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Update app.py
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app.py
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import streamlit as st
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# -------------------------
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#
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# -------------------------
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weight_kg = st.number_input("Enter Weight (in kilograms) *", min_value=0.0, format="%.2f")
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# -------------------------
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#
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# -------------------------
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st.
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goal = st.selectbox(
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"
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["Build Muscle", "Weight Loss", "Strength Gain", "Abs Building", "Flexible"]
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)
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equipment = st.multiselect(
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"Available Equipment
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["Dumbbells", "Resistance Band", "Yoga Mat", "Skipping Rope",
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"Weight Plates", "Cycling", "Inclined Bench", "Pullups Bar", "No Equipment"]
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)
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fitness_level = st.radio(
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["Beginner", "Intermediate", "Advanced"]
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)
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# -------------------------
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#
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# -------------------------
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height_m = height_cm / 100 # Convert cm to meters
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bmi = weight / (height_m ** 2)
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return round(bmi, 2)
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return "Underweight"
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elif 18.5 <= bmi < 24.9:
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return "Normal"
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elif 25 <= bmi < 29.9:
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return "Overweight"
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else:
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return "Obese"
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else:
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bmi = calculate_bmi(weight_kg, height_cm)
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category = bmi_category(bmi)
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st.write(f"### π Your BMI: {bmi}")
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st.write(f"### π· BMI Category: {category}")
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st.
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st.write(
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st.write(f"**Goal:** {goal}")
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st.write(f"**Fitness Level:** {fitness_level}")
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st.write(f"**Equipment Available:** {', '.join(equipment) if equipment else 'None selected'}")
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import streamlit as st
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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# -------------------------
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# Page Config
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# -------------------------
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st.set_page_config(page_title="FitPlan AI", page_icon="πͺ")
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st.title("πͺ FitPlan AI β Personalized Workout Generator")
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# -------------------------
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# BMI Functions
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# -------------------------
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def calculate_bmi(weight, height):
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height_m = height / 100
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return weight / (height_m ** 2)
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def bmi_category(bmi):
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if bmi < 18.5:
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return "Underweight"
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elif bmi < 25:
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return "Normal Weight"
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elif bmi < 30:
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return "Overweight"
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else:
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return "Obese"
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# -------------------------
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# Prompt Builder Function
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# -------------------------
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def build_prompt(name, gender, height, weight, goal, fitness_level, equipment):
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bmi = calculate_bmi(weight, height)
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bmi_status = bmi_category(bmi)
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equipment_list = ", ".join(equipment) if equipment else "No Equipment"
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prompt = f"""
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You are a certified professional fitness trainer.
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Create a STRICTLY FORMATTED 5-day personalized workout plan.
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User Profile:
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Name: {name}
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Gender: {gender}
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Height: {height} cm
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Weight: {weight} kg
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BMI: {bmi:.2f} ({bmi_status})
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Goal: {goal}
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Fitness Level: {fitness_level}
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Available Equipment: {equipment_list}
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Instructions:
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β’ Divide into Day 1 to Day 5
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β’ Include Warm-up
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β’ Include Exercises with Sets Γ Reps
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β’ Include Rest Time
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β’ Adjust intensity based on BMI
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"""
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return prompt, bmi, bmi_status
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# -------------------------
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# Load Model (Small for HF)
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# -------------------------
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@st.cache_resource
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def load_model():
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tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-small")
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model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small")
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return tokenizer, model
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tokenizer, model = load_model()
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# -------------------------
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# User Inputs
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# -------------------------
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name = st.text_input("Enter Your Name")
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gender = st.selectbox("Gender", ["Male", "Female", "Other"])
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height = st.number_input("Height (cm)", min_value=0.0)
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weight = st.number_input("Weight (kg)", min_value=0.0)
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goal = st.selectbox(
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"Fitness Goal",
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["Build Muscle", "Weight Loss", "Strength Gain", "Abs Building", "Flexible"]
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)
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equipment = st.multiselect(
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"Available Equipment",
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["Dumbbells", "Resistance Band", "Yoga Mat", "Skipping Rope",
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"Weight Plates", "Cycling", "Inclined Bench", "Pullups Bar", "No Equipment"]
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)
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fitness_level = st.radio(
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"Fitness Level",
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["Beginner", "Intermediate", "Advanced"]
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# -------------------------
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# Generate Plan
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# -------------------------
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if st.button("Generate Workout Plan"):
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if not name or height <= 0 or weight <= 0:
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st.error("Please fill all required fields")
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else:
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prompt, bmi, bmi_status = build_prompt(
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name, gender, height, weight,
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goal, fitness_level, equipment
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)
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st.success(f"BMI: {bmi:.2f} ({bmi_status})")
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with st.spinner("Generating your personalized workout plan..."):
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inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
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outputs = model.generate(
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**inputs,
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max_new_tokens=500,
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temperature=0.3,
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do_sample=True
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)
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result = tokenizer.decode(outputs[0], skip_special_tokens=True)
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st.subheader("ποΈ Your Workout Plan")
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st.write(result)
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