Update app.py

app.py

@@ -1,132 +1,118 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM  
-import torch 
-
-# --- Page Config ---
-st.set_page_config(page_title="FitPlan AI", page_icon="🏋", layout="wide")
-
-# --- Custom Styling ---
-st.markdown("""
-   <style>
-   .stApp { background-color: #0E1117; color: white; }
-   [data-testid="stSidebar"] { background-color: #161B22 !important; }
-   .stButton > button { width: 100%; border-radius: 8px; font-weight: bold; background-color: #00F260; color: black; }
-   </style>
-   """, unsafe_allow_html=True)
-
-# --- Initialize Session State ---
-if 'user_data' not in st.session_state:
-   st.session_state.user_data = {
-       'name': '', 'age': 25, 'height': 0.0, 'weight': 0.0,
-       'goal': 'Build Muscle', 'level': 'Beginner', 'equip': [], 'gender': 'Male'
-   }
-if 'profile_complete' not in st.session_state:
-   st.session_state.profile_complete = False
-
-# --- Helper Functions ---
-def calculate_bmi(w, h_cm):
-   if h_cm > 0:
-       return round(w / ((h_cm / 100) ** 2), 2)
-   return 0
+from huggingface_hub import InferenceClient
+import os
+
+# ---------- MODEL QUERY FUNCTION ----------
+def query_model(prompt):
+    try:
+        HF_TOKEN = os.getenv("HF_TOKEN")
+
+        client = InferenceClient(
+            model="mistralai/Mistral-7B-Instruct-v0.2",
+            token=HF_TOKEN
+        )
+
+        response = client.chat_completion(
+            messages=[
+                {"role": "system", "content": "You are a certified professional fitness trainer."},
+                {"role": "user", "content": prompt}
+            ],
+            max_tokens=2000,
+            temperature=0.7
+        )
+
+        return response.choices[0].message.content
+
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+# ---------- BMI FUNCTIONS ----------
+def calculate_bmi(weight, height):
+    height_m = height / 100
+    return weight / (height_m ** 2)
 
