Upload 17 files

chatbot_multiagent.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -223,20 +223,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/4gHYSUNDX1BST0ZJTEUAAQEAAAHIAAAAAAQwAABtbnRyUkdCIFhZWiAH4AABAAEAAAAAAABhY3NwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQAA9tYAAQAAAADTLQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlkZXNjAAAA8AAAACRyWFlaAAABFAAAABRnWFlaAAABKAAAABRiWFlaAAABPAAAABR3dHB0AAABUAAAABRyVFJDAAABZAAAAChnVFJDAAABZAAAAChiVFJDAAABZAAAAChjcHJ0AAABjAAAADxtbHVjAAAAAAAAAAEAAAAMZW5VUwAAAAgAAAAcAHMAUgBHAEJYWVogAAAAAAAAb6IAADj1AAADkFhZWiAAAAAAAABimQAAt4UAABjaWFlaIAAAAAAAACSgAAAPhAAAts9YWVogAAAAAAAA9tYAAQAAAADTLXBhcmEAAAAAAAQAAAACZmYAAPKnAAANWQAAE9AAAApbAAAAAAAAAABtbHVjAAAAAAAAAAEAAAAMZW5VUwAAACAAAAAcAEcAbwBvAGcAbABlACAASQBuAGMALgAgADIAMAAxADb/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsKCwsNDhIQDQ4RDgsLEBYQERMUFRUVDA8XGBYUGBIUFRT/2wBDAQMEBAUEBQkFBQkUDQsNFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBT/wAARCAHqAZkDASIAAhEBAxEB/8QAHQABAAICAwEBAAAAAAAAAAAAAAYHBAUCAwgBCf/EAFsQAAEDBAAEAgUFCQsGCwgDAAEAAgMEBQYRBxITIRQxFRYiQVEIMkJhgRcjU1VWkpSV0RgzUlRxkZOi0tPUCTdydbO0JCU0NUNidHaDobEoNkZkgrLBw0RHV//EABoBAQEAAwEBAAAAAAAAAAAAAAABAgMEBQf/xAAzEQEAAQIBCgQEBgMAAAAAAAAAAQIRAwQSEyExUVJhkdEUQXGhgbHB4RUjM0JT8AUiMv/aAAwDAQACEQMRAD8A/VNERAREQEREBERAREQEREBERAREQEREBFj3Cvp7XRTVdVIIqeFvM95BOh9QHcn4AdyewUfjs9ZlbRU3l9RQ0L9mOzwy8nsnyM7293P+LGu5BvR59cy200XjOqm0f3YsQ3VXfLbQSclTcKWnf/Blma0/zEro9arJ+OKD9KZ+1dVLhOPUTOSCxW2FutexSRjf8vbuu71Wsv4ooP0Zn7Fn+Tz9jU+etVk/HFB+lM/anrVZPxxQfpTP2r76rWX8UUH6Mz9ieq1l/FFB+jM/Yn5PP2XU+etVk/HFB+lM/anrVZPxxQfpTP2r76rWX8UUH6Mz9ieq1l/FFB+jM/Yn5PP2NT561WT8cUH6Uz9q+tyizPcA270LifICpZ+1PVay/iig/RmfsXx2KWR7S11noHNPYg0rNH/yT8nn7Gps45GysD2OD2HuHNOwVyUafgVupHmay82PVewepbgGRu17nxa5HA+/Y38CD3WbY71NVzT2+4wtprrTAF7Wb6czD5Sxk9+U+RB7tIIOxpzsaqImM6ib/NLbm4REWlBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERBGL/q65ZYrS/TqeNslzmYd+0YnMbEPsfIH/AMsYUnUYr2+E4h2iodvp1dBUUgOu3Ua6ORo39bRIf/pUmJABJOgPeV0Yv/NERu+srPk+oq//AHQvCv8A/wBLw/8AX1L/AHi+u+UHwtY4tdxKxBrgdEG/UoIP9IudGJYeOlvyjMrtYLRjeR3GG2VlRbqi9Q0cfgBVwMLpIecyBwcCOUOLQ0uIHN3Ub4Gce71xE4dXfIb5ht6pJaCavcHUlNE9lW2KqmjbDBGyd8jpWtja1wIALweUkELRUOJZPX8erbk+L4qcUslRWyz3m+0t8hnt2Q0JhcIX+FY7fXcTG4SFoIAO3vBC1trwPihZuDmdcOLZZpLXWtnuNVaMmp7pCxlcyeudUdJgDurBI6OWRnM5oDSNg+RQWDR/KUsbrRmFVdMfyPHLhi9qde6yz3ejjiq5aMNeRLCGyuY8ExPb88acNHSjHEH5Tl0t2E2HIcbwbIX0l0vdro4J7hS07BV01TK0F0LDUNcHOb7LDIGjme0kcuyK6k4D5K+XiHNj/C6LDbdf8ArLBS0DLpSy1ElftxY6dwkLdydTlD+d/wC97eW7CuTi1w/yK/cGcaorJQR1mQWCttF0ZbJJ2RCodSTRSPhEh9lpIY4Ak63rvrugtWxXOW82ekrprdV2iWeMPdQ13T68BP0X9Nz27H/VcR9az1XsPHDFbRSU8WZ3ux4HkD2dSexXm+UYqadpceQu5ZCDzNAcCCR3XL90LwsH/wDZeH/r6l/vEFgKMZrq2utN7Zps1FVxwPd326Cd7IpG9vdsxv8A5YwthjOX2LNbc64Y7e7dfqBshiNVbKuOpiDwAS0uYSNgEHXn3CwOILfE2OnoG7MtdX0sDABv/pmvefsYx5+xdGT/AKtMeXn6efssbUmREXOgiