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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/theorousseaux/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n",
" warnings.warn(\n"
]
}
],
"source": [
"from ultralytics import YOLO\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"id_joints_dict = {0: 'nose',\n",
" 1: 'left_eye',\n",
" 2: 'right_eye',\n",
" 3: 'left_ear',\n",
" 4: 'right_ear',\n",
" 5: 'left_shoulder',\n",
" 6: 'right_shoulder',\n",
" 7: 'left_elbow',\n",
" 8: 'right_elbow',\n",
" 9: 'left_wrist',\n",
" 10: 'right_wrist',\n",
" 11: 'left_hip',\n",
" 12: 'right_hip',\n",
" 13: 'left_knee',\n",
" 14: 'right_knee',\n",
" 15: 'left_ankle',\n",
" 16: 'right_ankle'}\n",
"joints_id_dict = {v: k for k, v in id_joints_dict.items()}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"model = YOLO('yolov8n-pose.pt')\n",
"\n",
"def get_keypoints_from_keypoints(video_path, model=YOLO('yolov8n-pose.pt')):\n",
"\n",
" keypoints = []\n",
" results = model(video_path, save=True, show_conf=False, show_boxes=False)\n",
" for frame in results:\n",
" keypoints.append(frame.keypoints.xy)\n",
"\n",
" return keypoints"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
"errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
"\n",
"Example:\n",
" results = model(source=..., stream=True) # generator of Results objects\n",
" for r in results:\n",
" boxes = r.boxes # Boxes object for bbox outputs\n",
" masks = r.masks # Masks object for segment masks outputs\n",
" probs = r.probs # Class probabilities for classification outputs\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/theorousseaux/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
},
{
"name": "stdout",
"output_type": "stream",
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"video 1/1 (frame 375/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 115.2ms\n",
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"video 1/1 (frame 379/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 122.3ms\n",
"video 1/1 (frame 380/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 128.6ms\n",
"video 1/1 (frame 381/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 119.3ms\n",
"video 1/1 (frame 382/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 114.3ms\n",
"video 1/1 (frame 383/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 126.5ms\n",
"video 1/1 (frame 384/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 156.6ms\n",
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"video 1/1 (frame 386/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 117.9ms\n",
"video 1/1 (frame 387/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 116.1ms\n",
"video 1/1 (frame 388/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 126.8ms\n",
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"video 1/1 (frame 390/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 114.9ms\n",
"video 1/1 (frame 391/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 115.6ms\n",
"video 1/1 (frame 392/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 122.9ms\n",
"video 1/1 (frame 393/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 132.3ms\n",
"video 1/1 (frame 394/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 158.4ms\n",
"video 1/1 (frame 395/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 113.0ms\n",
"video 1/1 (frame 396/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 120.3ms\n",
"video 1/1 (frame 397/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 120.0ms\n",
"video 1/1 (frame 398/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 157.0ms\n",
"video 1/1 (frame 399/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 112.3ms\n",
"video 1/1 (frame 400/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 142.3ms\n",
"video 1/1 (frame 401/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 145.3ms\n",
"video 1/1 (frame 402/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 126.9ms\n",
"video 1/1 (frame 403/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 130.2ms\n",
"video 1/1 (frame 404/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 139.4ms\n",
"video 1/1 (frame 405/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 115.2ms\n",
"Speed: 6.9ms preprocess, 162.9ms inference, 6.5ms postprocess per image at shape (1, 3, 640, 576)\n",
"Results saved to \u001b[1m/Users/theorousseaux/Documents/Hackathon/FitnessEquation/runs/pose/predict5\u001b[0m\n"
]
}
],
"source": [
"keypoints = get_keypoints_from_keypoints(model, '../../data/pose/squat.mp4')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def calculate_angle(a, b, c):\n",
"\n",
" \"\"\"\n",
" Calculates the angle between three joints.\n",
"\n",
" Args:\n",
" a (tuple): coordinates of the first joint\n",
" b (tuple): coordinates of the second joint\n",
" c (tuple): coordinates of the third joint\n",
"\n",
" Returns:\n",
" angle (float): angle between the three joints\n",
" \"\"\"\n",
" \n",
" ba = np.array(a) - np.array(b)\n",
" bc = np.array(c) - np.array(b)\n",
"\n",
" cosine_angle = np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))\n",
" angle = np.arccos(cosine_angle)\n",
"\n",
" return np.degrees(angle)\n",
"\n",
"def compute_left_knee_angle(pose):\n",
"\n",
" \"\"\"\n",
" Computes the knee angle.\n",
"\n",
" Args:\n",
" pose (dict): pose dictionary\n",
"\n",
" Returns:\n",
" knee_angle (float): knee angle\n",
" \"\"\"\n",
"\n",
" left_hip = pose[0][joints_id_dict['left_hip']]\n",
" left_knee = pose[0][joints_id_dict['left_knee']]\n",
" left_ankle = pose[0][joints_id_dict['left_ankle']]\n",
"\n",
" knee_angle = calculate_angle(left_hip, left_knee, left_ankle)\n",
"\n",
" return knee_angle\n",
"\n",
"def compute_right_knee_angle(pose):\n",
"\n",
" \"\"\"\n",
" Computes the knee angle.\n",
"\n",
" Args:\n",
" pose (dict): pose dictionary\n",
"\n",
" Returns:\n",
" knee_angle (float): knee angle\n",
" \"\"\"\n",
"\n",
" right_hip = pose[0][joints_id_dict['right_hip']]\n",
" right_knee = pose[0][joints_id_dict['right_knee']]\n",
" right_ankle = pose[0][joints_id_dict['right_ankle']]\n",
"\n",
" knee_angle = calculate_angle(right_hip, right_knee, right_ankle)\n",
"\n",
" return knee_angle\n",
"\n",
"def moving_average(data, window_size):\n",
"\n",
" \"\"\"\n",
" Computes the moving average of a list.\n",
"\n",
" Args:\n",
" data (list): list of values\n",
" window_size (int): size of the window\n",
"\n",
" Returns:\n",
" avg (list): list of moving average values\n",
" \"\"\"\n",
"\n",
" avg = []\n",
" for i in range(len(data) - window_size + 1):\n",
" avg.append(sum(data[i:i + window_size]) / window_size)\n",
"\n",
" return avg"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"angles = []\n",
"\n",
"for i in range(len(keypoints)):\n",
" angles.append(compute_left_knee_angle(keypoints[i]))\n",
"\n",
