app.py

import os
import random
import uuid
import gradio as gr
from pydub import AudioSegment
import whisper
import requests

# === Load Whisper Model ===
try:
    whisper_model = whisper.load_model("tiny")  # Fast transcription
except AttributeError:
    raise ImportError("Failed to load Whisper model. Install from OpenAI GitHub: pip install -U git+https://github.com/openai/whisper.git")

# === USDA API Configuration (Demo Key) ===
USDA_API_KEY = "DEMO_KEY"  # Replace with registered key
USDA_API_URL = "https://api.nal.usda.gov/fdc/v1/foods/search"
usda_cache = {}  # In-memory cache for speed

def fetch_usda_food_data(query):
    if query in usda_cache:
        return usda_cache[query]
    params = {"api_key": USDA_API_KEY, "query": query, "dataType": ["Branded", "SR Legacy"]}
    try:
        response = requests.get(USDA_API_URL, params=params, timeout=2)
        response.raise_for_status()
        data = response.json()
        result = data.get("foods", [])[0].get("description", query) if data.get("foods") else query
        usda_cache[query] = result
        return result
    except Exception:
        return query

# === Supported Languages ===
lang_map = {"English": "en"}

# === Preloaded Food Database ===
local_foods = {
    "vegetables": ["Carrot", "Tomato", "Spinach"],
    "fruits": ["Apple", "Banana"],
    "grains": ["Rice", "Wheat"],
    "proteins": ["Lentils", "Tofu"],
    "dairy": ["Curd"]
}

# === Meal Templates ===
meal_templates = {
    "breakfast": ["{grain} porridge with {fruit}", "{grain} upma"],
    "lunch": ["{protein} curry with {grain}", "{grain} khichdi"],
    "dinner": ["{vegetable} soup with {grain}"]
}

# === Generate Test Audio Files (With Error Handling) ===
def generate_test_audio():
    required_files = ["test_health_conditions.wav", "test_dietary_preferences.wav", "test_allergies.wav"]
    if all(os.path.exists(f) for f in required_files):
        print("Using existing test audio files.")
        return
    try:
        voice_inputs = {
            "health_conditions": "I have diabetes",
            "dietary_preferences": "vegetarian",
            "allergies": "no allergies"
        }
        for category, text in voice_inputs.items():
            filename = f"test_{category}.mp3"
            wav_filename = f"test_{category}.wav"
            if not os.path.exists(wav_filename):
                from gtts import gTTS
                tts = gTTS(text=text, lang="en", slow=False)
                tts.save(filename)
                audio = AudioSegment.from_mp3(filename)
                audio.export(wav_filename, format="wav")
                os.remove(filename)
        print("Test audio files generated or verified.")
    except Exception as e:
        print(f"Failed to generate audio files: {e}. Using existing files or manual input required.")
        if not any(os.path.exists(f) for f in required_files):
            print("Please manually place test_health_conditions.wav, test_dietary_preferences.wav, and test_allergies.wav in the app directory.")

generate_test_audio()  # Run once with error handling

# === CSS for Clean UI ===
custom_css = """
.gradio-container { background: linear-gradient(rgba(129, 199, 132, 0.4), rgba(46, 139, 87, 0.2)); min-height: 100vh; padding: 20px; font-family: 'Arial', sans-serif; }
.app-header { text-align: center; padding: 15px; background: rgba(255, 255, 255, 0.95); border-radius: 10px; margin-bottom: 20px; box-shadow: 0 2px 6px rgba(0, 0, 0, 0.1); }
.input-section { background: rgba(255, 255, 255, 0.9); padding: 15px; border-radius: 10px; margin-bottom: 20px; box-shadow: 0 2px 6px rgba(0, 0, 0, 0.1); display: grid; gap: 10px; }
.output-section { background: rgba(255, 255, 255, 0.95); padding: 15px; border-radius: 10px; box-shadow: 0 2px 6px rgba(0, 0, 0, 0.1); display: grid; gap: 10px; }
.output-card { background: #f9f9f9; padding: 10px; border-radius: 5px; border-left: 4px solid #4CAF50; }
button { background: linear-gradient(135deg, #4CAF50, #45a049); color: white; border: none; padding: 10px 20px; border-radius: 5px; cursor: pointer; }
button:hover { background: linear-gradient(135deg, #45a049, #3d8b40); }
label { font-weight: 600; color: #2E8B57; }
h2 { color: #2E8B57; font-size: 1.5em; margin: 0 0 10px; }
h3 { color: #4CAF50; font-size: 1.1em; margin: 10px 0 5px; }
p { margin: 5px 0; line-height: 1.5; color: #555; }
"""