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIg1mQWVt8oBEJfD1MMjaimqA3mMMrTtrtbGx7iNjbS4b7rpsuRsr5fA1sbaC8xt3LRPfvm15viPbqRn3OA9+nBrgWjcrAu9ioL9TthuFLHUsaeZhcNOjPltrh3afrBBW6mqJjMr2fJfV3ejaT+Kwf0Y/Yno2k/isP8ARj9i0HqKI+1NkF9po9aDBXdXQ/llD3fzlfPUif8AKm/f08X90sszD4/aVtG9KA0NAAAAHYAe5fVFvUif8qb9/Txf3SepE/5U37+ni/uk0eHx+0lo3pSi8+/JnvOQcWsBuN5vmT3VtXT3uut7BSPjY3pQyljNgsPfXmVbPqRP+VN+/p4v7pNHh8ftJaN6Ry0VPO/mkgikd/CcwErh6Mo/4pB/Rj9ij/qRP+VN+/p4v7pfRhE3cOye/OB93iIx/wCYj2mjw+P2ktG9u6ysoLDRPnqZYKClae73kMbs9gP5T5a961NrpZ77d473WwPpYIGOZbqWZpbI1rgOeaRp+a92tNb5tbvenPcxvbbcKtduq2VjmT19czuyquFQ+oew61tnOSGdv4IHmfiVvlJqpoiYo1zPn2TZsERFoQREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQed/kMf5ob1/3pu3+8FeiF53+Qx/mhvX/em7f7wV6IQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREHnf5DH+aG9f96bt/vBXohed/kMf5ob1/3pu3+8FeiEBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEUNmy+7XR75LFQUctC1zmMq66oezrEHRLGNYfY3sBxI3rYBaQ49XpzMP4jY/0qb+7XXGS4nnaPjC2TdeOv8prwcq8/wCEFuyy3NkmrMRmlmmgb33STBgmfr3lhjid9TQ8+5ejvTmYfxGx/pU392sa51OTXq21dvrrVYKqiq4XwTwS1ExbJG5pa5pHT7ggkfar4WvfHWCz8pfkMcDPu28c7cK6n62O2Hluly527ZIGuHShPuPO/WwfNrX/AAX7NLzf8nPgXX/Jsxm62mxQWmufca59XNWVU8glLfKKIkR92sb5fEuce3NoWz6czD+I2P8ASpv7tPC1746wWTdFCPTmYfxGx/pU392uTL9lzXbfbrLI0ebG1krSftMR1/Mnha98dYLJqi11hvkN/oBUxMfC9rzFNTyjT4ZG/OY7Xbt8RsEEEEggrYrlqpmmZpq2oIiLEEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERBXPDY83DvF3HW3WulJ0NdzE3akajfDX/Nziv+qqT/AGLV5/yXjRlVo4jUlwsl+uuQ4k7Kqew1kT7JSw2uESziB8cdTziokljc754DmFzSDpexlE2xavWfms7ZepEXl/K+IfEOnsXGLK6DLW0tLg95liorObbTviqYY4KeZ8c0hbz6IkcAWFrgdkud2A5ceuMGU2C4ZDX4VkF2qX45bI7hXWeksdLPQUu4zLy1dTK9r/bZ35YTzNHfR2Fz50I9JQ3m31F0qbZFXU0typo2Sz0bJmmaJj+YMc5m9tDuV2iRo8p15FZi88UUl6yTixxWmxqs9EX+sw2zzW+oLGSCGd3jHR7D2lpHNoHY8trhb+NOW8UzZ3YIYYq6nw+pvVxoJ42GN9yk5oKWke5w2zlniqSdObvpgE6JTOHoiSRkMbpJHNYxo25zjoAfElcl434gZNd8y+S/n9Pc84ulXkVuFE652q5WanoKyge6RvNC9gZp0Tz7THt3sM7Pd3XrPGLTXWSywUdyvdVkNZGXF9xrIYYpZduJG2wsYwaBA7NHYd9nZVibjI4fndyzIe5t4YAAP/kaQ/8AqSpgodw//wCdMz/1wz/caRTFaMp/U+EfKGU7RERcrEREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBEWLNdaKnr4KGWsgjragF0NM+VokkAGyWt3s6AO9IMpFWFDx9smWWHM6zCqKvy+4YvOaSottPTvp5JqgEgxRmVrQSC1wJ8vZPn2XO5y8UMqtuEXCzR2nCnvmZUZHaruPGzsi2wmCGSM8myA8F3/WBGiCEFmKD5vxqwzh9htZlV1vcUlkpKgUctRb2urOWckARERBxDtkDR1rY3ra+2/hi+l4m3rL6jKb9cKa40YomY3U1Ida6Zuo+ZzIS357jHsu39Nw1o9tngvDnGOGVgbZMWslHY7W2QzeGpY9NdIQAXuPm52mtGySew+CCOcNf83OK/6qpP9i1QC4/Jfx64OqIm5BktJbHXT01TWqmrmNpaKt63XM0TTGSdyczuSQvYC4kNB0RtsC4p4/LVXrELbLVXmrxGZtsq5LZQzTsZyjTGnkZ2cAC1wGwHNd3Kl3rnT/iq/fqSr/u17eLhzi11V0xeJmWUxMzqRuv4H2K44xn9ikq7i2kzWqmq7g9kkfUifJDHC4Qnk00csTSOYO7k+fkNVlHyb8fyqvvs0t4yC30N+p46e72y31rYqav6cQiY+T2C8O5A1p5HNa4NAcCN7nPrnT/iq/fqSr/u09c6f8VX79SVf92tegr4TNncjVJwnosSvlNlVrqLxX3u32JlofS+KjY27Mha7o9cFoZ1Q5ztPHIAXnfbsI7wY4MzY3jmdVN1pnY5ec2uVVcKqG11nNLb45dhkbJ2