"angles = moving_average(angles, 10)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1363b3be0>]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.plot(angles)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"Who's there?\\n\\n(Please provide the next line of the knock-knock joke.)\", response_metadata={'token_usage': {'prompt_tokens': 5, 'total_tokens': 26, 'completion_tokens': 21}, 'model': 'mistral-large-latest', 'finish_reason': 'stop'}, id='run-f72f8b00-6073-439e-9b82-cc50101f0809-0')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.messages import HumanMessage\n",
"from langchain_mistralai.chat_models import ChatMistralAI\n",
"\n",
"# If api_key is not passed, default behavior is to use the `MISTRAL_API_KEY` environment variable.\n",
"llm = ChatMistralAI(model='mistral-large-latest', api_key=\"i5jSJkCFNGKfgIztloxTMjfckiFbYBj4\")\n",
"messages = [HumanMessage(content=\"knock knock\")]\n",
"llm.invoke(messages)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
"# Import things that are needed generically\n",
"from langchain.pydantic_v1 import BaseModel, Field\n",
"from langchain.tools import tool\n",
"\n",
"@tool\n",
"def compute_right_knee_angle(pose: list) -> float:\n",
"\n",
" \"\"\"\n",
" Computes the knee angle.\n",
"\n",
" Args:\n",
" pose (list): list of keypoints\n",
"\n",
" Returns:\n",
" knee_angle (float): knee angle\n",
" \"\"\"\n",
"\n",
" right_hip = pose[joints_id_dict['right_hip']]\n",
" right_knee = pose[joints_id_dict['right_knee']]\n",
" right_ankle = pose[joints_id_dict['right_ankle']]\n",
"\n",
" knee_angle = calculate_angle(right_hip, right_knee, right_ankle)\n",
"\n",
" print(knee_angle)\n",
"\n",
" return str(knee_angle)\n",
"\n",
"@tool\n",
"def get_keypoints_from_path(video_path: str):\n",
" \"\"\"\n",
" Get keypoints from a video.\n",
"\n",
" Args:\n",
" video_path (str): path to the video\n",
" model (YOLO): model to use\n",
"\n",
" Returns:\n",
" keypoints (list): list of keypoints\n",
" \"\"\"\n",
"\n",
" keypoints = []\n",
" results = model(video_path, save=True, show_conf=False, show_boxes=False)\n",
" for frame in results:\n",
" tensor = frame.keypoints.xy[0]\n",
" keypoints.append(tensor.tolist())\n",
"\n",
" return keypoints"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
"errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
"\n",
"Example:\n",
" results = model(source=..., stream=True) # generator of Results objects\n",
" for r in results:\n",
" boxes = r.boxes # Boxes object for bbox outputs\n",
" masks = r.masks # Masks object for segment masks outputs\n",
" probs = r.probs # Class probabilities for classification outputs\n",
"\n",
"video 1/1 (frame 1/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 140.3ms\n",
"video 1/1 (frame 2/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 160.4ms\n",
"video 1/1 (frame 3/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 222.3ms\n",
"video 1/1 (frame 4/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 156.6ms\n",
"video 1/1 (frame 5/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 190.2ms\n",
"video 1/1 (frame 6/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 190.0ms\n",
"video 1/1 (frame 7/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 166.8ms\n",
"video 1/1 (frame 8/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 187.1ms\n",
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"video 1/1 (frame 393/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 141.6ms\n",
"video 1/1 (frame 394/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 238.5ms\n",
"video 1/1 (frame 395/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 158.1ms\n",
"video 1/1 (frame 396/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 121.8ms\n",
"video 1/1 (frame 397/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 396.2ms\n",
"video 1/1 (frame 398/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 463.3ms\n",
"video 1/1 (frame 399/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 278.9ms\n",
"video 1/1 (frame 400/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 211.4ms\n",
"video 1/1 (frame 401/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 163.1ms\n",
"video 1/1 (frame 402/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 163.2ms\n",
"video 1/1 (frame 403/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 125.6ms\n",
"video 1/1 (frame 404/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 163.0ms\n",
"video 1/1 (frame 405/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 162.2ms\n",
"Speed: 6.6ms preprocess, 152.9ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 576)\n",
"Results saved to \u001b[1m/Users/theorousseaux/Documents/Hackathon/FitnessEquation/runs/pose/predict5\u001b[0m\n"
]
}
],
"source": [
"sequence = get_keypoints_from_path('../../data/pose/squat.mp4')"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"compute_right_knee_angle\n",
"compute_right_knee_angle(pose: list) -> float - Computes the knee angle.\n",
"\n",
" Args:\n",
" pose (list): list of keypoints\n",
"\n",
" Returns:\n",
" knee_angle (float): knee angle\n",
"{'pose': {'title': 'Pose', 'type': 'array', 'items': {}}}\n",
"False\n"
]
}
],
"source": [
"print(compute_right_knee_angle.name)\n",
"print(compute_right_knee_angle.description)\n",
"print(compute_right_knee_angle.args)\n",
"print(compute_right_knee_angle.return_direct)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents import AgentExecutor, create_tool_calling_agent\n",
"from langchain_community.tools.tavily_search import TavilySearchResults\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"\n",
"tools = [compute_right_knee_angle]\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\n",
" \"system\",\n",
" \"You are a helpful assistant. Make sure to use the compute_right_knee_angle tool for information.\",\n",
" ),\n",
" (\"placeholder\", \"{chat_history}\"),\n",
" (\"human\", \"{input}\"),\n",
" (\"placeholder\", \"{agent_scratchpad}\"),\n",
" ]\n",
")\n",
"\n",
"# Construct the Tools agent\n",
"agent = create_tool_calling_agent(llm, tools, prompt)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Invoking: `compute_right_knee_angle` with `{'pose': [[705.0933227539062, 159.55224609375], [0.0, 0.0], [688.4795532226562, 135.9303436279297], [0.0, 0.0], [628.56689453125, 150.44094848632812], [603.4036254882812, 295.99420166015625], [619.6266479492188, 305.51202392578125], [0.0, 0.0], [629.2274780273438, 532.8370361328125], [0.0, 0.0], [665.978515625, 716.4405517578125], [634.9310302734375, 684.9545288085938], [642.503173828125, 691.6080932617188], [633.3930053710938, 1001.8157958984375], [649.4693603515625, 1014.472900390625], [601.3526611328125, 1321.026123046875], [580.6210327148438, 1337.936279296875]]}`\n",
"\n",
"\n",
"\u001b[0m166.748027626137\n",
