import streamlit as st from dataclasses import dataclass from typing import List, Dict, Optional from datetime import datetime import google.generativeai as genai import os from dotenv import load_dotenv from PIL import Image from langchain_groq import ChatGroq from langchain.schema import HumanMessage, SystemMessage import torch import numpy as np import plotly.graph_objects as go from fpdf import FPDF import time # Load environment variables load_dotenv() # Configure LLMs llm = ChatGroq( temperature=0.2, groq_api_key=os.environ.get("GROQ_API_KEY"), model_name="llama-3.1-70b-versatile" ) genai.configure(api_key=os.getenv("GOOGLE_API_KEY")) model = genai.GenerativeModel( model_name="gemini-1.5-pro", generation_config={ "temperature": 0.2, "top_p": 0.8, "top_k": 40, "max_output_tokens": 2048, } ) # Initialize session state for realtime.py components if 'chat_history' not in st.session_state: st.session_state.chat_history = [] if 'user_data' not in st.session_state: st.session_state.user_data = { 'progress': [], 'meal_logs': [], 'workout_logs': [] } # Code from acountable.py @dataclass class TransformationJourney: user_id: str goal_type: str # fitness, skill, personal growth etc start_date: datetime progress_images: List[Dict[str, str]] # [{date: image_path}] feedback_history: List[Dict[str, str]] # [{date: feedback}] chat_history: List[Dict[str, str]] # [{role: content}] class TransformationCoach: def __init__(self): self.model = model self.llm = llm def analyze_image(self, image: Image) -> str: prompt = """ As a transformation coach, analyze this image in relation to the user's goals. Provide detailed observations about: 1. Current state/progress visible in the image 2. Areas of improvement 3. Notable achievements Be specific and objective in your analysis. """ response = self.model.generate_content([prompt, image]) return response.text def generate_feedback(self, analysis: str, goal_type: str) -> Dict[str, List[str]]: prompt = f""" Based on the following analysis of the user's progress image for their {goal_type} transformation: {analysis} Provide structured feedback in the following format: 1. Specific "DO" recommendations (3-5 points) 2. Specific "DON'T" recommendations (2-3 points) 3. A motivational message Make all feedback actionable and tailored to the analysis. """ response = self.model.generate_content(prompt) feedback_parts = response.text.split("\n\n") return { "dos": feedback_parts[0].split("\n")[1:], "donts": feedback_parts[1].split("\n")[1:], "motivation": feedback_parts[2] } def chat_response(self, question: str, context: List[Dict[str, str]], analysis: str = None) -> str: prompt = f""" You are a supportive transformation coach. Based on the following context: Previous chat: {str(context)} Latest analysis: {analysis if analysis else 'No recent analysis'} User question: {question} Provide a helpful, encouraging response focused on their transformation journey. """ response = self.llm.invoke(prompt) return response.content # Functions from realtime.py def load_llm(): api_key = os.environ.get("GROQ_API_KEY") if api_key is None: st.error("GROQ_API_KEY is not set in the environment variables.") return None llm = ChatGroq( api_key=api_key, model_name="llama-3.1-70b-versatile", temperature=0.7, max_tokens=2048 ) return llm def analyze_image_realtime(image, task="meal"): try: if task == "meal": prompt = """ As a nutrition expert, analyze this meal image and provide: 1. Estimated caloric content 2. Macro-nutrient breakdown 3. Nutritional assessment 4. Suggestions for improvement 5. Any potential health concerns Be specific and detailed in your analysis. """ else: # workout form analysis prompt = """ As a fitness expert, analyze this workout form and provide: 1. Form assessment 2. Potential injury risks 3. Specific corrections needed 4. Benefits of proper form Be specific and detailed in your analysis. """ response = model.generate_content([prompt, image]) return response.text except Exception as e: return f"Error analyzing image: {str(e)}" def track_progress(metric, value, date=None): if date is None: date = datetime.now().strftime("%Y-%m-%d") st.session_state.user_data['progress'].append({ 'date': date, 'metric': metric, 'value': value }) def display_progress_chart(): if not st.session_state.user_data['progress']: return data = st.session_state.user_data['progress'] fig = go.Figure() metrics = set(item['metric'] for item in data) for metric in metrics: metric_data = [item for item in data if item['metric'] == metric] dates = [item['date'] for item in metric_data] values = [item['value'] for item in metric_data] fig.add_trace(go.Scatter(x=dates, y=values, name=metric)) fig.update_layout(title="Your Progress Over Time", xaxis_title="Date", yaxis_title="Value") st.plotly_chart(fig) # Functions from workout.py def generate_meal_plan(weight, height, age, gender, activity_level, goal, dietary_restrictions, health_history, current_eating_habits, meal_preparation, budget, sleep): """Generate personalized meal plan using LLM""" prompt = f""" Based on the following user data, generate a highly personalized and detailed daily meal plan with easily available ingredients: User Profile: - Weight: {weight} kg - Height: {height} cm - Age: {age} - Gender: {gender} - Activity Level: {activity_level} - Goal: {goal} - Dietary Restrictions: {', '.join(dietary_restrictions)} - Health History: {health_history} - Current Eating Habits: {current_eating_habits} - Meal Preparation: {meal_preparation} - Budget: {budget} - Sleep: {sleep} Generate a clear and structured meal plan with: 1. Total Daily Calorie Requirements 2. Macronutrient Distribution (in grams): - Protein - Carbohydrates - Fats Daily Meal Schedule: [Detailed meal schedule content...] """ response = llm.invoke(prompt) return response.content def generate_workout_plan(goal, activity_level, target_areas, workout_time, current_fitness_level, exercise_preferences): """Generate personalized workout plan using LLM""" prompt = f""" Based on: - Fitness Goal: {goal} - Activity Level: {activity_level} - Target Areas: {target_areas} - Workout Time: {workout_time} - Current Fitness Level: {current_fitness_level} - Exercise Preferences: {exercise_preferences} Generate a clear and structured weekly workout plan: [Detailed workout plan content...] """ response = llm.invoke(prompt) return response.content def create_pdf(name, meal_plan, workout_plan): pdf = FPDF() pdf.add_page() pdf.set_font('Arial', 'B', 20) pdf.cell(0, 10, f'Personalized Fitness Plan for {name}', ln=True, align='C') pdf.ln(10) pdf.set_font('Arial', 'B', 16) pdf.cell(0, 10, 'Meal Plan', ln=True) pdf.set_font('Arial', '', 12) pdf.multi_cell(0, 10, meal_plan) pdf.add_page() pdf.set_font('Arial', 'B', 16) pdf.cell(0, 10, 'Workout Plan', ln=True) pdf.set_font('Arial', '', 12) pdf.multi_cell(0, 10, workout_plan) return pdf def main(): st.set_page_config(page_title="AI Fitness Coach", layout="wide") # Chat input needs to be outside tabs user_input = st.chat_input("Type your message here...") if user_input: st.session_state.chat_history.append({"role": "user", "content": user_input}) # Process user input with LLM llm = load_llm() if llm is not None: messages = [ SystemMessage(content="You are an expert health and fitness coach. Provide detailed and personalized advice."), HumanMessage(content=user_input) ] response = llm(messages) st.session_state.chat_history.append({"role": "assistant", "content": response.content}) tab1, tab2, tab3 = st.tabs(["Transformation Journey", "Realtime Tracking", "Workout & Meal Plans"]) with tab1: # Implementation from acountable.py st.title("Transformation Journey Tracker") if 'coach' not in st.session_state: st.session_state.coach = TransformationCoach() if 'journey' not in st.session_state: st.session_state.journey = None # Initialize or load journey if not st.session_state.journey: with st.form("journey_setup"): st.write("Let's start your transformation journey!") user_id = st.text_input("Enter your user ID") goal_type = st.selectbox( "What type of transformation?", ["Fitness", "Skill Development", "Personal Growth", "Other"] ) if st.form_submit_button("Start Journey"): st.session_state.journey = TransformationJourney( user_id=user_id, goal_type=goal_type, start_date=datetime.now(), progress_images=[], feedback_history=[], chat_history=[] ) # Main interaction area if st.session_state.journey: st.subheader("Upload Progress Image") uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"], key="transform_uploader") if uploaded_file: image = Image.open(uploaded_file) st.image(image, caption="Uploaded Image", use_column_width=True) if st.button("Analyze and Get Feedback"): with st.spinner("Analyzing your progress..."): analysis = st.session_state.coach.analyze_image(image) feedback = st.session_state.coach.generate_feedback( analysis, st.session_state.journey.goal_type ) # Display feedback st.subheader("Your Personalized Feedback") st.write("🎯 DO:") for do in feedback["dos"]: st.write(f"✓ {do}") st.write("⚠️ DON'T:") for dont in feedback["donts"]: st.write(f"✗ {dont}") st.write("💪 Motivation:") st.write(feedback["motivation"]) # Store progress current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") image_path = f"progress_images/{st.session_state.journey.user_id}_{current_time}.jpg" st.session_state.journey.progress_images.append({ current_time: image_path }) st.session_state.journey.feedback_history.append({ current_time: feedback }) # Chat interface st.write("---") st.subheader("Chat with Your Coach") # Display chat history for message in st.session_state.journey.chat_history: if message["role"] == "user": st.write("You: " + message["content"]) else: st.write("Coach: " + message["content"]) # Chat input user_question = st.text_input("Ask your coach anything:", key="transform_chat") if st.button("Send", key="transform_send"): if user_question: # Add user message st.session_state.journey.chat_history.append({ "role": "user", "content": user_question }) # Get coach's response latest_analysis = st.session_state.coach.analyze_image(image) if uploaded_file else None response = st.session_state.coach.chat_response( user_question, st.session_state.journey.chat_history, latest_analysis ) # Add coach's response st.session_state.journey.chat_history.append({ "role": "coach", "content": response }) st.experimental_rerun() with tab2: # Implementation from realtime.py st.title("AI Health Coach") st.warning("""This system is for informational purposes only and is not a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or other qualified health provider.""") # Display chat history for message in st.session_state.chat_history: with st.chat_message(message["role"]): st.write(message["content"]) # Image upload and analysis uploaded_file = st.file_uploader("Upload an image for analysis (meal or workout form)", type=['png', 'jpg', 'jpeg'], key="realtime_uploader") if uploaded_file: image = Image.open(uploaded_file) st.image(image, caption="Uploaded Image") analysis_type = st.radio("What would you like to analyze?", ["Meal", "Workout Form"]) if st.button("Analyze Image", key="realtime_analyze"): with st.spinner("Analyzing image..."): results = analyze_image_realtime(image, analysis_type.lower()) st.write("Analysis Results:", results) # Progress tracking section with st.expander("Track Your Progress"): metric = st.selectbox("Select metric to track", ["Weight", "Body Fat %", "Workout Duration", "Calories"]) value = st.number_input("Enter value", key="progress_value") if st.button("Log Progress", key="log_progress"): track_progress(metric, value) st.success("Progress logged successfully!") # Display progress charts display_progress_chart() with tab3: # Implementation from workout.py st.title("AI Fitness Coach") st.markdown("

Welcome to Your Personalized Fitness Journey!

", unsafe_allow_html=True) # Get user inputs st.header("Personal Information") col1, col2, col3 = st.columns(3) with col1: name = st.text_input("What's your name?", placeholder="Enter your name") age = st.number_input("What's your age?", min_value=1, max_value=120, value=25) gender = st.selectbox("What's your gender?", ["Male", "Female"]) health_history = st.text_input("Do you have any medical conditions?") with col2: weight = st.number_input("What's your weight (in kg)?", min_value=1, value=70) height = st.number_input("What's your height (in cm)?", min_value=1, value=170) goal = st.selectbox( "What's your fitness goal?", ["Weight Loss", "Muscle Gain", "Maintenance", "General Fitness"], index=0 ) activity_level = st.selectbox( "What's your activity level?", [ "Sedentary (little to no exercise)", "Lightly Active (1-3 days/week)", "Moderately Active (3-5 days/week)", "Very Active (6-7 days/week)", "Extra Active (very intense exercise daily)" ] ) with col3: dietary_restrictions = st.multiselect( "Do you have any dietary restrictions?", ["None", "Vegetarian", "Vegan", "Gluten-Free", "Dairy-Free", "Keto"] ) current_eating_habits = st.text_input("What does your current diet look like?") meal_preparation = st.text_input("Do you cook your meals or rely on takeout?") budget = st.number_input("What is your monthly budget for groceries/food?", min_value=0) if st.button("Generate Plans", key="generate_plans"): if not name or not goal or not age or not gender or not weight or not height or not activity_level: st.error("Please fill in all required fields") return with st.spinner("Generating your personalized plans..."): meal_plan = generate_meal_plan( weight=weight, height=height, age=age, gender=gender, activity_level=activity_level, goal=goal, dietary_restrictions=dietary_restrictions if dietary_restrictions else ["None"], health_history=health_history, current_eating_habits=current_eating_habits, meal_preparation=meal_preparation, budget=budget, sleep="8 hours" # Default value ) workout_plan = generate_workout_plan( goal=goal, activity_level=activity_level, target_areas="full body", # Default value workout_time=60, # Default value current_fitness_level="Beginner", # Default value exercise_preferences="None" # Default value ) # Create PDF pdf = create_pdf(name, meal_plan, workout_plan) # Display plans in two columns col1, col2 = st.columns(2) with col1: st.subheader("Your Meal Plan") st.write(meal_plan) with col2: st.subheader("Your Workout Plan") st.write(workout_plan) # Add download button st.download_button( label="Download Plan as PDF", data=pdf.output(dest='S').encode('latin-1'), file_name=f"fitness_plan_{name}.pdf", mime="application/pdf" ) if __name__ == "__main__": main()