Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 import matplotlib.pyplot as plt
-import pandas as pd
-import numpy as np
+import pandas as np
 from datetime import datetime
 from langchain_core.messages import HumanMessage
 from tools import tools
@@ -15,89 +14,77 @@ graph = create_workflow()
 # Helper Functions
 def run_graph(input_message, history, user_details):
     try:
-        # Relevant fitness-related keywords to handle irrelevant user prompts
-        relevant_keywords = [
-            "workout", "training", "exercise", "cardio", "strength training", "hiit (high-intensity interval training)",
-            "flexibility", "yoga", "pilates", "aerobics", "crossfit", "bodybuilding", "endurance", "running",
-            "cycling", "swimming", "martial arts", "stretching", "warm-up", "cool-down", 
-            "diet plan", "meal plan", "macronutrients", "micronutrients", "vitamins", "minerals", "protein",
-            "carbohydrates", "fats", "calories", "calorie", "daily", "nutrition", "supplements", "hydration", "weightloss",
-            "weight gain", "healthy eating", "health", "fitness", "intermittent fasting", "keto diet", "vegan diet", "paleo diet",
-            "mediterranean diet", "gluten-free", "low-carb", "high-protein", "bmi", "calculate", "body mass index", "calculator",
-            "mental health", "mindfulness", "meditation", "stress management", "anxiety relief", "depression",
-            "positive thinking", "motivation", "self-care", "relaxation", "sleep hygiene", "therapy",
-            "counseling", "cognitive-behavioral therapy (cbt)", "mood tracking", "mental", "emotional well-being",
-            "healthy lifestyle", "fitness goals", "health routines", "daily habits", "ergonomics",
-            "posture", "work-life balance", "workplace", "habit tracking", "goal setting", "personal growth",
-            "injury prevention", "recovery", "rehabilitation", "physical therapy", "sports injuries",
-            "pain management", "recovery techniques", "foam rolling", "stretching exercises",
-            "injury management", "injuries", "apps", "health tracking", "wearable technology", "equipment",
-            "home workouts", "gym routines", "outdoor activities", "sports", "wellness tips", "water", "adult", "adults",
-            "child", "children", "infant", "sleep", "habit", "habits", "routine", "weight", "fruits", "vegetables", "lose", "lost weight", "weight-loss",
-            "chicken", "veg", "vegetarian", "non-veg", "non-vegetarian", "plant", "plant-based", "plant based", "fat", "resources",
-            "help", "cutting", "bulking", "link", "links", "website", "online", "websites", "peace", "mind", "equipments", "equipment",
-            "watch", "tracker", "watch", "band", "height", "injured", "quick", "remedy", "solution", "solutions", "pain", "male", "female", "kilograms", "kg", "Pounds", 
-            "lbs"
-        ]
+        system_prompt = (
+            "You are a fitness and health assistant. "
+            "Provide advice based on the user's personal details and goals. "
+            "If user details like weight, height, and activity level are provided, prioritize personalized advice. "
+            "Incorporate metrics such as BMI and daily caloric needs directly into your suggestions. "
+            "Visualize the user's metrics and provide actionable advice tailored to their needs. "
+            "If details are missing, provide general advice but encourage users to share their metrics for tailored guidance."
+        )
 
-        greetings = ["hello", "hi", "how are you doing"]
+        # Compose input for the LLM
+        user_details_summary = (
+            f"Name: {user_details.get('name', 'Unknown')}, "
+            f"Age: {user_details.get('age', 'Unknown')}, "
+            f"Gender: {user_details.get('gender', 'Unknown')}, "
+            f"Weight: {user_details.get('weight', 'Unknown')} kg, "
+            f"Height: {user_details.get('height', 'Unknown')} cm, "
+            f"Activity Level: {user_details.get('activity_level', 'Unknown')}"
+        )
 
-        if any(keyword in input_message.lower() for keyword in relevant_keywords):
-            response = graph.invoke({
-                "messages": [HumanMessage(content=input_message)],
-                "user_details": user_details  # Pass user-specific data for customization
-            })
-            return response['messages'][1].content
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": f"User details: {user_details_summary}"},
+            {"role": "user", "content": input_message}
+        ]
 