 def bmi_category(bmi):
-   if bmi < 18.5: return "Underweight"
-   if bmi < 25: return "Normal"
-   if bmi < 30: return "Overweight"
-   return "Obese"
-
-
-@st.cache_resource 
-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()
-
-# --- Sidebar Navigation ---
-with st.sidebar:
-   st.markdown("<h1 style='color: #00F260;'>🏋 FitPlan AI</h1>", unsafe_allow_html=True)
-   menu = st.radio("MENU", ["👤 Profile", "📊 Dashboard"])
-
-# --- NAVIGATION LOGIC ---
-
-if menu == "👤 Profile":
-   st.title("👤 User Profile")
-   
-   name = st.text_input("Name", value=st.session_state.user_data['name'])
-   gender = st.selectbox("Gender", ["Male", "Female", "Other"])
-   col1, col2 = st.columns(2)
-   height = col1.number_input("Height (cm)", min_value=0.0, value=st.session_state.user_data['height'])
-   weight = col2.number_input("Weight (kg)", min_value=0.0, value=st.session_state.user_data['weight'])
-   
-   goal = st.selectbox("Fitness Goal", ["Build Muscle", "Weight Loss", "Strength Gain", "Abs Building", "Flexible"])
-   equipment = st.multiselect("Equipment", ["Dumbbells", "Barbell", "Kettlebell", "Resistance Band", "Yoga Mat", "Full Gym", "No Equipment"])
-   fitness_level = st.select_slider("Fitness Level", options=["Beginner", "Intermediate", "Advanced"])
-
-   bmi = calculate_bmi(weight, height)
-
-  
-   if st.button(" Submit Profile"):
-    
-    if not name:
-        st.error("Please enter your name.")
- 
-    elif height <= 0 or weight <= 0:
-        st.error("Please enter valid height and weight.")
- 
-    elif not equipment:
-        st.error("Please select at least one equipment option.")
- 
+    if bmi < 18.5:
+        return "Underweight"
+    elif bmi < 25:
+        return "Normal Weight"
+    elif bmi < 30:
+        return "Overweight"
     else:
-        st.success(" Profile Submitted Successfully!")
-        
-        # Syncing data for dashboard
-        st.session_state.user_data.update({
-            'name': name, 'height': height, 'weight': weight, 
-            'goal': goal, 'equip': equipment, 'level': fitness_level, 'gender': gender
-        })
-        st.session_state.profile_complete = True
- 
-        bmi_status = bmi_category(bmi)
-        equipment_list = ", ".join(equipment)
- 
-        prompt = f"""
+        return "Obese"
+
+
+# ---------- PROMPT BUILDER ----------
+def build_prompt(name, gender, height, weight, goal, fitness_level, equipment):
+
+    bmi = calculate_bmi(weight, height)
+    bmi_status = bmi_category(bmi)
+
+    equipment_list = ", ".join(equipment) if equipment else "No Equipment"
+
+    prompt = f"""
 You are a certified professional fitness trainer.
-Create a detailed 5-day workout plan.
-User Information:
+Create a structured 5-day personalized workout plan.
+
+User Profile:
+- Name: {name}
 - Gender: {gender}
+- Height: {height} cm
+- Weight: {weight} kg
 - BMI: {bmi:.2f} ({bmi_status})
 - Goal: {goal}
 - Fitness Level: {fitness_level}
-- Equipment Available: {equipment_list}
-Start directly with:
-Day 1:
+- Available Equipment: {equipment_list}
+
+Instructions:
+1. Divide clearly into Day 1 to Day 5.
+2. Include exercise name.
+3. Include sets and reps.
+4. Include rest period.
+5. Adjust intensity based on BMI category.
+6. Avoid unsafe exercises for beginners.
+7. Keep the plan professional and easy to follow.
 """
- 
-        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=0.7,
-                do_sample=True
-            )
- 
-            result = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
- 
-        st.subheader(" Your Personalized Workout Plan")
-        st.write(result)
-
-elif menu == "📊 Dashboard":
-   if not st.session_state.profile_complete:
-       st.warning("Please complete your profile first.")
-   else:
-       ud = st.session_state.user_data
-       bmi = calculate_bmi(ud['weight'], ud['height'])
-       st.title(f"Welcome, {ud['name']}!")
-       st.metric("Current BMI", f"{bmi}")
-       st.write(f"**Current Goal:** {ud['goal']}")
-       st.write(f"**Equipment Available:** {', '.join(ud['equip'])}")
+    return prompt, bmi, bmi_status
+
+
+# ---------- STREAMLIT UI ----------
+st.title("🏋️ AI Personalized 5-Day Workout Planner")
+
+name = st.text_input("Name")
+gender = st.selectbox("Gender", ["Male", "Female", "Other"])
+height = st.number_input("Height (cm)", min_value=100, max_value=250)
+weight = st.number_input("Weight (kg)", min_value=30, max_value=200)
+
+goal = st.selectbox("Fitness Goal", [
+    "Weight Loss",
+    "Muscle Gain",
+    "General Fitness",
+    "Strength"
+])
+
+fitness_level = st.selectbox("Fitness Level", [
+    "Beginner",
+    "Intermediate",
+    "Advanced"
+])
+
+equipment = st.multiselect(
+    "Available Equipment",
+    ["Dumbbells", "Barbell", "Resistance Bands", "Kettlebell", "No Equipment"]
+)
+
+if st.button("Generate 5-Day Plan 💪"):
+
+    prompt, bmi, bmi_status = build_prompt(
+        name, gender, height, weight, goal, fitness_level, equipment
+    )
+
+    st.subheader(f"Your BMI: {bmi:.2f} ({bmi_status})")
+
+    with st.spinner("Creating your personalized workout plan..."):
+        result = query_model(prompt)
+
+    st.markdown("## 🗓️ Your 5-Day Workout Plan")
+    st.write(result)