"\u001b[36;1m\u001b[1;3m166.748027626137\u001b[0m\u001b[32;1m\u001b[1;3mThe knee angle at the beginning is approximately 166.75 degrees.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'input': 'Compute my knee angle at the beginning: [[705.0933227539062, 159.55224609375], [0.0, 0.0], [688.4795532226562, 135.9303436279297], [0.0, 0.0], [628.56689453125, 150.44094848632812], [603.4036254882812, 295.99420166015625], [619.6266479492188, 305.51202392578125], [0.0, 0.0], [629.2274780273438, 532.8370361328125], [0.0, 0.0], [665.978515625, 716.4405517578125], [634.9310302734375, 684.9545288085938], [642.503173828125, 691.6080932617188], [633.3930053710938, 1001.8157958984375], [649.4693603515625, 1014.472900390625], [601.3526611328125, 1321.026123046875], [580.6210327148438, 1337.936279296875]]',\n",
" 'output': 'The knee angle at the beginning is approximately 166.75 degrees.'}"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create an agent executor by passing in the agent and tools\n",
"agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)\n",
"agent_executor.invoke({\"input\": f\"Compute my knee angle at the beginning: {sequence[0]}\"})"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"from langchain import hub\n",
"from langchain.agents import AgentExecutor, create_json_chat_agent\n",
"from langchain_community.tools.tavily_search import TavilySearchResults\n",
"\n",
"\n",
"tools = [compute_right_knee_angle]\n",
"\n",
"# Get the prompt to use - you can modify this!\n",
"prompt = hub.pull(\"hwchase17/react-chat-json\")\n",
"\n",
"# Construct the JSON agent\n",
"agent = create_json_chat_agent(llm, tools, prompt)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"# Create an agent executor by passing in the agent and tools\n",
"agent_executor = AgentExecutor(\n",
" agent=agent, tools=tools, verbose=True, handle_parsing_errors=False\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Parameter `stop` not yet supported (https://docs.mistral.ai/api)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n"
]
},
{
"ename": "SSEError",
"evalue": "Expected response header Content-Type to contain 'text/event-stream', got 'application/json'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mSSEError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[51], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43magent_executor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minput\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mHi, is my squat correct ? \u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mkeypoints\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain/chains/base.py:163\u001b[0m, in \u001b[0;36mChain.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 162\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n\u001b[0;32m--> 163\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 164\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_chain_end(outputs)\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m include_run_info:\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain/chains/base.py:153\u001b[0m, in \u001b[0;36mChain.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_inputs(inputs)\n\u001b[1;32m 152\u001b[0m outputs \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 153\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[1;32m 155\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(inputs)\n\u001b[1;32m 156\u001b[0m )\n\u001b[1;32m 158\u001b[0m final_outputs: Dict[\u001b[38;5;28mstr\u001b[39m, Any] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprep_outputs(\n\u001b[1;32m 159\u001b[0m inputs, outputs, return_only_outputs\n\u001b[1;32m 160\u001b[0m )\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain/agents/agent.py:1432\u001b[0m, in \u001b[0;36mAgentExecutor._call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m 1430\u001b[0m \u001b[38;5;66;03m# We now enter the agent loop (until it returns something).\u001b[39;00m\n\u001b[1;32m 1431\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_continue(iterations, time_elapsed):\n\u001b[0;32m-> 1432\u001b[0m next_step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_take_next_step\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1433\u001b[0m \u001b[43m \u001b[49m\u001b[43mname_to_tool_map\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1434\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolor_mapping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1435\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1436\u001b[0m \u001b[43m \u001b[49m\u001b[43mintermediate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1437\u001b[0m \u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1438\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1439\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(next_step_output, AgentFinish):\n\u001b[1;32m 1440\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_return(\n\u001b[1;32m 1441\u001b[0m next_step_output, intermediate_steps, run_manager\u001b[38;5;241m=\u001b[39mrun_manager\n\u001b[1;32m 1442\u001b[0m )\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain/agents/agent.py:1138\u001b[0m, in \u001b[0;36mAgentExecutor._take_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)\u001b[0m\n\u001b[1;32m 1129\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_take_next_step\u001b[39m(\n\u001b[1;32m 1130\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1131\u001b[0m name_to_tool_map: Dict[\u001b[38;5;28mstr\u001b[39m, BaseTool],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1135\u001b[0m run_manager: Optional[CallbackManagerForChainRun] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 1136\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[AgentFinish, List[Tuple[AgentAction, \u001b[38;5;28mstr\u001b[39m]]]:\n\u001b[1;32m 1137\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_consume_next_step(\n\u001b[0;32m-> 1138\u001b[0m [\n\u001b[1;32m 1139\u001b[0m a\n\u001b[1;32m 1140\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iter_next_step(\n\u001b[1;32m 1141\u001b[0m name_to_tool_map,\n\u001b[1;32m 1142\u001b[0m color_mapping,\n\u001b[1;32m 1143\u001b[0m inputs,\n\u001b[1;32m 1144\u001b[0m intermediate_steps,\n\u001b[1;32m 1145\u001b[0m run_manager,\n\u001b[1;32m 1146\u001b[0m )\n\u001b[1;32m 1147\u001b[0m ]\n\u001b[1;32m 1148\u001b[0m )\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain/agents/agent.py:1138\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 1129\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_take_next_step\u001b[39m(\n\u001b[1;32m 1130\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1131\u001b[0m name_to_tool_map: Dict[\u001b[38;5;28mstr\u001b[39m, BaseTool],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1135\u001b[0m run_manager: Optional[CallbackManagerForChainRun] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 1136\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[AgentFinish, List[Tuple[AgentAction, \u001b[38;5;28mstr\u001b[39m]]]:\n\u001b[1;32m 1137\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_consume_next_step(\n\u001b[0;32m-> 1138\u001b[0m [\n\u001b[1;32m 1139\u001b[0m a\n\u001b[1;32m 1140\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iter_next_step(\n\u001b[1;32m 1141\u001b[0m name_to_tool_map,\n\u001b[1;32m 1142\u001b[0m color_mapping,\n\u001b[1;32m 1143\u001b[0m inputs,\n\u001b[1;32m 1144\u001b[0m intermediate_steps,\n\u001b[1;32m 1145\u001b[0m run_manager,\n\u001b[1;32m 1146\u001b[0m )\n\u001b[1;32m 1147\u001b[0m ]\n\u001b[1;32m 1148\u001b[0m )\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain/agents/agent.py:1166\u001b[0m, in \u001b[0;36mAgentExecutor._iter_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)\u001b[0m\n\u001b[1;32m 1163\u001b[0m intermediate_steps \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_intermediate_steps(intermediate_steps)\n\u001b[1;32m 1165\u001b[0m \u001b[38;5;66;03m# Call the LLM to see what to do.