app.py

# app.py
import os
from dotenv import load_dotenv
from google import genai
import gradio as gr

# Load environment variables (local .env or HF Secrets)
load_dotenv()

# Initialize Gemini client (will read GEMINI_API_KEY from environment)
client = genai.Client()
MODEL = "gemini-2.5-flash"  # quickstart example uses gemini-2.5-flash; change to gemini-2.5-flash-pro if you have access

# --- Utilities ---
def calculate_calorie_requirements(age, gender, weight, height, fitness_goal):
    try:
        age = float(age); weight = float(weight); height = float(height)
    except Exception:
        return 0
    if gender == "Male":
        bmr = 10 * weight + 6.25 * height - 5 * age + 5
    else:
        bmr = 10 * weight + 6.25 * height - 5 * age - 161
    if fitness_goal == "Weight Loss":
        return int(bmr * 1.2)
    if fitness_goal == "Weight Gain":
        return int(bmr * 1.5)
    return int(bmr * 1.375)

AYURVEDA_PROMPT = """
You are an expert in modern medicine and Ayurveda. Create a concise personalized weekly diet & exercise plan for {name}, a {age}-year-old {gender} with BMI {bmi} ({health_status}).
Fitness Goal: {fitness_goal}
Daily Calories: {daily_calories} kcal
Diet Preference: {dietary_preference}
Food Allergies: {food_allergies}
Local Cuisine: {local_cuisine}
Month: {month}
Return short bullets for each weekday (Mon..Sun).
"""

REGULAR_PROMPT = """
You are a health expert. Create a concise personalized weekly diet & exercise plan for {name}, a {age}-year-old {gender} with BMI {bmi} ({health_status}).
Fitness Goal: {fitness_goal}
Daily Calories: {daily_calories} kcal
Diet Preference: {dietary_preference}
Food Allergies: {food_allergies}
Local Cuisine: {local_cuisine}
Month: {month}
Return short bullets for each weekday (Mon..Sun).
"""

def call_gemini(prompt_text, model_name=MODEL):
    """
    Calls client.models.generate_content exactly as in the official quickstart:
      client.models.generate_content(model=..., contents=...)
    Returns the best available text representation (response.text or fallbacks).
    """
    try:
        # Per quickstart: use `contents=` (can be a string or structured contents)
        response = client.models.generate_content(model=model_name, contents=prompt_text)
    except Exception as e:
        return f"Error calling Gemini API: {e}"

    # Preferred accessor per docs/examples
    if hasattr(response, "text") and response.text:
        return response.text

    # Fallbacks for various SDK response shapes:
    #  - candidates -> content -> parts -> text
    try:
        cand = getattr(response, "candidates", None)
        if cand:
            first = cand[0]
            # dict-like candidate with nested content->parts
            if isinstance(first, dict):
                content = first.get("content") or {}
                parts = content.get("parts") or []
                if parts:
                    texts = []
                    for p in parts:
                        if isinstance(p, dict) and p.get("text"):
                            texts.append(p.get("text"))
                        elif isinstance(p, str):
                            texts.append(p)
                    if texts:
                        return "\n".join(texts)
            # if candidate object has .content.parts style
            try:
                parts = first.get("content", {}).get("parts", [])
            except Exception:
                parts = []
            if parts:
                out = []
                for p in parts:
                    t = p.get("text") if isinstance(p, dict) else str(p)
                    out.append(t)
                return "\n".join(out)
    except Exception:
        pass

    # Last resort: try response.output or stringified response
    try:
        if hasattr(response, "output"):
            return str(response.output)
    except Exception:
        pass

    return str(response)

def generate_plan(name, age, weight, height, gender, fitness_goal, dietary_preference,
                  food_allergies, local_cuisine, month, include_ayurveda):
    # validate numeric values
    try:
        age_f = float(age); weight_f = float(weight); height_f = float(height)
    except Exception:
        return "Please enter valid numeric values for age, weight, and height."

    bmi = round(weight_f / (height_f / 100) ** 2, 2)
    health_status = "Underweight" if bmi < 18.5 else "Normal weight" if bmi <= 24.9 else "Overweight"
    daily_calories = calculate_calorie_requirements(age_f, gender, weight_f, height_f, fitness_goal)

    metrics = {
        "name": name or "User",
        "age": int(age_f),
        "gender": gender,
        "bmi": bmi,
        "health_status": health_status,
        "fitness_goal": fitness_goal,
        "dietary_preference": dietary_preference,
        "food_allergies": food_allergies or "None",
        "daily_calories": daily_calories,
        "local_cuisine": local_cuisine or "Local",
        "month": month or ""
    }

    prompt_template = AYURVEDA_PROMPT if include_ayurveda else REGULAR_PROMPT
    prompt = prompt_template.format(**metrics)

    text = call_gemini(prompt, model_name=MODEL)
    header = f"BMI: {bmi} ({health_status})\nEstimated calories/day: {daily_calories} kcal\n\n"
    return header + text

# --- Gradio UI ---
with gr.Blocks(title="AI Health Advisor") as demo:
    gr.Markdown("# 🩺 AI-Based Personalized Weekly Diet & Exercise Planner (Gemini quickstart style)")

    with gr.Row():
        with gr.Column():
            name_in = gr.Textbox(label="Name")
            age_in = gr.Number(label="Age", value=25)
            weight_in = gr.Number(label="Weight (kg)", value=70)
            height_in = gr.Number(label="Height (cm)", value=170)
            gender_in = gr.Dropdown(label="Gender", choices=["Male", "Female", "Other"], value="Male")
            goal_in = gr.Dropdown(label="Fitness Goal", choices=["Weight Loss", "Weight Gain", "Maintenance"], value="Maintenance")
            diet_in = gr.Dropdown(label="Diet Preference", choices=["Vegetarian", "Vegan", "Keto", "Halal", "None"], value="None")
            allergy_in = gr.Textbox(label="Food Allergies")
            cuisine_in = gr.Textbox(label="Local Cuisine")
            month_in = gr.Dropdown(label="Month", choices=[
                "January","February","March","April","May","June","July","August","September","October","November","December"
            ], value="October")
            ayurveda_in = gr.Checkbox(label="Include Ayurvedic Insights", value=True)
            btn = gr.Button("Generate Plan")

        with gr.Column():
            output = gr.Textbox(label="Generated Plan", lines=25)

    btn.click(
        generate_plan,
        inputs=[name_in, age_in, weight_in, height_in, gender_in, goal_in, diet_in, allergy_in, cuisine_in, month_in, ayurveda_in],
        outputs=output
    )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", share=False)