Merge pull request #6 from Lucs1590/llm-analysis

LLM analysis

requirements.txt

@@ -1,3 +1,10 @@
 defusedxml==0.7.1
+langchain_core==0.3.6
+langchain_openai==0.2.1
+numpy==2.1.1
+pandas==2.2.3
+python-dotenv==1.0.1
 questionary==2.0.1
+scipy==1.14.1
 tcxreader==0.4.10
+tqdm==4.66.5

src/main.py

@@ -1,14 +1,25 @@
 import re
 import os
+import time
 import logging
 import webbrowser
-import time
-from defusedxml.minidom import parseString
+
+from typing import Tuple
 
 import questionary
+import numpy as np
+import pandas as pd
 
+from tqdm import tqdm
+from dotenv import load_dotenv
+from langchain_openai import ChatOpenAI
+from langchain_core.prompts.prompt import PromptTemplate
+from defusedxml.minidom import parseString
+from scipy.spatial.distance import squareform, pdist
 from tcxreader.tcxreader import TCXReader
 
+
+load_dotenv()
 logger = logging.getLogger()
 
 if not logger.handlers:
@@ -39,15 +50,22 @@ def main():
     else:
         file_path = ask_file_path(file_location)
 
-    if sport in ["Swim", "Other"]:
-        logger.info("Formatting the TCX file to be imported to TrainingPeaks")
-        format_to_swim(file_path)
-    elif sport in ["Bike", "Run"]:
-        logger.info("Validating the TCX file")
-        validate_tcx_file(file_path)
-    else:
-        logger.error("Invalid sport selected")
-        raise ValueError("Invalid sport selected")
+    if file_path:
+        if sport in ["Swim", "Other"]:
+            logger.info(
+                "Formatting the TCX file to be imported to TrainingPeaks"
+            )
+            format_to_swim(file_path)
+        elif sport in ["Bike", "Run"]:
+            logger.info("Validating the TCX file")
+            _, tcx_data = validate_tcx_file(file_path)
+            if ask_llm_analysis():
+                plan = ask_training_plan()
+                logger.info("Performing LLM analysis")
+                perform_llm_analysis(tcx_data, sport, plan)
+        else:
+            logger.error("Invalid sport selected")
+            raise ValueError("Invalid sport selected")
 
     indent_xml_file(file_path)
     logger.info("Process completed successfully!")
@@ -76,7 +94,8 @@ def ask_activity_id() -> str:
 
 def download_tcx_file(activity_id: str, sport: str) -> None:
     if sport in ["Swim", "Other"]:
-        url = f"https://www.strava.com/activities/{activity_id}/export_original"
+        url = f"https://www.strava.com/activities/{
+            activity_id}/export_original"
     else:
         url = f"https://www.strava.com/activities/{activity_id}/export_tcx"
     try:
@@ -104,14 +123,23 @@ def get_latest_download() -> str:
     return latest_file
 
 
-def ask_file_path(file_location) -> str:
-    question = "Enter the path to the TCX file:" if file_location == "Provide path" else "Check if the TCX file was downloaded and then enter the path to the file:"
+def ask_file_path(file_location: str) -> str:
+    if file_location == "Provide path":
+        question = "Enter the path to the TCX file:"
+    else:
+        question = "Check if the TCX was downloaded and validate the file:"
+
     return questionary.path(
         question,
-        validate=os.path.isfile
+        validate=validation,
+        only_directories=False
     ).ask()
 
 
+def validation(path: str) -> bool:
+    return os.path.isfile(path)
+
+
 def format_to_swim(file_path: str) -> None:
     xml_str = read_xml_file(file_path)
     xml_str = modify_xml_header(xml_str)
@@ -138,7 +166,7 @@ def write_xml_file(file_path: str, xml_str: str) -> None:
         xml_file.write(xml_str)
 
 
-def validate_tcx_file(file_path: str) -> bool:
+def validate_tcx_file(file_path: str) -> Tuple[bool, TCXReader]:
     xml_str = read_xml_file(file_path)
     if not xml_str:
         logger.error("The TCX file is empty.")
@@ -151,12 +179,112 @@ def validate_tcx_file(file_path: str) -> bool:
             "The TCX file is valid. You covered a significant distance in this activity, with %d meters.",
             data.distance
         )
-        return True
+        return True, data
     except Exception as err:
         logger.error("Invalid TCX file.")
         raise ValueError(f"Error reading the TCX file: {err}") from err
 