\u001b[39;00m\n\u001b[0;32m-> 1166\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplan\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1167\u001b[0m \u001b[43m \u001b[49m\u001b[43mintermediate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1168\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 1169\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1170\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1171\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m OutputParserException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 1172\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_parsing_errors, \u001b[38;5;28mbool\u001b[39m):\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain/agents/agent.py:397\u001b[0m, in \u001b[0;36mRunnableAgent.plan\u001b[0;34m(self, intermediate_steps, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m 389\u001b[0m final_output: Any \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstream_runnable:\n\u001b[1;32m 391\u001b[0m \u001b[38;5;66;03m# Use streaming to make sure that the underlying LLM is invoked in a\u001b[39;00m\n\u001b[1;32m 392\u001b[0m \u001b[38;5;66;03m# streaming\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 395\u001b[0m \u001b[38;5;66;03m# Because the response from the plan is not a generator, we need to\u001b[39;00m\n\u001b[1;32m 396\u001b[0m \u001b[38;5;66;03m# accumulate the output into final output and return that.\u001b[39;00m\n\u001b[0;32m--> 397\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrunnable\u001b[38;5;241m.\u001b[39mstream(inputs, config\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcallbacks\u001b[39m\u001b[38;5;124m\"\u001b[39m: callbacks}):\n\u001b[1;32m 398\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m final_output \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 399\u001b[0m final_output \u001b[38;5;241m=\u001b[39m chunk\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:2875\u001b[0m, in \u001b[0;36mRunnableSequence.stream\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 2869\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstream\u001b[39m(\n\u001b[1;32m 2870\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 2871\u001b[0m \u001b[38;5;28minput\u001b[39m: Input,\n\u001b[1;32m 2872\u001b[0m config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 2873\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Optional[Any],\n\u001b[1;32m 2874\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Output]:\n\u001b[0;32m-> 2875\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform(\u001b[38;5;28miter\u001b[39m([\u001b[38;5;28minput\u001b[39m]), config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:2862\u001b[0m, in \u001b[0;36mRunnableSequence.transform\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 2856\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtransform\u001b[39m(\n\u001b[1;32m 2857\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 2858\u001b[0m \u001b[38;5;28minput\u001b[39m: Iterator[Input],\n\u001b[1;32m 2859\u001b[0m config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 2860\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Optional[Any],\n\u001b[1;32m 2861\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Output]:\n\u001b[0;32m-> 2862\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transform_stream_with_config(\n\u001b[1;32m 2863\u001b[0m \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m 2864\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transform,\n\u001b[1;32m 2865\u001b[0m patch_config(config, run_name\u001b[38;5;241m=\u001b[39m(config \u001b[38;5;129;01mor\u001b[39;00m {})\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_name\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname),\n\u001b[1;32m 2866\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 2867\u001b[0m )\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:1880\u001b[0m, in \u001b[0;36mRunnable._transform_stream_with_config\u001b[0;34m(self, input, transformer, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m 1878\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1879\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m-> 1880\u001b[0m chunk: Output \u001b[38;5;241m=\u001b[39m \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m 1881\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m chunk\n\u001b[1;32m 1882\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m final_output_supported:\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:2826\u001b[0m, in \u001b[0;36mRunnableSequence._transform\u001b[0;34m(self, input, run_manager, config)\u001b[0m\n\u001b[1;32m 2817\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step \u001b[38;5;129;01min\u001b[39;00m steps:\n\u001b[1;32m 2818\u001b[0m final_pipeline \u001b[38;5;241m=\u001b[39m step\u001b[38;5;241m.\u001b[39mtransform(\n\u001b[1;32m 2819\u001b[0m final_pipeline,\n\u001b[1;32m 2820\u001b[0m patch_config(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2823\u001b[0m ),\n\u001b[1;32m 2824\u001b[0m )\n\u001b[0;32m-> 2826\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m output \u001b[38;5;129;01min\u001b[39;00m final_pipeline:\n\u001b[1;32m 2827\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m output\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:1283\u001b[0m, in \u001b[0;36mRunnable.transform\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 1280\u001b[0m final: Input\n\u001b[1;32m 1281\u001b[0m got_first_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m-> 1283\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28minput\u001b[39m:\n\u001b[1;32m 1284\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m got_first_val:\n\u001b[1;32m 1285\u001b[0m final \u001b[38;5;241m=\u001b[39m adapt_first_streaming_chunk(chunk) \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:4728\u001b[0m, in \u001b[0;36mRunnableBindingBase.transform\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 4722\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtransform\u001b[39m(\n\u001b[1;32m 4723\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 4724\u001b[0m \u001b[38;5;28minput\u001b[39m: Iterator[Input],\n\u001b[1;32m 4725\u001b[0m config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 4726\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 4727\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Output]:\n\u001b[0;32m-> 4728\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbound\u001b[38;5;241m.