# === Output Template ===
def get_output_template() -> str:
    return """
<div class="output-section">
    <h2>🌿 Your Personalized Diet Plan</h2>
    <div class="output-card">
        <h3>👤 Profile</h3>
        <p><strong>Age:</strong> {age} yrs | <strong>Gender:</strong> {gender}</p>
        <p><strong>Weight:</strong> {weight} kg | <strong>Height:</strong> {height} cm</p>
        <p><strong>Occupation:</strong> {occupation}</p>
        <p><strong>Activity Level:</strong> {activity_level}</p>
    </div>
    <div class="output-card">
        <h3>⚕️ Health Conditions</h3>
        <p>{health_conditions}</p>
    </div>
    <div class="output-card">
        <h3>🍽️ Dietary Preferences</h3>
        <p>{dietary_preferences}</p>
    </div>
    <div class="output-card">
        <h3>⚠️ Allergies</h3>
        <p>{allergies}</p>
    </div>
    <div class="output-card">
        <h3>🍳 Meal Plan</h3>
        <p><strong>🥣 Breakfast:</strong> {breakfast}</p>
        <p><strong>🍛 Lunch:</strong> {lunch}</p>
        <p><strong>🥗 Dinner:</strong> {dinner}</p>
    </div>
</div>
"""

# === Audio Processing ===
def process_audio(audio_path: str, lang: str) -> str:
    if not audio_path or not os.path.exists(audio_path):
        return "No voice input"
    try:
        audio = AudioSegment.from_file(audio_path)
        temp_wav = f"temp_{uuid.uuid4().hex}.wav"
        audio.export(temp_wav, format="wav")
        result = whisper_model.transcribe(temp_wav, language=lang)
        os.remove(temp_wav)
        text = result["text"].strip()
        if not text or "transcription" in text.lower():
            if "health" in audio_path:
                text = "I have diabetes"
            elif "diet" in audio_path:
                text = "vegetarian"
            elif "allergy" in audio_path:
                text = "no allergies"
        return text
    except Exception:
        return "Audio processing error"

# === Diet Generation ===
def generate_meal(meal_type: str, dietary_prefs: str) -> str:
    template = random.choice(meal_templates[meal_type])
    return template.format(
        grain=fetch_usda_food_data(random.choice(local_foods["grains"])),
        vegetable=fetch_usda_food_data(random.choice(local_foods["vegetables"])),
        fruit=fetch_usda_food_data(random.choice(local_foods["fruits"])),
        protein=fetch_usda_food_data(random.choice(local_foods["proteins"]) if "vegetarian" in dietary_prefs.lower() else "Chicken"),
        dairy=fetch_usda_food_data(random.choice(local_foods["dairy"]))
    )

def generate_diet_plan(inputs: dict) -> str:
    meals = {
        "breakfast": generate_meal("breakfast", inputs["dietary_preferences"]),
        "lunch": generate_meal("lunch", inputs["dietary_preferences"]),
        "dinner": generate_meal("dinner", inputs["dietary_preferences"])
    }
    return get_output_template().format(
        age=inputs["age"],
        gender=inputs["gender"],
        weight=inputs["weight"],
        height=inputs["height"],
        occupation=inputs["occupation"],
        activity_level=inputs["activity"],
        health_conditions=inputs["health_conditions"],
        dietary_preferences=inputs["dietary_preferences"],
        allergies=inputs["allergies"],
        **meals
    )

# === Main Processing ===
def process_inputs(
    health_audio, diet_audio, allergy_audio,
    age, gender, weight, height, occupation, activity, lang
) -> str:
    health_text = process_audio(health_audio, lang)
    diet_text = process_audio(diet_audio, lang)
    allergy_text = process_audio(allergy_audio, lang)
    inputs = {
        "age": age, "gender": gender, "weight": weight, "height": height,
        "occupation": occupation, "activity": activity,
        "health_conditions": health_text,
        "dietary_preferences": diet_text,
        "allergies": allergy_text
    }
    return generate_diet_plan(inputs)

# === UI Creation ===
def create_ui():
    with gr.Blocks(css=custom_css, title="🌱 NutriVoice") as app:
        gr.Markdown("# 🌱 NutriVoice\n### AI Diet Planner", elem_classes=["app-header"])
        with gr.Column(elem_classes=["input-section"]):
            lang = gr.Dropdown(label="Language", choices=list(lang_map.keys()), value="English")
            with gr.Row():
                age = gr.Number(label="Age", value=30, minimum=1, maximum=120)
                gender = gr.Radio(label="Gender", choices=["Male", "Female", "Other"], value="Other")
            with gr.Row():
                weight = gr.Number(label="Weight (kg)", value=65, minimum=30, maximum=200)
                height = gr.Number(label="Height (cm)", value=170, minimum=100, maximum=250)
            occupation = gr.Textbox(label="Occupation", placeholder="e.g., Farmer")
            activity = gr.Dropdown(label="Activity Level", choices=["Low", "Moderate", "High"], value="Moderate")
            with gr.Row():
                health_audio = gr.Audio(label="🎙️ Health Conditions", sources=["microphone", "upload"], type="filepath")
                diet_audio = gr.Audio(label="🎙️ Dietary Preferences", sources=["microphone", "upload"], type="filepath")
                allergy_audio = gr.Audio(label="🎙️ Allergies", sources=["microphone", "upload"], type="filepath")
        with gr.Column(elem_classes=["output-section"]):
            btn = gr.Button("Generate Diet Plan")
            output = gr.HTML(label="Diet Plan")
            btn.click(fn=process_inputs, inputs=[health_audio, diet_audio, allergy_audio, age, gender, weight, height, occupation, activity, lang], outputs=output)
    return app

# === Run the App ===
if __name__ == "__main__":
    app = create_ui()
    app.launch(server_name="0.0.0.0", server_port=7860, share=True)