 
+def ask_llm_analysis() -> str:
+    return questionary.confirm(
+        "Do you want to perform AI analysis?",
+        default=False
+    ).ask()
+
+
+def ask_training_plan() -> str:
+    return questionary.text(
+        "Was there anything planned for this training?"
+    ).ask()
+
+
+def perform_llm_analysis(data: TCXReader, sport: str, plan: str) -> str:
+    dataframe = preprocess_trackpoints_data(data)
+
+    prompt = """SYSTEM: You are an AI Assistant that helps athletes to improve their performance.
+    Based on the following csv data that is related to a {sport} training session, carry out an analysis highlighting positive points, where the athlete did well and where he did poorly and what he can do to improve in the next {sport}.
+    <csv_data>
+    {data}
+    </csv_data>
+    """
+    prompt += "plan: {plan}" if plan else ""
+    prompt = PromptTemplate.from_template(prompt)
+    prompt = prompt.format(
+        sport=sport,
+        data=dataframe.to_csv(index=False),
+        plan=plan
+    )
+
+    openai_llm = ChatOpenAI(
+        openai_api_key=os.getenv("OPENAI_API_KEY"),
+        model_name="gpt-4o",
+        max_tokens=1500,
+        temperature=0.6,
+        max_retries=5
+    )
+    response = openai_llm.invoke(prompt)
+    logger.info("AI analysis completed successfully.")
+    logger.info("\nAI response:\n %s \n", response.content)
+    return response.content
+
+
+def preprocess_trackpoints_data(data):
+    dataframe = pd.DataFrame(data.trackpoints_to_dict())
+    dataframe.rename(
+        columns={
+            "distance": "Distance_Km",
+            "time": "Time",
+            "Speed": "Speed_Kmh"
+        }, inplace=True
+    )
+    dataframe["Time"] = dataframe["Time"].apply(lambda x: x.value / 10**9)
+    dataframe["Distance_Km"] = round(dataframe["Distance_Km"] / 1000, 2)
+    dataframe["Speed_Kmh"] = dataframe["Speed_Kmh"] * 3.6
+    dataframe["Pace"] = round(
+        dataframe["Speed_Kmh"].apply(lambda x: 60 / x if x > 0 else 0),
+        2
+    )
+    if dataframe["cadence"].isnull().sum() >= len(dataframe) / 2:
+        dataframe.drop(columns=["cadence"], inplace=True)
+
+    dataframe = dataframe.drop_duplicates()
+    dataframe = dataframe.reset_index(drop=True)
+    dataframe = dataframe.dropna()
+
+    if dataframe.shape[0] > 4000:
+        dataframe = run_euclidean_dist_deletion(dataframe, 0.55)
+    elif dataframe.shape[0] > 1000:
+        dataframe = run_euclidean_dist_deletion(dataframe, 0.35)
+    else:
+        dataframe = run_euclidean_dist_deletion(dataframe, 0.10)
+
+    dataframe["Time"] = pd.to_datetime(
+        dataframe["Time"],
+        unit='s'
+    ).dt.strftime('%H:%M:%S')
+
+    return dataframe
+
+
+def run_euclidean_dist_deletion(dataframe: pd.DataFrame, percentage: float) -> pd.DataFrame:
+    dists = pdist(dataframe, metric='euclidean')
+    dists = squareform(dists)
+    np.fill_diagonal(dists, np.inf)
+
+    total_rows = int(percentage * len(dataframe))
+    with tqdm(total=total_rows, desc="Removing similar points") as pbar:
+        for _ in range(total_rows):
+            min_idx = np.argmin(dists)
+            row, col = np.unravel_index(min_idx, dists.shape)
+            dists[row, :] = np.inf
+            dists[:, col] = np.inf
+            dataframe = dataframe.drop(row)
+            pbar.update(1)
+
+    dataframe = dataframe.reset_index(drop=True)
+    return dataframe
+
+
 def indent_xml_file(file_path: str) -> None:
     try:
         with open(file_path, "r", encoding='utf-8') as xml_file:

tests/test_main.py

@@ -1,7 +1,10 @@
 import os
-#import sys
+# import sys
 import unittest
+
 from unittest.mock import patch
+from pandas import DataFrame
+from tcxreader.tcxreader import TCXReader
 
 # sys.path.append(os.path.abspath(''))
 
@@ -18,13 +21,21 @@
     ask_file_location,
     ask_activity_id,
     ask_file_path,
-    get_latest_download
+    get_latest_download,
+    validation,
+    ask_training_plan,
+    ask_llm_analysis,
+    perform_llm_analysis,
+    preprocess_trackpoints_data,
+    run_euclidean_dist_deletion
 )
 
 
 class TestMain(unittest.TestCase):
     def setUp(self) -> None:
-        pass
+        tcx_reader = TCXReader()
+        self.running_example_data = tcx_reader.read("assets/run.tcx")
+        self.biking_example_data = tcx_reader.read("assets/bike.tcx")
 
     @patch('src.main.webbrowser.open')
     def test_download_tcx_file(self, mock_open):
@@ -96,6 +107,7 @@ def test_validate_tcx_file(self):
         file_path = "assets/bike.tcx"
         result = validate_tcx_file(file_path)
         self.assertTrue(result)
+        self.assertEqual(len(result), 2)
 
     def test_validate_tcx_file_error(self):
         file_path = "assets/swim.tcx"
@@ -185,6 +197,9 @@ def test_main_invalid_sport(self, mock_indent, mock_validate, mock_format, mock_
         mock_validate.assert_not_called()
         mock_indent.assert_not_called()
 