\u001b[39mtransform(\n\u001b[1;32m 4729\u001b[0m \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m 4730\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_merge_configs(config),\n\u001b[1;32m 4731\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs},\n\u001b[1;32m 4732\u001b[0m )\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:1300\u001b[0m, in \u001b[0;36mRunnable.transform\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 1293\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 1294\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed while trying to add together \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1295\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(final)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(chunk)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1296\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThese types should be addable for transform to work.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1297\u001b[0m )\n\u001b[1;32m 1299\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m got_first_val:\n\u001b[0;32m-> 1300\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstream(final, config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_core/language_models/chat_models.py:249\u001b[0m, in \u001b[0;36mBaseChatModel.stream\u001b[0;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[1;32m 242\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 243\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_llm_error(\n\u001b[1;32m 244\u001b[0m e,\n\u001b[1;32m 245\u001b[0m response\u001b[38;5;241m=\u001b[39mLLMResult(\n\u001b[1;32m 246\u001b[0m generations\u001b[38;5;241m=\u001b[39m[[generation]] \u001b[38;5;28;01mif\u001b[39;00m generation \u001b[38;5;28;01melse\u001b[39;00m []\n\u001b[1;32m 247\u001b[0m ),\n\u001b[1;32m 248\u001b[0m )\n\u001b[0;32m--> 249\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 250\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 251\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_llm_end(LLMResult(generations\u001b[38;5;241m=\u001b[39m[[generation]]))\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_core/language_models/chat_models.py:229\u001b[0m, in \u001b[0;36mBaseChatModel.stream\u001b[0;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[1;32m 227\u001b[0m generation: Optional[ChatGenerationChunk] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 228\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 229\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stream(messages, stop\u001b[38;5;241m=\u001b[39mstop, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 230\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunk\u001b[38;5;241m.\u001b[39mmessage\u001b[38;5;241m.\u001b[39mid \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 231\u001b[0m chunk\u001b[38;5;241m.\u001b[39mmessage\u001b[38;5;241m.\u001b[39mid \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun-\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrun_manager\u001b[38;5;241m.\u001b[39mrun_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_mistralai/chat_models.py:428\u001b[0m, in \u001b[0;36mChatMistralAI._stream\u001b[0;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m 425\u001b[0m params \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mTrue\u001b[39;00m}\n\u001b[1;32m 427\u001b[0m default_chunk_class: Type[BaseMessageChunk] \u001b[38;5;241m=\u001b[39m AIMessageChunk\n\u001b[0;32m--> 428\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcompletion_with_retry(\n\u001b[1;32m 429\u001b[0m messages\u001b[38;5;241m=\u001b[39mmessage_dicts, run_manager\u001b[38;5;241m=\u001b[39mrun_manager, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams\n\u001b[1;32m 430\u001b[0m ):\n\u001b[1;32m 431\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(chunk[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchoices\u001b[39m\u001b[38;5;124m\"\u001b[39m]) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_mistralai/chat_models.py:301\u001b[0m, in \u001b[0;36mChatMistralAI.completion_with_retry.<locals>._completion_with_retry.<locals>.iter_sse\u001b[0;34m()\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21miter_sse\u001b[39m() \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Dict]:\n\u001b[1;32m 298\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m connect_sse(\n\u001b[1;32m 299\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclient, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPOST\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/chat/completions\u001b[39m\u001b[38;5;124m\"\u001b[39m, json\u001b[38;5;241m=\u001b[39mkwargs\n\u001b[1;32m 300\u001b[0m ) \u001b[38;5;28;01mas\u001b[39;00m event_source:\n\u001b[0;32m--> 301\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m event \u001b[38;5;129;01min\u001b[39;00m event_source\u001b[38;5;241m.\u001b[39miter_sse():\n\u001b[1;32m 302\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m event\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[DONE]\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 303\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/httpx_sse/_api.py:28\u001b[0m, in \u001b[0;36mEventSource.iter_sse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21miter_sse\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[ServerSentEvent]:\n\u001b[0;32m---> 28\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_content_type\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 29\u001b[0m decoder \u001b[38;5;241m=\u001b[39m SSEDecoder()\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_response\u001b[38;5;241m.\u001b[39miter_lines():\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/httpx_sse/_api.py:18\u001b[0m, in \u001b[0;36mEventSource._check_content_type\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 16\u001b[0m content_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_response\u001b[38;5;241m.\u001b[39mheaders\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent-type\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mpartition(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m;\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext/event-stream\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m content_type:\n\u001b[0;32m---> 18\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m SSEError(\n\u001b[1;32m 19\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected response header Content-Type to contain \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtext/event-stream\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgot \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcontent_type\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 21\u001b[0m )\n",
