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Update src/streamlit_app.py
Browse files- src/streamlit_app.py +59 -39
src/streamlit_app.py
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import streamlit as st
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from transformers import
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# -------------------------
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# PAGE CONFIG
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# -------------------------
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@@ -69,54 +70,73 @@ with st.form("fitness_form"):
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# -------------------------
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# HANDLE SUBMISSION
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# -------------------------
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elif height <= 0 or weight <= 0:
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st.error("Height and Weight must be positive values.")
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elif not equipment:
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st.error("Please select at least one equipment option.")
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else:
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# Calculate BMI
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user_bmi = calculate_bmi(weight, height)
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bmi_status = get_category(user_bmi)
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st.success(
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equipment_list = ", ".join(equipment)
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# FLAN-T5 works best with instruction-style prompts
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prompt = f"""
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Create a structured 5-day workout plan.
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User Details:
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Name: {name}
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BMI: {user_bmi} ({bmi_status})
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Goal: {goal}
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Fitness Level: {level}
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Equipment: {equipment_list}
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Include:
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- Warm-up
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- Exercises with sets and reps
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- Rest time
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- Day-wise breakdown
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- Safety advice
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"""
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with st.spinner("Generating your AI workout plan..."):
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import streamlit as st
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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import torch
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# -------------------------
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# PAGE CONFIG
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# -------------------------
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# -------------------------
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# HANDLE SUBMISSION
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# -------------------------
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# SUBMIT BUTTON
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if st.button(" Submit Profile"):
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if not name:
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st.error("Please enter your name.")
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elif height <= 0 or weight <= 0:
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st.error("Please enter valid height and weight.")
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elif not equipment:
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st.error("Please select at least one equipment option.")
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else:
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st.success(" Profile Submitted Successfully!")
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bmi_status = bmi_category(bmi)
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equipment_list = ", ".join(equipment)
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prompt = f"""
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You are a certified professional fitness trainer.
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Create a detailed 5-day workout plan.
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User Information:
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- Gender: {gender}
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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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- Equipment Available: {equipment_list}
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Start directly with:
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Day 1:
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"""
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with st.spinner("Generating your AI 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=900,
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temperature=0.7,
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do_sample=True
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)
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result = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
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st.subheader(" Your Personalized Workout Plan")
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st.write(result)
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