app.py

import os
import random
import uuid
import gradio as gr
from pydub import AudioSegment
import whisper
import asyncio
import platform

# Load Whisper model
whisper_model = whisper.load_model("tiny")

# Supported languages
lang_map = {
    "English": "en", "Hindi": "hi", "Telugu": "te", "Spanish": "es",
    "Tamil": "ta", "Gujarati": "gu", "Kannada": "kn", "Bengali": "bn",
    "Marathi": "mr", "Punjabi": "pa", "Urdu": "ur"
}

# Local food database
local_foods = {
    "vegetables": [
        {"name": "carrot", "calories": 41, "iron": 0.3, "fiber": 2.8, "vitamin_c": 6},
        {"name": "spinach", "calories": 23, "iron": 2.7, "fiber": 2.2, "vitamin_c": 28},
        {"name": "beetroot", "calories": 43, "iron": 0.8, "fiber": 2.0, "vitamin_c": 4}
    ],
    "grains": [
        {"name": "rice", "calories": 130, "iron": 1.2, "fiber": 0.4, "protein": 2.7},
        {"name": "wheat", "calories": 340, "iron": 3.2, "fiber": 12.2, "protein": 13},
        {"name": "millet", "calories": 119, "iron": 3.0, "fiber": 8.5, "protein": 3.5}
    ],
    "proteins": [
        {"name": "lentils", "calories": 116, "iron": 3.3, "fiber": 7.9, "protein": 9},
        {"name": "chicken", "calories": 239, "iron": 1.3, "fiber": 0.0, "protein": 27},
        {"name": "tofu", "calories": 76, "iron": 1.6, "fiber": 0.3, "protein": 8}
    ]
}

# Select random foods
def precompute_foods():
    return {
        "grain": random.choice(local_foods['grains']),
        "veg1": random.choice(local_foods['vegetables']),
        "veg2": random.choice(local_foods['vegetables']),
        "protein": random.choice(local_foods['proteins'])
    }

# Output templates (shortened for brevity here, use your full dictionary)
def get_output_template(lang):
    templates = {
        "English": """
🌾 Personalized Diet Plan
────────────────────────────
👤 Age: {age}, Gender: {gender}
💼 Occupation: {occupation}
🏃 Activity Level: {activity_level}
⚕️ Health Conditions: {health_conditions}
🍽️ Preferences: {dietary_preferences}
⚠️ Allergies: {allergies}
💰 Budget: ₹{budget}/day

🥣 Breakfast:
{breakfast}

🍛 Lunch:
{lunch}

🥗 Dinner:
{dinner}

📊 Summary: Balanced meals with local ingredients.
"""
    }
    return templates.get(lang, templates["English"])

# Transcription
async def transcribe_audio(audio_path, lang):
    try:
        if not audio_path or not os.path.exists(audio_path):
            return ""
        temp_wav = f"temp_{uuid.uuid4().hex}.wav"
        audio = AudioSegment.from_file(audio_path)
        audio = audio.set_frame_rate(16000).set_channels(1)
        audio.export(temp_wav, format="wav")
        result = whisper_model.transcribe(temp_wav, language=lang_map.get(lang, "en"), fp16=False)
        os.remove(temp_wav)
        return result.get("text", "").strip()
    except Exception as e:
        return f"❌ Error: {str(e)}"

# Full pipeline
async def full_pipeline(health_audio, diet_audio, allergy_audio, health_text, diet_text, allergy_text,
                        age, gender, weight, height, occupation, activity, budget, lang):
    health_conditions = health_text or await transcribe_audio(health_audio, lang)
    dietary_preferences = diet_text or await transcribe_audio(diet_audio, lang)
    allergies = allergy_text or await transcribe_audio(allergy_audio, lang)
    return generate_diet(age, gender, weight, height, occupation, activity,
                         health_conditions, dietary_preferences, allergies, budget, lang)

# Diet generator
def generate_diet(age, gender, weight, height, occupation, activity_level,
                  health_conditions, dietary_preferences, allergies, budget, lang):
    foods = precompute_foods()
    grain, veg1, veg2, protein = foods['grain'], foods['veg1'], foods['veg2'], foods['protein']
    breakfast = f"{grain['name']} porridge with {veg1['name']}"
    lunch = f"{protein['name']} with {grain['name']} and {veg2['name']}"
    dinner = f"{veg1['name']} curry, {veg2['name']} salad, and {protein['name']}"
    nutrients = ["calories", "iron", "fiber", "protein", "vitamin_c"]
    nutrition = {n: round(grain.get(n, 0) * 2 + veg1.get(n, 0) + veg2.get(n, 0) * 2 + protein.get(n, 0) * 2, 1)
                 for n in nutrients}
    plan = get_output_template(lang).format(
        age=age, gender=gender, occupation=occupation, activity_level=activity_level,
        health_conditions=health_conditions or "None",
        dietary_preferences=dietary_preferences or "None",
        allergies=allergies or "None", budget=budget,
        breakfast=breakfast, lunch=lunch, dinner=dinner
    )
    nutrition_summary = f"""
🍽️ Nutritional Summary:
------------------------------
🔥 Calories: {nutrition['calories']} kcal
🩸 Iron: {nutrition['iron']} mg
🧬 Protein: {nutrition['protein']} g
🌾 Fiber: {nutrition['fiber']} g
🍊 Vitamin C: {nutrition['vitamin_c']} mg
"""
    return plan + nutrition_summary

# UI
def render_ui(default_lang="English"):
    with gr.Blocks() as demo:
        lang_sel = gr.Dropdown(label="Language", choices=list(lang_map.keys()), value=default_lang)
        age = gr.Number(label="Age", value=30, minimum=1)
        gender = gr.Radio(["Male", "Female", "Other"], label="Gender")
        weight = gr.Number(label="Weight (kg)", value=70, minimum=1)
        height = gr.Number(label="Height (cm)", value=170, minimum=1)
        occupation = gr.Textbox(label="Occupation")
        activity = gr.Radio(["Low", "Moderate", "High"], label="Activity Level")

        with gr.Row():
            health_audio = gr.Audio(label="🎤 Speak Health Conditions", type="filepath")
            health_text = gr.Textbox(label="Or Type Health Conditions")
        with gr.Row():
            diet_audio = gr.Audio(label="🎤 Speak Dietary Preferences", type="filepath")
            diet_text = gr.Textbox(label="Or Type Dietary Preferences")
        with gr.Row():
            allergy_audio = gr.Audio(label="🎤 Speak Allergies", type="filepath")
            allergy_text = gr.Textbox(label="Or Type Allergies")

        budget = gr.Number(label="Daily Budget (₹)", value=200, minimum=0)
        output = gr.Textbox(label="Generated Diet Plan", lines=15)

        gr.Button("🎯 Generate Plan").click(
            full_pipeline,
            inputs=[health_audio, diet_audio, allergy_audio, health_text, diet_text, allergy_text,
                    age, gender, weight, height, occupation, activity, budget, lang_sel],
            outputs=output
        )
    return demo

async def main():
    demo = render_ui()
    await demo.launch(server_name="0.0.0.0", server_port=7860)

if __name__ == "__main__":
    asyncio.run(main())