"\u001b[0;31mSSEError\u001b[0m: Expected response header Content-Type to contain 'text/event-stream', got 'application/json'"
]
}
],
"source": [
"agent_executor.invoke({\"input\": f\"Hi, is my squat correct ? {keypoints}\"})"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Parameter `stop` not yet supported (https://docs.mistral.ai/api)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n"
]
},
{
"ename": "SSEError",
"evalue": "Expected response header Content-Type to contain 'text/event-stream', got 'application/json'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mSSEError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[44], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m input_\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIs my squat correct? \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkeypoints\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 2\u001b[0m \u001b[43magent_executor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minput\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain/chains/base.py:163\u001b[0m, in \u001b[0;36mChain.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 162\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n\u001b[0;32m--> 163\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 164\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_chain_end(outputs)\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m include_run_info:\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain/chains/base.py:153\u001b[0m, in \u001b[0;36mChain.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_inputs(inputs)\n\u001b[1;32m 152\u001b[0m outputs \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 153\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[1;32m 155\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(inputs)\n\u001b[1;32m 156\u001b[0m )\n\u001b[1;32m 158\u001b[0m final_outputs: Dict[\u001b[38;5;28mstr\u001b[39m, Any] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprep_outputs(\n\u001b[1;32m 159\u001b[0m inputs, outputs, return_only_outputs\n\u001b[1;32m 160\u001b[0m )\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain/agents/agent.py:1432\u001b[0m, in \u001b[0;36mAgentExecutor._call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m 1430\u001b[0m \u001b[38;5;66;03m# We now enter the agent loop (until it returns something).\u001b[39;00m\n\u001b[1;32m 1431\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_continue(iterations, time_elapsed):\n\u001b[0;32m-> 1432\u001b[0m next_step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_take_next_step\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1433\u001b[0m \u001b[43m \u001b[49m\u001b[43mname_to_tool_map\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1434\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolor_mapping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1435\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1436\u001b[0m \u001b[43m \u001b[49m\u001b[43mintermediate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1437\u001b[0m \u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1438\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1439\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(next_step_output, AgentFinish):\n\u001b[1;32m 1440\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_return(\n\u001b[1;32m 1441\u001b[0m next_step_output, intermediate_steps, run_manager\u001b[38;5;241m=\u001b[39mrun_manager\n\u001b[1;32m 1442\u001b[0m )\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain/agents/agent.py:1138\u001b[0m, in \u001b[0;36mAgentExecutor._take_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)\u001b[0m\n\u001b[1;32m 1129\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_take_next_step\u001b[39m(\n\u001b[1;32m 1130\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1131\u001b[0m name_to_tool_map: Dict[\u001b[38;5;28mstr\u001b[39m, BaseTool],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1135\u001b[0m run_manager: Optional[CallbackManagerForChainRun] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 1136\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[AgentFinish, List[Tuple[AgentAction, \u001b[38;5;28mstr\u001b[39m]]]:\n\u001b[1;32m 1137\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_consume_next_step(\n\u001b[0;32m-> 1138\u001b[0m [\n\u001b[1;32m 1139\u001b[0m a\n\u001b[1;32m 1140\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iter_next_step(\n\u001b[1;32m 1141\u001b[0m name_to_tool_map,\n\u001b[1;32m 1142\u001b[0m color_mapping,\n\u001b[1;32m 1143\u001b[0m inputs,\n\u001b[1;32m 1144\u001b[0m intermediate_steps,\n\u001b[1;32m 1145\u001b[0m run_manager,\n\u001b[1;32m 1146\u001b[0m )\n\u001b[1;32m 1147\u001b[0m ]\n\u001b[1;32m 1148\u001b[0m )\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain/agents/agent.py:1138\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 1129\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_take_next_step\u001b[39m(\n\u001b[1;32m 1130\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1131\u001b[0m name_to_tool_map: Dict[\u001b[38;5;28mstr\u001b[39m, BaseTool],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1135\u001b[0m run_manager: Optional[CallbackManagerForChainRun] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 1136\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[AgentFinish, List[Tuple[AgentAction, \u001b[38;5;28mstr\u001b[39m]]]:\n\u001b[1;32m 1137\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_consume_next_step(\n\u001b[0;32m-> 1138\u001b[0m [\n\u001b[1;32m 1139\u001b[0m a\n\u001b[1;32m 1140\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iter_next_step(\n\u001b[1;32m 1141\u001b[0m name_to_tool_map,\n\u001b[1;32m 1142\u001b[0m color_mapping,\n\u001b[1;32m 1143\u001b[0m inputs,\n\u001b[1;32m 1144\u001b[0m intermediate_steps,\n\u001b[1;32m 1145\u001b[0m run_manager,\n\u001b[1;32m 1146\u001b[0m )\n\u001b[1;32m 1147\u001b[0m ]\n\u001b[1;32m 1148\u001b[0m )\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain/agents/agent.py:1166\u001b[0m, in \u001b[0;36mAgentExecutor._iter_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)\u001b[0m\n\u001b[1;32m 1163\u001b[0m intermediate_steps \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_intermediate_steps(intermediate_steps)\n\u001b[1;32m 1165\u001b[0m \u001b[38;5;66;03m# Call the LLM to see what to do.