+    @patch('src.main.ask_training_plan')
+    @patch('src.main.perform_llm_analysis')
+    @patch('src.main.ask_llm_analysis')
     @patch('src.main.ask_sport')
     @patch('src.main.ask_file_location')
     @patch('src.main.ask_activity_id')
@@ -194,10 +209,15 @@ def test_main_invalid_sport(self, mock_indent, mock_validate, mock_format, mock_
     @patch('src.main.validate_tcx_file')
     @patch('src.main.indent_xml_file')
     def test_main_bike_sport(self, mock_indent, mock_validate, mock_format, mock_ask_path, mock_download,
-                             mock_ask_id, mock_ask_location, mock_ask_sport):
+                             mock_ask_id, mock_ask_location, mock_ask_sport, mock_llm_analysis, mock_perform_llm,
+                             mock_training_plan):
         mock_ask_sport.return_value = "Bike"
         mock_ask_location.return_value = "Local"
         mock_ask_path.return_value = "assets/bike.tcx"
+        mock_llm_analysis.return_value = True
+        mock_validate.return_value = True, "TCX Data"
+        mock_perform_llm.return_value = "Training Plan"
+        mock_training_plan.return_value = ""
 
         main()
 
@@ -207,6 +227,8 @@ def test_main_bike_sport(self, mock_indent, mock_validate, mock_format, mock_ask
         mock_ask_path.assert_called_once()
         mock_download.assert_not_called()
         mock_format.assert_not_called()
+        mock_llm_analysis.assert_called_once()
+        mock_perform_llm.assert_called_once()
         mock_validate.assert_called_once_with("assets/bike.tcx")
         mock_indent.assert_called_once_with("assets/bike.tcx")
 
@@ -245,7 +267,8 @@ def test_ask_file_path(self):
             result = ask_file_path("Provide path")
             mock_path.assert_called_once_with(
                 "Enter the path to the TCX file:",
-                validate=os.path.isfile
+                validate=validation,
+                only_directories=False
             )
             self.assertEqual(result, "assets/test.tcx")
 
@@ -254,8 +277,9 @@ def test_ask_file_path(self):
             mock_path.return_value.ask.return_value = "assets/downloaded.tcx"
             result = ask_file_path("Download")
             mock_path.assert_called_once_with(
-                "Check if the TCX file was downloaded and then enter the path to the file:",
-                validate=os.path.isfile
+                "Check if the TCX was downloaded and validate the file:",
+                validate=validation,
+                only_directories=False
             )
             self.assertEqual(result, "assets/downloaded.tcx")
 
@@ -275,6 +299,60 @@ def test_get_latest_downloads_with_ask(self, mock_ask_path):
 
         self.assertEqual(result, "assets/bike.tcx")
 
+    def test_validation(self):
+        file_path = "assets/bike.tcx"
+        result = validation(file_path)
+
+        self.assertTrue(result)
+
+    def test_ask_training_plan(self):
+        with patch('src.main.questionary.text') as mock_text:
+            mock_text.return_value.ask.return_value = ""
+            result = ask_training_plan()
+            mock_text.assert_called_once_with(
+                "Was there anything planned for this training?"
+            )
+            self.assertEqual(result, "")
+
+    def test_ask_llm_analysis(self):
+        with patch('src.main.questionary.confirm') as mock_confirm:
+            mock_confirm.return_value.ask.return_value = True
+            result = ask_llm_analysis()
+            mock_confirm.assert_called_once_with(
+                "Do you want to perform AI analysis?",
+                default=False
+            )
+            self.assertTrue(result)
+
+    @patch('src.main.ChatOpenAI')
+    def test_perform_llm_analysis(self, mock_chat):
+        mock_invoke = mock_chat.return_value.invoke.return_value
+        mock_invoke.content = "Training Plan"
+        tcx_data = self.running_example_data
+        sport = "Run"
+        plan = "Training Plan"
+
+        result = perform_llm_analysis(tcx_data, sport, plan)
+        self.assertEqual(result, "Training Plan")
+
+    def test_preprocess_running_trackpoints_data(self):
+        tcx_data = self.running_example_data
+        result = preprocess_trackpoints_data(tcx_data)
+        self.assertEqual(len(result), 1646)
+
+    def test_preprocess_biking_trackpoints_data(self):
+        tcx_data = self.biking_example_data
+        result = preprocess_trackpoints_data(tcx_data)
+        self.assertEqual(len(result), 2028)
+
+    def test_run_euclidean_distance(self):
+        dataframe = DataFrame({
+            'latitude': [1, 2, 3, 3.5, 4, 5, 6, 6.5, 7, 8, 9],
+            'longitude': [1, 2, 3, 3.5, 4, 5, 6, 6.5, 7, 8, 9]
+        })
+        result = run_euclidean_dist_deletion(dataframe, 0.1)
+        self.assertEqual(len(result), 10)
+
 
 if __name__ == '__main__':
     unittest.main()