src/streamlit_app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

# 1. Page Configuration
st.set_page_config(page_title="PERSONALIZED FITPLAN", layout="centered")
def load_model():
    tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base")
    model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
    return tokenizer, model
 
tokenizer, model = load_model()
 


def bmi_category(bmi):
    if bmi < 18.5:
        return "Underweight"
    elif 18.5 <= bmi < 24.9:
        return "Normal"
    elif 25 <= bmi < 29.9:
        return "Overweight"
    else:
        return "Obese"

# 2. Clean Styling
st.markdown("""
<style>
.stApp {
    background: linear-gradient(rgba(0,0,0,0.85), rgba(0,0,0,0.85)), 
                url("https://images.unsplash.com/photo-1534438327276-14e5300c3a48?q=80&w=2070");
    background-size: cover;
}
h1, h2, h3, label, p, span, .stMarkdown {
    color: #FFFFFF !important;
    font-weight: 900 !important;
}
</style>
""", unsafe_allow_html=True)

# 3. Header
st.title("🏋️ PERSONALIZED FITPLAN")
st.markdown("---")

# 4. Athlete Profile Form
st.header("Athlete Profile")
col1, col2 = st.columns(2)

with col1:
    name = st.text_input("NAME (Required)")
    gender = st.selectbox("GENDER", ["Male", "Female", "Other"])
    height_cm = st.number_input("HEIGHT (CM) (Required)", min_value=0.0, step=0.1)

with col2:
    weight_kg = st.number_input("WEIGHT (KG) (Required)", min_value=0.0, step=0.1)
    goal = st.selectbox("FITNESS GOAL", ["Build Muscle", "Weight Loss", "Strength Gain", "Abs Building", "Flexible"])
    level = st.selectbox("FITNESS LEVEL", ["Beginner", "Intermediate", "Advanced"])

equipment_options = [
    "Dumbbells", "Barbell", "Kettlebells", "Weight Plates", 
    "Resistance Band", "Yoga Mat", "Pull-up Bar", "Bench Press", "No Equipment"
]
equipment = st.multiselect("AVAILABLE EQUIPMENT", equipment_options)

st.markdown("---")

# 5. Logic and Results
if st.button(" Submit Profile"):
 
    if not name:
        st.error("Please enter your name.")
 
    # 🔧 FIXED VARIABLES
    elif height_cm <= 0 or weight_kg <= 0:
        st.error("Please enter valid height and weight.")
 
    elif not equipment:
        st.error("Please select at least one equipment option.")
 
    else:
        st.success(" Profile Submitted Successfully!")

        # 🔧 BMI CALCULATION (missing earlier)
        height_m = height_cm / 100
        bmi = weight_kg / (height_m ** 2)
        bmi_status = bmi_category(bmi)

        equipment_list = ", ".join(equipment)

prompt = f"""
You are a certified professional gym trainer.

Generate a COMPLETE 5-day workout schedule.

FORMAT:

Day 1:
Warm-up:
-
Workout:
-
Rest:

Day 2:
Warm-up:
-
Workout:
-
Rest:

Day 3:
Warm-up:
-
Workout:
-
Rest:

Day 4:
Warm-up:
-
Workout:
-
Rest:

Day 5:
Warm-up:
-
Workout:
-
Rest:

User Details:
Gender: {gender}
BMI: {bmi:.2f} ({bmi_status})
Goal: {goal}
Fitness Level: {level}
Available Equipment: {equipment_list}

Generate the full 5-day workout plan.
"""
 
        with st.spinner("Generating your AI workout plan..."):
            inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
            outputs = model.generate(
                **inputs,
                max_new_tokens=600,
                temperature=0.3,
                do_sample=False
            )

            result = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
 
        st.subheader(" Your Personalized Workout Plan")
        st.write(result)

    # Validation block (unchanged)
    if not name or height_cm <= 0 or weight_kg <= 0:
        st.error("🚨 VALIDATION FAILED: PLEASE PROVIDE NAME, HEIGHT, AND WEIGHT.")
    else:
        height_m = height_cm / 100
        bmi_val = round(weight_kg / (height_m ** 2), 2)

        if bmi_val < 18.5: category = "Underweight"
        elif 18.5 <= bmi_val < 24.9: category = "Normal"
        elif 25 <= bmi_val < 29.9: category = "Overweight"
        else: category = "Obese"

        st.success(f"PROFILE SUBMITTED SUCCESSFULLY! HELLO {name.upper()}")
        st.markdown(f"### BMI: {bmi_val} | CATEGORY: {category.upper()}")

        athlete_data = {
            "Name": name,
            "Gender": gender,
            "BMI": bmi_val,
            "Goal": goal,
            "Fitness Level": level,
            "Equipment": equipment
        }
        st.json(athlete_data)