-        elif any(keyword in input_message.lower() for keyword in greetings):
-            return "Hi there, I am FIT bot, your personal wellbeing coach! Let me know your fitness goals or questions."
+        # Generate the response
+        response = graph.invoke({"messages": messages})
+        return response["messages"][1]["content"]
 
-        else:
-            return "I'm here to assist with fitness, nutrition, mental health, and related topics. Please ask questions related to these areas."
     except Exception as e:
         return f"An error occurred while processing your request: {e}"
 
 
-def calculate_bmi(height, weight, gender):
-    """
-    Calculate BMI and determine the category based on height, weight, and gender.
-    """
-    try:
-        height_m = height / 100
-        bmi = weight / (height_m ** 2)
-        if bmi < 18.5:
-            status = "underweight"
-        elif 18.5 <= bmi < 24.9:
-            status = "normal weight"
-        elif 25 <= bmi < 29.9:
-            status = "overweight"
-        else:
-            status = "obese"
-
-        return bmi, status
-    except:
-        return None, "Invalid height or weight provided."
-
-
-def visualize_bmi(bmi):
-    """
-    Create a visualization for BMI, showing the user's value against standard categories.
-    """
+def calculate_bmi(height, weight):
+    height_m = height / 100
+    bmi = weight / (height_m ** 2)
+    if bmi < 18.5:
+        status = "underweight"
+    elif 18.5 <= bmi < 24.9:
+        status = "normal weight"
+    elif 25 <= bmi < 29.9:
+        status = "overweight"
+    else:
+        status = "obese"
+    return bmi, status
+
+
+def visualize_bmi_and_calories(bmi, calories):
+    # Create a combined visualization
     categories = ["Underweight", "Normal Weight", "Overweight", "Obese"]
-    bmi_ranges = [18.5, 24.9, 29.9, 40]
-    x_pos = np.arange(len(categories))
-    user_position = min(len(bmi_ranges), sum(bmi > np.array(bmi_ranges)))
-
-    plt.figure(figsize=(8, 4))
-    plt.bar(x_pos, bmi_ranges, color=['blue', 'green', 'orange', 'red'], alpha=0.6, label="BMI Categories")
-    plt.axhline(y=bmi, color='purple', linestyle='--', linewidth=2, label=f"Your BMI: {bmi:.2f}")
-    plt.xticks(x_pos, categories)
-    plt.ylabel("BMI Value")
-    plt.title("BMI Visualization")
-    plt.legend()
-    plt.tight_layout()
+    bmi_values = [18.5, 24.9, 29.9, 40]
+    calorie_range = [1500, 2000, 2500, 3000]
+
+    fig, ax1 = plt.subplots(figsize=(10, 6))
+
+    # BMI visualization
+    ax1.bar(categories, bmi_values, color=['blue', 'green', 'orange', 'red'], alpha=0.6, label="BMI Ranges")
+    ax1.axhline(y=bmi, color='purple', linestyle='--', linewidth=2, label=f"Your BMI: {bmi:.2f}")
+    ax1.set_ylabel("BMI Value")
+    ax1.set_title("BMI and Caloric Needs Visualization")
+    ax1.legend(loc="upper left")
 
-    plt_path = "bmi_chart.png"
+    # Calorie visualization
+    ax2 = ax1.twinx()
+    ax2.plot(categories, calorie_range, 'o-', color='magenta', label="Calorie Ranges")
+    ax2.axhline(y=calories, color='cyan', linestyle='--', linewidth=2, label=f"Your Calorie Needs: {calories:.2f} kcal")
+    ax2.set_ylabel("Calories")
+    ax2.legend(loc="upper right")
+
+    plt.tight_layout()
+    plt_path = "bmi_calorie_chart.png"
     plt.savefig(plt_path)
     plt.close()
 