\u001b[39;00m\n\u001b[0;32m-> 1166\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplan\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1167\u001b[0m \u001b[43m \u001b[49m\u001b[43mintermediate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1168\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 1169\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1170\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1171\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m OutputParserException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 1172\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_parsing_errors, \u001b[38;5;28mbool\u001b[39m):\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain/agents/agent.py:397\u001b[0m, in \u001b[0;36mRunnableAgent.plan\u001b[0;34m(self, intermediate_steps, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m 389\u001b[0m final_output: Any \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstream_runnable:\n\u001b[1;32m 391\u001b[0m \u001b[38;5;66;03m# Use streaming to make sure that the underlying LLM is invoked in a\u001b[39;00m\n\u001b[1;32m 392\u001b[0m \u001b[38;5;66;03m# streaming\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 395\u001b[0m \u001b[38;5;66;03m# Because the response from the plan is not a generator, we need to\u001b[39;00m\n\u001b[1;32m 396\u001b[0m \u001b[38;5;66;03m# accumulate the output into final output and return that.\u001b[39;00m\n\u001b[0;32m--> 397\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrunnable\u001b[38;5;241m.\u001b[39mstream(inputs, config\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcallbacks\u001b[39m\u001b[38;5;124m\"\u001b[39m: callbacks}):\n\u001b[1;32m 398\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m final_output \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 399\u001b[0m final_output \u001b[38;5;241m=\u001b[39m chunk\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:2875\u001b[0m, in \u001b[0;36mRunnableSequence.stream\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 2869\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstream\u001b[39m(\n\u001b[1;32m 2870\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 2871\u001b[0m \u001b[38;5;28minput\u001b[39m: Input,\n\u001b[1;32m 2872\u001b[0m config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 2873\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Optional[Any],\n\u001b[1;32m 2874\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Output]:\n\u001b[0;32m-> 2875\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform(\u001b[38;5;28miter\u001b[39m([\u001b[38;5;28minput\u001b[39m]), config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:2862\u001b[0m, in \u001b[0;36mRunnableSequence.transform\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 2856\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtransform\u001b[39m(\n\u001b[1;32m 2857\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 2858\u001b[0m \u001b[38;5;28minput\u001b[39m: Iterator[Input],\n\u001b[1;32m 2859\u001b[0m config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 2860\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Optional[Any],\n\u001b[1;32m 2861\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Output]:\n\u001b[0;32m-> 2862\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transform_stream_with_config(\n\u001b[1;32m 2863\u001b[0m \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m 2864\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transform,\n\u001b[1;32m 2865\u001b[0m patch_config(config, run_name\u001b[38;5;241m=\u001b[39m(config \u001b[38;5;129;01mor\u001b[39;00m {})\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_name\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname),\n\u001b[1;32m 2866\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 2867\u001b[0m )\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:1880\u001b[0m, in \u001b[0;36mRunnable._transform_stream_with_config\u001b[0;34m(self, input, transformer, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m 1878\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1879\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m-> 1880\u001b[0m chunk: Output \u001b[38;5;241m=\u001b[39m \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m 1881\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m chunk\n\u001b[1;32m 1882\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m final_output_supported:\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:2826\u001b[0m, in \u001b[0;36mRunnableSequence._transform\u001b[0;34m(self, input, run_manager, config)\u001b[0m\n\u001b[1;32m 2817\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step \u001b[38;5;129;01min\u001b[39;00m steps:\n\u001b[1;32m 2818\u001b[0m final_pipeline \u001b[38;5;241m=\u001b[39m step\u001b[38;5;241m.\u001b[39mtransform(\n\u001b[1;32m 2819\u001b[0m final_pipeline,\n\u001b[1;32m 2820\u001b[0m patch_config(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2823\u001b[0m ),\n\u001b[1;32m 2824\u001b[0m )\n\u001b[0;32m-> 2826\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m output \u001b[38;5;129;01min\u001b[39;00m final_pipeline:\n\u001b[1;32m 2827\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m output\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:1283\u001b[0m, in \u001b[0;36mRunnable.transform\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 1280\u001b[0m final: Input\n\u001b[1;32m 1281\u001b[0m got_first_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m-> 1283\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28minput\u001b[39m:\n\u001b[1;32m 1284\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m got_first_val:\n\u001b[1;32m 1285\u001b[0m final \u001b[38;5;241m=\u001b[39m adapt_first_streaming_chunk(chunk) \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:4728\u001b[0m, in \u001b[0;36mRunnableBindingBase.transform\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 4722\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtransform\u001b[39m(\n\u001b[1;32m 4723\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 4724\u001b[0m \u001b[38;5;28minput\u001b[39m: Iterator[Input],\n\u001b[1;32m 4725\u001b[0m config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 4726\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 4727\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Output]:\n\u001b[0;32m-> 4728\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbound\u001b[38;5;241m.