Create w_debugging/app.py

mylab/w_debugging/app.py

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+import gradio as gr
+import matplotlib.pyplot as plt
+import pandas as np
+import tempfile
+from datetime import datetime
+from langchain_core.messages import HumanMessage
+from tools import tools
+from agents import *
+from config import *
+from workflow import create_workflow
+import logging
+import threading
+import queue
+
+# Global timeout variable
+TIMEOUT_SECONDS = 300
+
+# Initialize workflow
+graph = create_workflow()
+
+# Configure logging
+logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger(__name__)
+
+# Debug logs for displaying in UI
+debug_logs = []
+
+# Helper Functions
+def run_graph(input_message, history, user_details):
+    def invoke_workflow(q):
+        try:
+            system_prompt = (
+                "You are a fitness and health assistant. "
+                "Provide actionable, tailored advice based on the user's personal details and goals. "
+                "Incorporate BMI and daily caloric needs into your suggestions. "
+                "Offer steps for achieving goals like weight loss or fitness improvement."
+            )
+
+            # Summarize user details
+            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')}"
+            )
+
+            # Messages for the workflow
+            messages = [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": f"User details: {user_details_summary}"},
+                {"role": "user", "content": input_message}
+            ]
+
+            logger.debug("Invoking workflow with messages: %s", messages)
+            debug_logs.append(f"Workflow Input: {messages}")
+
+            response = graph.invoke({"messages": messages})
+            logger.debug("Workflow response: %s", response)
+            debug_logs.append(f"Workflow Response: {response}")
+
+            # Extract LLM response
+            llm_response = None
+            for msg in response.get("messages", []):
+                if isinstance(msg, HumanMessage) and msg.name in ["nutritionist", "workout_coach"]:
+                    llm_response = msg.content
+                    break
+
+            if llm_response:
+                q.put(llm_response)
+            else:
+                q.put("The workflow did not return a valid response. Please try again.")
+        except Exception as e:
+            logger.error("Error in run_graph: %s", str(e))
+            debug_logs.append(f"Error: {str(e)}")
+            q.put(f"An error occurred: {e}")
+
+    q = queue.Queue()
+    thread = threading.Thread(target=invoke_workflow, args=(q,))
+    thread.start()
+    thread.join(timeout=TIMEOUT_SECONDS)
+
+    if thread.is_alive():
+        logger.error(f"Workflow timed out after {TIMEOUT_SECONDS} seconds.")
+        debug_logs.append(f"Workflow timed out after {TIMEOUT_SECONDS} seconds.")
+        return f"The request took longer than {TIMEOUT_SECONDS} seconds and timed out. Please try again."
+    return q.get()
+
+
+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):
+    categories = ["Underweight", "Normal Weight", "Overweight", "Obese"]
+    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")
+
+    # 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()
+
+    # Save visualization to a temporary file
+    temp_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
+    try:
+        plt.savefig(temp_file.name)
+    finally:
+        plt.close()
+
+    return temp_file.name
+
+
+def calculate_calories(age, weight, height, activity_level, gender):
+    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,
+    }
+
+    activity_level = activity_level.lower()
+    return bmr * activity_multipliers.get(activity_level, 1.2)
+
+# Interface Components
+with gr.Blocks() as demo:
+    gr.Markdown("<strong>FIT.AI - Your Fitness and Wellbeing Coach</strong>")
+
+    debug_logs = []
+
+    with gr.Tabs():
+        with gr.Tab("Visualization + Chat"):
+            # User Input
+            with gr.Row():
+                user_name = gr.Textbox(placeholder="Enter your name", label="Name")
+                user_age = gr.Number(label="Age (years)", value=25, precision=0)
+                user_gender = gr.Dropdown(choices=["Male", "Female"], label="Gender", value="Male")
+                user_weight = gr.Number(label="Weight (kg)", value=70, precision=1)
+                user_height = gr.Number(label="Height (cm)", value=170, precision=1)
+                activity_level = gr.Dropdown(
+                    choices=["Sedentary", "Lightly active", "Moderately active", "Very active", "Extra active"],
+                    label="Activity Level",
+                    value="Moderately active"
+                )
+
+            # Visualization Output
+            bmi_chart = gr.Image(label="BMI and Calorie Chart")
+            debug_output = gr.Textbox(label="Debug Logs", interactive=False, lines=10)
+
+            # Chat Outputs
+            with gr.Row():
+                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=[]):
+                try:
+                    # Debug logging
+                    debug_logs.clear()
+                    debug_logs.append("Processing user request...")