@@ -105,27 +92,20 @@ def visualize_bmi(bmi):
 
 
 def calculate_calories(age, weight, height, activity_level, gender):
-    """
-    Estimate daily calorie needs based on user inputs.
-    """
-    try:
-        if gender.lower() == "male":
-            bmr = 10 * weight + 6.25 * height - 5 * age + 5
-        else:
-            bmr = 10 * weight + 6.25 * height - 5 * age - 161
-
-        activity_multipliers = {
-            "Sedentary": 1.2,
-            "Lightly active": 1.375,
-            "Moderately active": 1.55,
-            "Very active": 1.725,
-            "Extra active": 1.9,
-        }
+    if gender.lower() == "male":
+        bmr = 10 * weight + 6.25 * height - 5 * age + 5
+    else:
+        bmr = 10 * weight + 6.25 * height - 5 * age - 161
+
+    activity_multipliers = {
+        "Sedentary": 1.2,
+        "Lightly active": 1.375,
+        "Moderately active": 1.55,
+        "Very active": 1.725,
+        "Extra active": 1.9,
+    }
 
-        calories = bmr * activity_multipliers.get(activity_level, 1.2)
-        return calories
-    except Exception:
-        return "Invalid inputs for calorie calculation."
+    return bmr * activity_multipliers.get(activity_level, 1.2)
 
 # Interface Components
 with gr.Blocks() as demo:
@@ -147,19 +127,52 @@ with gr.Blocks() as demo:
     # Outputs
     bmi_output = gr.Label(label="BMI Result")
     calorie_output = gr.Label(label="Calorie Needs")
-    bmi_chart = gr.Image(label="BMI Chart")
+    bmi_chart = gr.Image(label="BMI and Calorie Chart")
+
+    chatbot = gr.Chatbot(label="Chat with FIT.AI")
+    text_input = gr.Textbox(placeholder="Type your question here...", label="Your Question")
+    submit_button = gr.Button("Submit")
+    clear_button = gr.Button("Clear Chat")
+
+    def submit_message(message, history=[]):
+        user_details = {
+            "name": user_name.value,
+            "age": user_age.value,
+            "weight": user_weight.value,
+            "height": user_height.value,
+            "activity_level": activity_level.value,
+            "gender": user_gender.value
+        }
+        bmi, status = calculate_bmi(user_details['height'], user_details['weight'])
+        calories = calculate_calories(
+            user_details['age'], user_details['weight'], user_details['height'], user_details['activity_level'], user_details['gender']
+        )
+        chart_path = visualize_bmi_and_calories(bmi, calories)
+
+        # Append metrics to response
+        personalized_metrics = (
+            f"Based on your metrics:\n"
+            f"- BMI: {bmi:.2f} ({status})\n"
+            f"- Daily Caloric Needs: {calories:.2f} kcal\n"
+            f"Visualized metrics are displayed alongside."
+        )
+
+        response = run_graph(f"{message}\n\n{personalized_metrics}", history, user_details)
+        history.append((f"{user_details['name']}", message))
+        history.append(("FIT.AI", response))
+        return history, ""
+
+    submit_button.click(submit_message, inputs=[text_input, chatbot], outputs=[chatbot, text_input])
+    clear_button.click(lambda: ([], ""), inputs=None, outputs=[chatbot, text_input])
 
     # Actions
     calculate_button = gr.Button("Calculate")
 
     def calculate_metrics(age, weight, height, gender, activity_level):
-        bmi, status = calculate_bmi(height, weight, gender)
-        if bmi:
-            bmi_path = visualize_bmi(bmi)
-            calories = calculate_calories(age, weight, height, activity_level, gender)
-            return f"Your BMI is {bmi:.2f}, considered {status}.", f"Daily calorie needs: {calories:.2f} kcal", bmi_path
-        else:
-            return "Invalid inputs.", "", ""
+        bmi, status = calculate_bmi(height, weight)
+        bmi_path = visualize_bmi_and_calories(bmi, calculate_calories(age, weight, height, activity_level, gender))
+        calories = calculate_calories(age, weight, height, activity_level, gender)
+        return f"Your BMI is {bmi:.2f}, considered {status}.", f"Daily calorie needs: {calories:.2f} kcal", bmi_path
 
     calculate_button.click(
         calculate_metrics,