\u001b[39mtransform(\n\u001b[1;32m 4729\u001b[0m \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m 4730\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_merge_configs(config),\n\u001b[1;32m 4731\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs},\n\u001b[1;32m 4732\u001b[0m )\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:1300\u001b[0m, in \u001b[0;36mRunnable.transform\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 1293\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 1294\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed while trying to add together \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1295\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(final)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(chunk)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1296\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThese types should be addable for transform to work.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1297\u001b[0m )\n\u001b[1;32m 1299\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m got_first_val:\n\u001b[0;32m-> 1300\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstream(final, config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_core/language_models/chat_models.py:249\u001b[0m, in \u001b[0;36mBaseChatModel.stream\u001b[0;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[1;32m 242\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 243\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_llm_error(\n\u001b[1;32m 244\u001b[0m e,\n\u001b[1;32m 245\u001b[0m response\u001b[38;5;241m=\u001b[39mLLMResult(\n\u001b[1;32m 246\u001b[0m generations\u001b[38;5;241m=\u001b[39m[[generation]] \u001b[38;5;28;01mif\u001b[39;00m generation \u001b[38;5;28;01melse\u001b[39;00m []\n\u001b[1;32m 247\u001b[0m ),\n\u001b[1;32m 248\u001b[0m )\n\u001b[0;32m--> 249\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 250\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 251\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_llm_end(LLMResult(generations\u001b[38;5;241m=\u001b[39m[[generation]]))\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_core/language_models/chat_models.py:229\u001b[0m, in \u001b[0;36mBaseChatModel.stream\u001b[0;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[1;32m 227\u001b[0m generation: Optional[ChatGenerationChunk] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 228\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 229\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stream(messages, stop\u001b[38;5;241m=\u001b[39mstop, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 230\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunk\u001b[38;5;241m.\u001b[39mmessage\u001b[38;5;241m.\u001b[39mid \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 231\u001b[0m chunk\u001b[38;5;241m.\u001b[39mmessage\u001b[38;5;241m.\u001b[39mid \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun-\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrun_manager\u001b[38;5;241m.\u001b[39mrun_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_mistralai/chat_models.py:428\u001b[0m, in \u001b[0;36mChatMistralAI._stream\u001b[0;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m 425\u001b[0m params \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mTrue\u001b[39;00m}\n\u001b[1;32m 427\u001b[0m default_chunk_class: Type[BaseMessageChunk] \u001b[38;5;241m=\u001b[39m AIMessageChunk\n\u001b[0;32m--> 428\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcompletion_with_retry(\n\u001b[1;32m 429\u001b[0m messages\u001b[38;5;241m=\u001b[39mmessage_dicts, run_manager\u001b[38;5;241m=\u001b[39mrun_manager, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams\n\u001b[1;32m 430\u001b[0m ):\n\u001b[1;32m 431\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(chunk[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchoices\u001b[39m\u001b[38;5;124m\"\u001b[39m]) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/langchain_mistralai/chat_models.py:301\u001b[0m, in \u001b[0;36mChatMistralAI.completion_with_retry.<locals>._completion_with_retry.<locals>.iter_sse\u001b[0;34m()\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21miter_sse\u001b[39m() \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Dict]:\n\u001b[1;32m 298\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m connect_sse(\n\u001b[1;32m 299\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclient, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPOST\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/chat/completions\u001b[39m\u001b[38;5;124m\"\u001b[39m, json\u001b[38;5;241m=\u001b[39mkwargs\n\u001b[1;32m 300\u001b[0m ) \u001b[38;5;28;01mas\u001b[39;00m event_source:\n\u001b[0;32m--> 301\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m event \u001b[38;5;129;01min\u001b[39;00m event_source\u001b[38;5;241m.\u001b[39miter_sse():\n\u001b[1;32m 302\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m event\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[DONE]\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 303\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/httpx_sse/_api.py:28\u001b[0m, in \u001b[0;36mEventSource.iter_sse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21miter_sse\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[ServerSentEvent]:\n\u001b[0;32m---> 28\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_content_type\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 29\u001b[0m decoder \u001b[38;5;241m=\u001b[39m SSEDecoder()\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_response\u001b[38;5;241m.\u001b[39miter_lines():\n",
"File \u001b[0;32m~/Documents/Hackathon/FitnessEquation/venv/lib/python3.9/site-packages/httpx_sse/_api.py:18\u001b[0m, in \u001b[0;36mEventSource._check_content_type\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 16\u001b[0m content_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_response\u001b[38;5;241m.\u001b[39mheaders\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent-type\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mpartition(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m;\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext/event-stream\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m content_type:\n\u001b[0;32m---> 18\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m SSEError(\n\u001b[1;32m 19\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected response header Content-Type to contain \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtext/event-stream\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgot \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcontent_type\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 21\u001b[0m )\n",
"\u001b[0;31mSSEError\u001b[0m: Expected response header Content-Type to contain 'text/event-stream', got 'application/json'"
]
}
],
"source": [
"input_=f\"Is my squat correct? {keypoints}\"\n",
"agent_executor.invoke({\"input\": input_})"
]
}
],
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|