+
+                    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
+                    }
+                    debug_logs.append(f"User Details: {user_details}")
+
+                    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)
+
+                    user_prompt = (
+                        f"User wants advice on: {message}\n"
+                        f"User Details:\n"
+                        f"- Name: {user_details['name']}\n"
+                        f"- Age: {user_details['age']}\n"
+                        f"- Gender: {user_details['gender']}\n"
+                        f"- Weight: {user_details['weight']} kg\n"
+                        f"- Height: {user_details['height']} cm\n"
+                        f"- Activity Level: {user_details['activity_level']}\n"
+                        f"- BMI: {bmi:.2f} ({status})\n"
+                        f"- Daily Caloric Needs: {calories:.2f} kcal\n"
+                        f"\nProvide tailored advice based on these metrics."
+                    )
+                    debug_logs.append(f"Composed user prompt: {user_prompt}")
+
+                    response = run_graph(user_prompt, history, user_details)
+                    debug_logs.append(f"Received response: {response}")
+
+                    # Append the tailored response to the chatbot history
+                    history.append(("User", message))
+                    if isinstance(response, str):
+                        history.append(("FIT.AI", response))
+                    else:
+                        history.append(("FIT.AI", "An unexpected response was received. Check debug logs for details."))
+
+                    return history, chart_path, "\n".join(debug_logs)
+
+                except Exception as e:
+                    error_message = f"Error: {str(e)}"
+                    logger.error(error_message)
+                    debug_logs.append(error_message)
+                    return history + [("FIT.AI", "An error occurred. Please try again.")], None, "\n".join(debug_logs)
+
+            submit_button.click(submit_message, inputs=[text_input, chatbot], outputs=[chatbot, bmi_chart, debug_output])
+            clear_button.click(lambda: ([], "", ""), inputs=None, outputs=[chatbot, bmi_chart, debug_output])
+
+        # Calculator + Visualization Tab
+        with gr.Tab("Calculator + Visualization"):
+            user_age_calc = gr.Number(label="Age (years)", value=25, precision=0)
+            user_gender_calc = gr.Dropdown(choices=["Male", "Female"], label="Gender", value="Male")
+            user_weight_calc = gr.Number(label="Weight (kg)", value=70, precision=1)
+            user_height_calc = gr.Number(label="Height (cm)", value=170, precision=1)
+            activity_level_calc = gr.Dropdown(
+                choices=["Sedentary", "Lightly active", "Moderately active", "Very active", "Extra active"],
+                label="Activity Level",
+                value="Moderately active"
+            )
+
+            bmi_output = gr.Label(label="BMI Result")
+            calorie_output = gr.Label(label="Calorie Needs")
+            bmi_chart_calc = gr.Image(label="BMI and Calorie Chart")
+
+            calculate_button = gr.Button("Calculate")
+
+            def calculate_metrics(age, weight, height, gender, activity_level):
+                try:
+                    debug_logs.clear()
+                    debug_logs.append("Processing calculator request...")
+
+                    bmi, status = calculate_bmi(height, weight)
+                    calories = calculate_calories(age, weight, height, activity_level, gender)
+                    chart_path = visualize_bmi_and_calories(bmi, calories)
+
+                    debug_logs.append(f"Calculated metrics: BMI={bmi:.2f}, Calories={calories:.2f}")
+                    return f"Your BMI is {bmi:.2f}, considered {status}.", f"Daily calorie needs: {calories:.2f} kcal", chart_path
+
+                except Exception as e:
+                    error_message = f"Error: {str(e)}"
+                    logger.error(error_message)
+                    debug_logs.append(error_message)
+                    return "An error occurred.", "", None
+
+            calculate_button.click(
+                calculate_metrics,
+                inputs=[user_age_calc, user_weight_calc, user_height_calc, user_gender_calc, activity_level_calc],
+                outputs=[bmi_output, calorie_output, bmi_chart_calc]
+            )
+
+demo.launch(share=True)