 10,
+        f"📋 Selected Conditions: {', '.join(conditions)}\n"
+        f"📊 Risk Score: {risk_score}% ({risk_level})\n"
+        f"⚠️ Risk Factors: {', '.join(flags) if flags else 'None'}\n"
+        f"✅ Advice: {advice}"
+    )
+
     pdf.ln(5)
     pdf.set_font("DejaVu", "I", 10)
-    pdf.multi_cell(0, 10, "*Note: This advice is not a medical diagnosis.*")
+    pdf.multi_cell(0, 10, "*Note: This advice is generated using simplified nutritional rules and is not a medical diagnosis.*")
 
-    path = os.path.join(tempfile.gettempdir(), "report.pdf")
-    pdf.output(path)
-    return path
+    temp_pdf = os.path.join(tempfile.gettempdir(), "report.pdf")
+    pdf.output(temp_pdf)
 
-# Main function
+    return temp_pdf
+
+# Main logic
 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)
+    img_array = tf.keras.preprocessing.image.img_to_array(image) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
 
-    # Predict
-    preds = model.predict(img)[0]
-    idx = np.argmax(preds)
-    predicted_class = class_names[idx]
-    confidence = round(float(preds[idx]) * 100, 2)
+    predictions = model.predict(img_array)[0]
+    predicted_index = np.argmax(predictions)
+    predicted_class = class_names[predicted_index]
+    confidence = round(float(predictions[predicted_index]) * 100, 2)
 
     info = food_info[predicted_class]
-    display_name = info.get("display_name", predicted_class.replace("_", " ").title())
+    display_name = info.get("display_name", predicted_class.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
+    # Translate
+    if language != "English":
+        try:
+            translated = GoogleTranslator(source="auto", target=LANG_CODE[language]).translate(advice)
+            advice = translated
+        except:
+            advice += " (⚠️ Translation failed)"
+
+    # TTS with fallback
+    tts_audio = None
+    try:
+        tts_lang = LANG_CODE.get(language, "en")
+        tts = gTTS(text=advice, lang=tts_lang)
+        tts_audio = os.path.join(tempfile.gettempdir(), "tts.mp3")
+        tts.save(tts_audio)
+    except Exception as e:
+        print(f"TTS error: {e}")
+        advice += " (⚠️ Voice advice unavailable)"
+
+    # Save image and PDF
+    tmp_img = os.path.join(tempfile.gettempdir(), "upload.jpg")
+    image.save(tmp_img)
+    pdf_path = generate_pdf(info, advice, risk_score, risk_level, flags, conditions, language, display_name, tmp_img)
+
+    # Output
     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+.*"
+        f"🧠 *Predictions*:\n"
+        f"🍲 {display_name} – {confidence}%\n\n"
+        f"📉 *Top 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"
+        f"🔁 *Substitute*: {info.get('substitute', 'None')}\n\n"
+        f"📝 This advice is generated using simple nutrition rules and is not a medical diagnosis."
     )
 
-    return result, audio_path, f"🗣️ Language: {language}", pdf_path
+    return result, tts_audio, f"🗣️ Language: {language}", pdf_path
 
-# Gradio interface
+# 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")
+        gr.CheckboxGroup(
+            label="Select Health Conditions",
+            choices=["Normal", "Diabetic", "Hypertensive", "Weight Loss", "Malnourished", "Pregnant/Nursing", "Cholesterol Watch"]
+        ),
+        gr.Dropdown(label="Language for TTS", choices=list(LANG_CODE.keys()), value="English")
     ],
     outputs=[
-        gr.Textbox(label="Prediction and Advice"),
+        gr.Textbox(label="Result", lines=10),
         gr.Audio(label="Hear Advice"),
-        gr.Textbox(label="Language Info"),
-        gr.File(label="Download PDF Report")
+        gr.Textbox(label="TTS Language"),
+        gr.File(label="Download Health Report (PDF)")
     ],
-    title="🍲 FoodHealth Pro+",
-    description="Upload a food image and get nutrition details, health risk, personalized advice, audio guidance, and a downloadable PDF report."
+    title="🥗 FoodHealth Pro+",
+    description="Upload a food image. Get full nutrition info, risk score, health advice with voice support in your language and download a detailed PDF."
 )
 
 if __name__ == "__main__":
-    interface.launch()
+    interface.launch()