gDnbzPfzNGg6Q63oFTS2cWbBerjcqC3i5V1dbJGxV1NT2uofJSvcOZrZGhm2EjuAdbC2frnT/iq/fqSr/u00FfDJmzuQmj+TljnoXLaC8XK9ZPPlFLFRXC43ira+pMMQd0mMLGMa3kL3OBDdknZJU4wvFpcPsjLdLfbtkTmvLvG3qVktQRoANLmMYNDXw33OyVx9c6f8VX79SVf92uTMuimPLHab49/ua60VDN9/i5gH85TQVx+1LSz+H/8Azpmf+uGf7jSKYrzzJ8q3hRwnyW+4/l+XwWjKX1Yqa63iiqZm0rnQRBkZmjiMbnCNsfNpx07Y32UixL5YfBvOMit9is2d0FTda+UQUsEsU0HVkPzWB0jGt5nHsBvbiQBskBcOUVRViTblHSIgnauRERcyCIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgItJes2sWPWu93GuulPFS2SndVXIsd1HUsQa5xc9jNuHZrj5d+U/BQW4ccprvhGMZRgOJXPPbffarox+HcKMwRBzmumk6wBa32Ha2Bvt5bQWqumrq4KCmkqKqaOmp4xzPlleGsaPiSewUWZTZy7iVJNJW2NuBNpOWOkZBKbi+oOtue8u5AwEHQA2d9/JRyx/J9s7MDvWJ5beb3xEtl4rRXVIyasM5a5rmOZHGWhpbG0xMIbs99+4kIN1lHGnEcPyTErDcLk43PKpOS0R01PJMypHs7f1GtLA0c7O5cPnA+S6Ldm+WX7KcvscGGT2WmtkBFryC6TNdSV9QW+yBEwh/ICQSQfc4diplabNQ2G2UVut1JDRUNFA2mpqeBgayGJoDWsaB5NAaBr6gs1BUldwszTiHwuobDmmc1llyAVpqay6YNI6hMkW36ga5wJDdPAJI2eQE+9TWbhpjFTnNJmdRZaSpyukpfBwXiWMGeOL29hp8m76jwSADpxHl2UmRB8a0NGgABsnsvq11/v1NjtnuNxqBLNHQ0slXJBTRmWZzGNLjyMHdx7aAHmeyriK5Zhxkx7CMixi413Dm2vrTV3S2Xu0tdX1VMx/sRac7UQk5dkg75ZBo9tEJXkHEagoqbJaaxdHKMnsdH4ubHbfVR+LJIcY2EE+wXcp1v+Y7G45Dh2Q8TY+H+S5HXXnBbjaia2uxa13Bj6eeckcrJ5Gj74xuj7I7Hn+pTKyYHjuN3y8Xq12ShoLteJBLcK6CBrZqlwAAL3eZ8vLy2SfMknfIOmnpIKTq9CGOHqvMsnTYG87z5uOvMn4ruREBERBWmA3W1u4w8TLRQ4fLZayldb6isvxZqO8PlgJBB13MQAYe58/d77LULtMWbN4q36SvmonYG6hpxbImAeIbU7PWL+2+XWtbP2KaICIiDxX/lGPkv/dExM8RscpOfJLHAfSEELfarKNvcu173xdz8SzmHflaF53/yc3C3KpuJ1v4j0WJQ37HqCqks766S5tpn2+aSJvVqGx73LyQyFvKRo9ca7tJb+ri02IYdZcBsMVlx63Q2m0xSzTRUdOCI4nSyvmkDB9FpfI8ho01oOmgAAAMPFOJOL5zcr3b7DfaK6V9kqnUdxpaeTctLK0lpa9vmO7XDfkS06PYqSqJ5Zw5oMhsmR0ttqJsUut9hbFU32xtZBXbZvkd1OXZLQSAT3AcQCPNaJl5y/Aq3A8ZdZLjnlJUwmlu+XdaCB1NK1o5ZZIOxLXaeSR5aaNuc5BZKLUWHLbJlEtxis92ornLbql9HWR0k7ZHU0zSQ6OQA7a4EHsVt0BERAREQEREBERAREQEREBERAREQEREBF8c4MaXOIa0DZJOgAoTnvGvCuGmKQZLf79DBZaiqFFDVUrH1Qln9r720RNcS7cbx9RadoJuihlbnF5h4nWzGaXDrjWWSopHVVTlAmjbR0x0/ljA3zPcSwAgAa52nuFo7bjHEjKMezG15hkFtsj66odHZq/EBJHUUlNzHTnGUH76QG71sDZ17kFi111orWYBWVlPSGolbDCJ5Ws6kjjprG7PdxJAAHc7UXpeLeM3LP7zg9vrnVmW2ikFbV21sEjemwhhYDI5oZtwkYQA4nR35LXO4E4pc7bhkGRwVOX12JadbLrfKh01W2T2Pvr3t5Q95MbDsjzaD5qwg0NLiAAXHZIHmfL/8IKkpsv4o8Q+Fstxx/FqPAMslrRFDRZfI6dgpdjcxEGi15BOmO97e/Y7W/uHDSvvmcYtlFZl97o5LNTck1ltlT0rbXTFrg6SaIg8w9s6GwRpvfsp6iCJ4lwpxLBbzkV2sVjp7dcciqDV3WoYXOdVylznFzuYn3yPOhoe0e3dSxEQEREBF8JAGz2CrjMuLFWcRdc+Gtnp+Jlf6SFsfT265RRw08n/SOllJIAZ22B324eXmAsGtrYLdRz1dVK2CmgjdLLK86axjRsuJ+AAJVVTcUL5xXwyxX7gzNZbtQ1V1NPWV98bPDHHTRucJXxs5Q5xPLyt/0wfjqT0nDmdnFKrzSfJr3PFNQNoYcdkqG+jqYHlMjxGB3e4sb7ROx7Q7ggCYUtLBQ00VNTQx09PE0MjiiaGsY0DQAA7AAe5BE7Lwlxuw8SL7ndNTTuya8wR0tTVTVMj2thYG6jjYTysbtgcdDz38VMURAREQdZnjBIMjQR7uYJ4iL8Iz84KvcxyC34pQ3i83aqZRW2gbLUVFRJ5Rsbsk9u57DyHcqu7dx6tU1jud8u2PZHi9ioaI3D0neqFsUM0WwByBr3O5zzDTHNa477BB6G8RF+EZ+cE8RF+EZ+cF53tXH60Vj7nDcbFkGOV9Ha5bzHQ3ikZFLV0sY298PLI5pI9kFri1w5hsBfcX4+WjKLpZ6Jliv9u9N0clbZ6i4UjIo7ixjA9zYvvhIdykECQM2O4JHdBYDLDY6T5QsmQHIav05V42KJtkIJpRC2o5zUc2uUP3putg6HkVY/iIvwjPzgvIWNfKDv2Q8Dssym5Y1fLTU25lwLbhQ0lJK1jY55ow+OJ9T7boGsDpA8tBLHcpcCNyt/HmjtMsFoZZMjyi8QWSkvFQ+20MPtwSh46h3K1rXbjJLAdnmAYH6Og9JeIi/CM/OCeIi/CM/OCo/GuL1gy6/WS12s1NSbxYjkNLVdMCE0wkjj07Z5g/crfZ5fcdkEaWpdx+sMlogqqO2Xi5V1Vcqy10loo6ZklXUyUsjmTPa3n5RG3l3zvc0ac3eiQEHobxEX4Rn5wXMHY2PJU3gXEO3cQaOuko6ett1Zb6k0ldbblD0amll5WuDXt2R3a5rg5pIIcNFW/Sf8kh/wBBv/og7kREECv3COg9D5WMOkhwPJMhDH1F/tVHGZjKw7bI5p0Hnu4Heied3cHusI53fMHvWB4lerLecrrbtTGCtyq20bGUcVUxgLnSsBHSa7lkd9WgBzbOrKRBi0F0orq2Z1FVwVjYJn08pp5WvEcrDp7HaPZzT2IPcHzWUqxunBmHGLDmU3C30fg2X5FLHVzXQ0vXhdO12+Z0RPKOYF4OhoF5done8lvFB+JZHhOGZTSVtVkt8o/butrtsptbqpjNyRh/csJ5XuAd5NbtxHZBYqLhHKyZpdG9r2glpLTsbB0R/KCCPsXNAREQEREBERAREQEREBERAREQRjihVUVBw0y2pudqffrbDaKuSptUY26tiELy+ED4vaC37Vq+C9gxu3cK8VdjuNsxm0VNHDc4LS9p56N8zBIWu33DwXkE+e9qQ5r6b9Tb96tdH1j8BUejPE66Xium7o8+/o8/Lv6kwr036m2H1l6PrH4Cn9J+G10vFdNvW5NfR5+bX1IN0iIgIiICIiAiIgIiINJnF1rbFhd/uVstbr5caO31FRTWtm+aslZG5zIRoE7e4BvkfPyWk4MY/bse4Y47HbsUpcJ8VRQ1tTY6WIRilqJI2ukY7sCXNceUlw2eXut1nNLdK7CcgprHcYrReprdUR0Nwn/e6WoMbhHK7sezXFrj2Pl5FMFpbpQ4Tj1NfLjFd71DbqeOuuEH73VVAjaJJW9h2c4OcOw8/IIN4iIgIiICIiCk+PWBT8T+HWW4vS1LKSruML2QSyj2BI14ezm19EuaAfPsT2KrvMbfn/GfhvfcXuuFR4lWSUkc8NbPdYamCashmiljja2MlwicYztztED6J2vUUlko5ZHPdFtziXE8x8/51x9A0P4E/nu/ag8n5FiWdcV79Ne7rinqqy1Y1dLbRUMlxgqJq6rq42NOnRuLWxNEY0XkEl2yANrd0HDzIIKzgNI+38rcYpJYrsetH/wZxtpgA+d7f3zTfY5vj5d16V9A0P4E/nu/anoGh/An8937UHk2y4Rmts4P8RuHUuMF4mo756Ku8ddAYq91U+Z8MfIXB8bvv+iXgNHL591J+HmB3yx8R6y6V1D0aCTEbVa2y9aN26mF05lZoOJ7c7e+tHfYnurZyGosVu4lYnZp8jmttfcYqt9PYGwtey5CNgLnOkLC5nTB5hyvbvffm8lMvQND+BP57v2oPHfDbh1nHC+m4Z3gYu+81luxafHrna4K6njmppHTxTMkDnvEb2/ey08riRsEA+Sj8XALKH2zGb7fMHtmT1ttvF7lr8UqqqCRk0FbUGRksEr/AL3zsLWkB/KS0kHlPZe5PQND+BP57v2p6BofwJ/Pd+1BTPCPGKTHMdmdT4Nb8Amqp3PltlC6F+wOzHyOiAaXEe4E68tlXhSf8kh/0G/+ixfQND+BP57v2rOYwRsa1o01o0Ag5IiICIiAiIgqv5O1uwu24tkDMHvNdfLdJkNfNWT17S18da6QGeNu449ta7yOj/pFWoq64H3b0xjt5l9QfuedO9VkPgOh0fGcrx/wzXSj31fnb0d6+c5WKgIiICIiAiIgIiICIiAiIgIiIIxxQpaKv4aZbTXO6vsNtmtFXHU3WM6dRRGF4fMD8WNJd9icL6WioOGmJU1sur79bYbRSR011kO3VsQhYGTE/F7QHfanFCqoqDhpltTc7U+/W2G0VclTaoxt1bEIXl8IHxe0Fv2pwvqqKv4aYlU2y1PsNtmtFJJTWqQadRRGFhZCR8WNIb9iCToiICIiAiIgIiICIiCKcWIrJPwszKPJZpqfHH2atbc5qYEyspTA/rOZoH2gzmI7Hv7inCaKyQcK8Njxqaaoxxlmo22yapBEr6UQM6Ln7A9os5Sew7+4LL4hVc9BgOS1NNZW5JUw2ypkiszm8wr3iJxFORo76hAZrR+d5FOHlXPX4BjNTU2VuN1M1sppJbK1vKKBxiaTTgaGumTya0Pm+QQSBERAREQEREBERAWNcrlSWa3VVfX1UNDQUsT56iqqZBHFDG0FznvcSA1oAJJPYALJWDfLLRZJZLhaLlA2qt1fTyUtTA/5skT2lr2n6i0kfagoTLflN8HouMuBU77viV5qJoK8x5Wy80ro7LqIbY5+yG9b5o25u9e9Xfima49ndukuGNX62ZFQRymB9VaqyOqibIAHFhdG4gOAc0689OHxX4g8ZOCd54V8abrgAp5qyrZWiC3crdvrIpHDw7m60CXNc3sPJ2x5hfsf8nfg/ScCuENgxGARuqqaHq188Y7T1T/alfvzI37I39FrR7kFkoiICIiAiIgIiICIiCF8K7Xm1ps1zjzu8UV6uUl0qZaOahYGtjonO+8RO1HH7TW7BOj/AKRU0VU/J0teE2nFcgjwS8Vt6tsmQ18tZNXMLXR1rnjrxN3HH7LXaAOj/pFWsgIiICIiAiIgIiICIiAiIgIiINLmvpv1Nv3q10fWPwFR6M8TrpeK6bujz7+jz8u/qTCvTfqbYfWXo+sfgKf0n4bXS8V029bk19Hn5tfUsLihS0Vfw0y2mud1fYbbNaKuOpusZ06iiMLw+YH4saS77E4X0tFQcNMSprZdX362w2ikjprrIdurYhCwMmJ+L2gO+1BJ0REBERAREQEREBERBo85pbpXYTkFNY7jFaL1NbqiOhuE/wC90tQY3COV3Y9muLXHsfLyKYLS3ShwnHqa+XGK73qG3U8ddcIP3uqqBG0SSt7Ds5wc4dh5+QWBxYisk/CzMo8lmmp8cfZq1tzmpgTKylMD+s5mgfaDOYjse/uKcJorJBwrw2PGppqjHGWajbbJqkESvpRAzoufsD2izlJ7Dv7gglaIiAovf8irzc32my+GZWRRtmqKqsYZI4Wu2GNDGuaXOPKT84AAefcBShQSmJOd5ON9gKX/AGZXXk9MVTVMxsi/vEfVYObMfx9af1PJ/iE5sx/H1p/U8n+IW4WnxjLrTmVFVVdnq/GU9NWT0Er+m9nLPDIY5WacATpzSNjsddiQuvP5R0jsXObMfx9af1PJ/iE5sx/H1p/U8n+IWVdb5b7Gymdca2CibVVEdJAZ5AzqzPOmRt35ucfIDus5M/lHSOy3afmzH8fWn9Tyf4hObMfx9af1PJ/iFuETP5R0jsXVLlPAl+Y8VsY4h3O42yXI8eifDSPFpd03g7LDI0znmMZc9zNEaLie/bVhc2Y/j60/qeT/ABC3CiEnFvE4rfU1rrrqlpr0Melk8NL7NeZWxCHXJs+29o5h7Pfe9d0z+UdI7F235sx/H1p/U8n+ITmzH8fWn9Tyf4hbhEz+UdI7F2n5sx/H1p/U8n+ITmzH8fWn9Tyf4hZUl8t8V6gtD62Bt0ngfVR0ZkHVfExzWukDfPlBe0E+W3BZyZ/KOkdi7T82Y/j60/qeT/ELJpMiu9lq6Vl7loq2iqZmU4qqSB0DoZXkNYHNc9/M1zyG7BBBc0aIJcM9aDN3FtkgIJB9I0A7f9rhWVMRi1RRVEa+UR8iJvNlhIiLx2IiIgrrgfdvTGO3mX1B+55071WQ+A6HR8ZyvH/DNdKPfV+dvR3r5zlYqhfCu15tabNc487vFFerlJdKmWjmoWBrY6JzvvETtRx+01uwTo/6RU0QEREBERAREQEREBERAREQEREEY4oVVFQcNMtqbnan362w2irkqbVGNurYhC8vhA+L2gt+1OF9VRV/DTEqm2Wp9hts1opJKa1SDTqKIwsLISPixpDfsWbmvpv1Nv3q10fWPwFR6M8TrpeK6bujz7+jz8u/qTCvTfqbYfWXo+sfgKf0n4bXS8V029bk19Hn5tfUg3SIiAiIgIiICIiAiIgj/EKrnoMByWpprK3JKmG2VMkVmc3mFe8ROIpyNHfUIDNaPzvIpw8q56/AMZqamytxupmtlNJLZWt5RQOMTSacDQ10yeTWh83yC7M5pbpXYTkFNY7jFaL1NbqiOhuE/wC90tQY3COV3Y9muLXHsfLyKYLS3ShwnHqa+XGK73qG3U8ddcIP3uqqBG0SSt7Ds5wc4dh5+QQbxERAUDpv/fzKP5KT/ZlTxQOm/wDfzKP5KT/ZlduS/v8AT6wyjZKreIUlxzvjvZ8AdkF0x2xQWCS+zGy1bqSpr5vECFsZmb7YYwbcQ0jZe3fYKpcPlfScOaHC6Cqya4ZBcs0v0dGLZefRstSyCpmMslVVtaXNYAWuPIOZzi0Aa2F6XzzhPivEyS3S5Fa/GVNue59HVQ1EtNPAXABwbLE9rwHaGxvR0Nhaf9zzw/GOUVijsJp7dQ1k1fSinrqiKaCeUkyOZM2QSNDuY7aHcuu2tLKaZuxeca5t2zjhlh9vyu63R1fZeKLLB4iC7y9bpCoLW888fTMkjAQ1svK13bY0SVZnGGkrbjklswfFazLZ7zabH46WWnyiS3QQwFxjimqJyySSomLo36a4OB5SXee1ZEfAPAYcSueMRY5DFYbjUsrKiijmla3rtDAJWEP3G/72w8zC0kjZ7kk8Ll8n/A7w21itsklSbdTuo4Xvr6nnfAXl5imd1NzxlxJ5JS4dz27qZsjs4AZZcM54KYVfrtKKi519qglqZg0DqScoDnaHYEkb7du6rzgJbLlxLxK18TrxleRz3qsqaipNmpLk6G3wNZNIxtJ4YewQ0MDSXAvLtnmV1YjiNowTHqWx2KkFBaaXn6FMJHvbGHPLy1pcSQ3bjob0BoAAABReDgHgdLl7smp7CKe7Oq/HudBVzsgdUfhjAHiIv335uTZPfe1ladQoWwZBkVJwx4dcV35leq/IchvtFBW2iSsc63TRVVSYn0sVL8xhjaTpzRzbjJJPddVwIHD/ACck6DeM0W/q/wCM6dX/AG3gHgVoyxuSUmPRRXVlQ+riJnmdBDO/fPLHAXmJjzs7c1gPc9133Lghg93ZlDKvH4JYsnfFLdo+pI1lTJGdsk5Q4BjwdHnYGuJAJJIGsc2RSuV1GfcUuMef2azT1UFFjDqSlpIKPKpbK6Iy07ZfEPZHSy9fmc4gc55QI9cu9k7Gw0eY8ReIzMIzfK7hZ6jHsZoayqjxeufRG51kz5WyVHVYGvMbek0cg03medjyCtDKeAOB5pWUlZd7EZ6ympW0LamKtqIJZYG+UUro5GmVv1SF3mfisrMOCmF54+2SXiyiSe2xeHpKmkqZqSaKLt97EkL2OLO3zSSPqVzZFV3vh1ST/KlwumqL3kMjqfD6lwqG3ieGSd0NVSgc5jc0ODg4l7dcryASDoaimS33Iq7hzxQ4ouzO82u/Yze6+nt1qgrSy3QRUk4jjp5aYexKZQPac4Fx6g5SOyv2/cD8JyS3WCirrL94sEfRtjqarnp5KaMtDSxskb2vLSGtBaSQdDYXTeOAeBX/ACp+RV+PRVF0knjqpdzzNgnmZrkkkgDxFI8aGnOYT2HdJpkQTCrfcs64+cQprnkN/prbZZLNPRWWmuUsFPFK+kbI8Pa1w5mkjRjPsHbuYEnYtvOP+ZIP9Y0H++QrIteI2my3+93ujpOjc706F1fP1Hu6xiZ04/ZJLW6b29kDfv2Vj5x/zJB/rGg/3yFdGBFsSn1+qxthYaIi8hBERBVPydLXhNpxXII8EvFberbJkNfLWTVzC10da5468Tdxx+y12gDo/wCkVayrrgfdvTGO3mX1B+55071WQ+A6HR8ZyvH/AAzXSj31fnb0d6+c5WKgIiICIiAiIgIiICIiAiIgIiIIxxQpaKv4aZbTXO6vsNtmtFXHU3WM6dRRGF4fMD8WNJd9icL6WioOGmJU1sur79bYbRSR011kO3VsQhYGTE/F7QHfanFCqoqDhpltTc7U+/W2G0VclTaoxt1bEIXl8IHxe0Fv2pwvqqKv4aYlU2y1PsNtmtFJJTWqQadRRGFhZCR8WNIb9iCToiICIiAiIgIiICLXXDIrTaK6ioq650dFWVr+nS09RUMjkqHfwWNJBcfqG1EKDjbjl+yPMccsRq7zkWLQGWtt8VLJHuTRLYmPe0Nc49h2JHtDug2vFiKyT8LMyjyWaanxx9mrW3OamBMrKUwP6zmaB9oM5iOx7+4pwmiskHCvDY8ammqMcZZqNtsmqQRK+lEDOi5+wPaLOUnsO/uCrfPcs4qZd8n2svOO4Pb7HlMjah1TjWTg1xmo2sl3GxkXZ0soEYbG8a9std8VJMLxW+32qwbLai63LErfBYqds+AUsMUdHBUPhPMyQ8gf9752tDPZAMLfrCCfX/IbZi1nrLreK+nttto4+tUVVTIGRxM/hOJ8goNfuPNjt1mxO7Wa2XrMrdktSIKKpxuhNUxjdgGWXuCyMdyTo/NPZZuIcDMKwk5X6Ps7ZW5TUuqrvHXyvqo6pxLjylkhc0NAeQGgAa0DvQXzMeKeI8J8Crb0Aaqz2qdtA6jx6nFS+OdzmtbCI4+zXbe0aOtcw3rYQZNHes4m4qV1tqMboKfA4aMOp7344OqqipPIeXogey0ffAd+emkHzCiNjx/M+HVuut/zHJKDLqiof1Kp8FNFa46SFjdMDC+TkcBt23Pc0nmHf2dGUzX/ADa6Zhi77JZrY3CKuj8Vc6y6TSQ18TnNcWRRwBvZwJjJ5z/CHYgFYNj4K08UWbU2T5Dds4tmUVBfJa75KJKWjg5nlsEDAAWtAcATvvyNOgd724eJOHN4WJs0U3GSFrbBJTYtkV1p77s2+qtEEFdBK0cvtmWCZ7GM9oe08tb59+x1J/Td5/Iu9/01D/iVMbLZLfjlqpbZaaGmtltpWCKCkpImxRRMHk1rWgAD6gs1dPio4I9+63jcgPpu8/kXe/6ah/xKem7z+Rd7/pqH/EqfInio4I9+5eNyA+m7z+Rd7/pqH/Ep6bvP5F3v+mof8Sp8ieKjgj37l43Kfu3FuGx5lYcUrsbvcF/vrKiS3Un/AAV3XbC0OlPMJy1vKCD7RG/dtSL03efyLvf9NQ/4lcsnut7puLOFUNHikNzslTDWmuyB7QZLY5sYMbWn3dU7af5FPE8VHBHv3LxuQH03efyLvf8ATUP+JT03efyLvf8ATUP+JU+RPFRwR79y8bkB9N3n8i73/TUP+JT03efyLvf9NQ/4lT5E8VHBHv3LxuedeLHyq8W4H3Ghos2tF/sk9dEZqYupI5o5Wg6OnxyObsHW272NtJGnDdB8W/8AKHWXIaOisXDWx3S+3yunYyndJF03NqN7pwyMNeZT1ukeTXthpZ25tj2Vxq4MY3x4wOsxbJabqU0v3ynqowOtSTAHlljJ8nDZ+ogkHYJXgL5NXyHcpxD5WUFNl9HWxWHFv+OqO9U1I/wVyljkj8PG2Y+yx23iQs7u1E5uu/MJOVT+2mIn49y/JV/yk+KHyjcHy+oxziDmN6opTt0TrXN4SkrIt6D4+gGNe0/WNjycAQQr9/yauV2fC6CtF+F/GT59cjFQ1M9FLJR1UNJG4tc2bl+dzyVQcS4t+9a9ktO/R3GLgncuK1ozik4g0tuyzFooH1uN0tlpnU96pJ2sJMbJnEs2eVoBIIcXe0OUALl8m66YvwX+TpgNpvVTW4bE981FFDmfSoaqSrdNNJI0s5tNDn9QsaTvkLNkk7PExXJZswsORV9xobVerfcq62ymCtpaSqZLLSyAkFsrGklh2D2cB5LcKL1fDLFqmnyWOKyUlvkySB1Pdqq3RilqKtrg8bfLHyvLh1H6dvY5j3URufBm9Wbh5YMXwDO7liHoirNQK+shbdJqmIukc6CQzHu3cnYneuVvnpBIuFdrza02a5x53eKK9XKS6VMtHNQsDWx0TnfeInajj9prdgnR/wBIqaLzr8niOezZVeqDAnUd+4WT3WvnrrtWzSMuNNdS/c8IjdFG10QdyhpAPbZ5j2U6s/H21nE8nyLKrHfMBteP1XhqqfIaMxNlaXANlh5eYvYS5o3rzOvcUFoItVYsptGTWq2XK13CCsobnCKijmjf2qIyAeZgPcjRB+1bVAREQEREBERAREQEREBERBpc19N+pt+9Wuj6x+AqPRniddLxXTd0eff0efl39SYV6b9TbD6y9H1j8BT+k/Da6Xium3rcmvo8/Nr6lhcUKWir+GmW01zur7DbZrRVx1N1jOnUURheHzA/FjSXfYnC+loqDhpiVNbLq+/W2G0UkdNdZDt1bEIWBkxPxe0B32oJOiKH3jihabbllzxOnbNcMro7E7II7TCzT6mn6j4hyOPs8xkZy6325m/FBMEVSVuR8WM64V2244vj9qwTLqqsLaigy2R1S2npAZBzgwf9I7UZDXDQ24H3FSiswa7VvEy2ZR633WltVHRmB+M0/IKKolIkBlkJbzE6e3QBGjG0+8hBIr/kdrxWz1t1vFwp7bbaKPq1NVUyBkcTPi4nyCg9+48WS3WnErnZ7Xe8xt+TVAgoqnHKE1TI2kgGaXuCyMAkk6PzT2WyxfgrhWHsyZlusMHTyWodVXeOre+pZVvcXE8zZXOHL7RAaABrQ12UwoaGmtlJFS0dPFSUsTeWOCBgYxg+AaOwCCIUd6zmo4p3G21GOUNLgcNGHU17FcH1VRUEMPL0dew1u5Ad+emkHuQo9aOE2W3zhpdcbz/iJcbxcrhWCf0vjkTbPPTQgxkU8boydtPI4FxGyJHD4FWuiCHP4RYlV12L19ws8N2umMwNp7XcLjueogADRzczvN/sNPMe+9nfcqYNaG70ANnZ0PMqJ5vn7MbprjRWamgyXMaehFwp8Wp6+KGtqYeoI+oGvO2x8xI5yOXY159lpK6z8QcyfgV1ivbMCipyyryLHm08Ve+pd7B8M2o2A1oIkBe0bO2nXmEG+4n3Gai4dZe+gvENjulPZqueG4StLxROEL+SdzA1xLWuHN2ad8pAB8lW2N8UczvvA3Ab1g1qg4i3WtbBR3K4107rYz72wtnrAyVjXOa58bi1umnUjSBrYW9yfgpgNktXE+/1FtqoJcqtdTHf6yjfLNUy0/Qc17Yo/aAdy8xDWt2XHyPksvg5nWCP4P4fNjl66eLRQRWW2T3fdLLK6BphEREoYXSDpOB0O5Y4jsEG2iwrIDxSqckqMzrJscNEKWDFW0sTaeN51zyuk1zPJI7A/N2Rsg6Wbw74YYrwmsLrLiNkprFbHzGofBTA+3IQAXuJJLjprRsk9gB7lKEQEREBERAREQEREEDye1Xup4s4VXUeVw2yyU0NaK7H3uAkubnRgRuaPf0jtx/lU8VScSavBbRxv4X12R+PhymQ11FYKiJjzSc8sbWyxyEDQc5uuUH6/grbQEREBERAREQFqshxSy5bSxU18tFDeKeKRs0cVfTMmayRp217Q4HTgfIjuFtUQQqPhmKXilU5vDkV9Lqih8HLYX1x9GOcOXklEWvZeAHDfffMTr4xe355nnDThjX3riZZY8jvFJXdJsGA0ctS6WlPKBMY3kO23by7WgA3sFbq6K6up7ZRVFZWTx0tJTxulmnmeGMjY0bc5zj2AABJJQVRwN4kWO8UNVb5cWpOGV4qrlVy0+O1HJTVVwjD9eObCY43OEnclwa7uPnFW1UU0VZA+GeJk8Mg5XxyNDmuHwIPmqo4Z8NKi61NLmGfOsGWZXS11TNYcht0LC6C2yh3QiEjY2cwDJH/AMIbeSHEnazaPA8v4f2jOKvHcnq8wu90ndXWq3ZZPuloZHOc50TXxtDhGebs3yaGtA13KDfZPwhw3MajGZ7vj9JVSYzOyps5aDGKJ7Swt5AwgAbjZ7J9k8oGtLpouHldbOIN9yqDLr5VsudIII7DX1AkttJKAwNkhjDQWH733GzsvefetPU8Z3YbBgFBm9irrdkuVF1P4SzU77hBS1DWtc5j5IwSBp29gEaa8700uVnIKiiu/FvAOFU1VdrNbOJmZQVga2lsUwtzZqUke2TLsdQDmJa0aPYD4rf3DjLZ7FmWK4neKS40F/yKm61NEykfNAx/KS6J8zRyhw5Xeehob7KfIg1Fny+xZDcLjQWu9W+411tlMNbS0tUySWlkBI5ZWNJLDsHs4BbdRCbhLiZhysUlkpbTUZTA+C71lrjFNUVQcHgvdIzR5/vrzz+e3b3tR+r4S36w8P7FjOCZ3cMcfa6kzG4XWBt3mqoSXkwSGYg8u39nDu0MaB2GiFnooVLcM9ZxUhpGWqzScPn0fM64CpeK+KpAPsmM+yWH2QNeWiSfIJwz4kzcRI7z4jFL/istsrHUhZfaUQeJA7iWE7POwjXfQ7nXuQTVERAREQEREEY4oVVFQcNMtqbnan362w2irkqbVGNurYhC8vhA+L2gt+1OF9VRV/DTEqm2Wp9hts1opJKa1SDTqKIwsLISPixpDfsWbmvpv1Nv3q10fWPwFR6M8TrpeK6bujz7+jz8u/qTCvTfqbYfWXo+sfgKf0n4bXS8V029bk19Hn5tfUg3Srb0rRfujvRnqW/0l6p+J9c+l7PS8Zy+j+fl8+b77rm+vl96slQzwubfdk8T4yi+5z6A6fg9DxPpTxG+pvl30+j2+drfu96CZoiICIiAiIgqn0phP7qf0d6HrfujepniPS/OfC+i/HcvQ5eprqdb2t9Pevp/RVrKGelM2+7J6O9D0X3OfQHiPS/OPFelPEcvQ5epvp9H2t9PW/p/RUzQFGc84a4txPsrLRldior7bmTCoZT1kQcGSAEc7feHac4bHxPxUmRBCanhrNNxVo80jyu/wQwURopMcZVAWuUe3yyGHl/fAXk82z5N8tLSUly4oYTh+V3G+0Nrzy509QZbNbbADRzT05cPYldKSwPaCfmjuG+8lWiiCuqjjlj+PU+CxZcKjE75mHJHQWishfLKKh3J94e6Npa1+5Gj2iO+x7ip7BcKWqqamnhqYZaimc1s8TJA58RLQ5ocB3aS0gjfuIK7JoIqgNEsbJA1weA9oOnA7BH1g99qIwcIsWoMwvuW262MtmV3qkNFV3mlcRO9mm6OiS3Y5GEHl+iPcgmSKpBhfEnhzwrbasQyanzfKYKsysrc4fIerTk94i+LuXgaAce29kqSVuf3W1cQ7Di0+IXeupblRmaXI7fG19vpZmh5dHKS4OYNR9jo7L2DzJQTdFE8M4rYnxDr7/RY7e6e6VVgqnUdziiDmmmlDntLXcwGxuN+iNg8p0SpYgIiIOL4mSOY5zGucw8zSRstOiNj4diR9pVO3C/V3ybcTut3yq+ZJxBtdZfAYHw25s1RaqeY9xJ0wC+Njub2tdg5jGt8grkRBxY4PY1w2ARsbBB/mPkuSrC84pXcOL/nPEW1VWR5VPW25rm4bHUNdA+oibproA4bY5zWtaQ3z9o6cS0Cp/lC/LotvBjE8ErqbHqqe95M2Kvks9zLaeooKEPb1XTR83M2R4L2R7HLzNe4k9MscHqdFgWG+UWTWO33e2ztqrdX08dVTTs8pIntDmOH8oIKz0BERAREQcZJGxRue9wYxoLnOcdAAeZJVY5TbbhxWyXK+H+TYafubyWuEOvMlcY311Q9wcY4mRnmDWBvdxLSHN8i1wKxcybFxwuOccLLlYsjtOOxUNO2oyWnk8LHUySEPdBA4jbxygBxAc07e12u3NLKnLMP4YvxXE6q7UdnnruS22a3Tzkyz8jNNY3ZLjoNA5j5ktG9uAIbygobVh+PwUlLFTWizW2nEccbOWKGnhY3QHuDWtA+zSi9flF9u/EGvw+DGbrQ2I2l0z8xZPEyOOd/sxxwNOy94HOSSPZLW7aQ4E6E4LeuM2O5LYuLVgtUVifdg+2UFqrpnOlpYngsdUPHLvnLeblGvZdotBCtOkpIaClhpqaJkFPCxsccUbQ1rGgaDQB5AAa0gi/C3h8zhhhFvx4Xm55C+mL5JLleJzNUTyveXvcXHyBc52h7gdbPmpaiICIiAiIgKsr9arJL8oHFrhPlc1JfobPVR02NtcenVxFw55yPiw9vtVmqsr9dbJF8oHFrfPik1XfprPVSU2SNaenSRBw54Cfi89/sQWaiIgIiICIiCMcUKWir+GmW01zur7DbZrRVx1N1jOnUURheHzA/FjSXfYnC+loqDhpiVNbLq+/W2G0UkdNdZDt1bEIWBkxPxe0B32pxQqqKg4aZbU3O1Pv1thtFXJU2qMbdWxCF5fCB8XtBb9qcL6qir+GmJVNstT7DbZrRSSU1qkGnUURhYWQkfFjSG/Ygk6q70Vi/7p/0l6w1Xrr6neH9X+/Q8B43m8V83XP1fY+d5e73q0VW3pWi/dHejPUt/pL1T8T659L2el4zl9H8/L58333XN9fL70FkoiICLq8VB+Gj/OCeKg/DR/nBB2ourxUH4aP84J4qD8NH+cEEA9E/+0L6T9fv/hbw3qH1/wD5vm9JdPq/+DzdP6uf6KsRVf4jEf3SXN6AuPrp6p69ZPa9H+A8Z/yTfU5et1fvmunvl+lrsrL8VB+Gj/OCDtRdXioPw0f5wTxUH4aP84IO1F1eKg/DR/nBdqAiIgIiINBkGBY9lNlvdpuVpp5qG9RGG4sjBidVMIIIe9hDj2J7733UQq+DNZZ8ew6x4Pl1xwu2Y7OHGCONtYK2Dm26GQy7J7cwDu5BdvvoKzkQQmiuWet4pXOkrbTZxw+bRiWiudPUPdXGcBnNHJERrRJkILfc0b7nS1OPcf8AHq/AavLcio7rw+t1HXC3VEeW0vgpY5SWAHW3AsJkADt67E9gCrMWFd7Lb8ht8tDdKCmuVDL2kpqyFssb/wCVrgQUHfRVtPcqOCrpJ4qqlnjbLDPC8PZIxw21zXDsQQQQR5qk/k5fK+wn5SLrjR2cz2i+UckjjaLiWiaSnDyGTM5SQ4FvLzAE8jiR3HK90b+WnjVnw/hNdeJVJHcqTI8Ztht1pdarnLRMg8S9tMx3Kzt97dM2QAAEmNo5h2I/L3hZw64q1OQ269YFjeSOudJKJKe42yil1C7y2ZAOUDzB2dEEg9ig/cjILmbPZK6tY6jbLDC50XpCp8NTuk17DZJeV3I0u0C4NcRvfKfI/ivxl4QcVazK7vkeWvgye610xmqLhb7pS1rpj5DUcLy5jQAGtZytDWgAAAAL9P8Ag7fM2zijidxQx6Ky5LaaWBvhGyxyxSPf1N1Qa1xDHPaA3lPdungaDyFanI3+CP5l6FOBh0xGfeZnXq1bfhLLVG15R/yePGl9JwmqcMzaX0BU4/N/xfPeHeGE9LKXODGuk1zOY/nGh5Ncwa7L1Z91DDfytsX6yh/tLlyN/gj+ZORv8EfzK6LB3T1jsanH7qGG/lbYv1lD/aT7qGG/lbYv1lD/AGly5G/wR/MnI3+CP5k0WDunrHY1OP3UMN/K2xfrKH+0qrz7idiHGeny7hxRZpX4e6GGATZLTujggnY9+5IaeaQ6kPK0tcWdhz9ie4Vrcjf4I/mTkb/BH8yaLB3T1jsalf1vHTGp3XnCMYymCmyKgs/NR329h0luM3KGsDqlxDZnglrnBpJO3HuWuA2/Daislxt+LHIMlx/P87s9NLE2+wsp+vzP0ZTE1pJZtoaCW62G7OtkKU8jf4I/mWHdbNS3ijfT1EQIPdkjfZfE4d2vY4d2uBAIcCCCAQmiwZ1a4+MT9INSYotJhF2mv2G2K5VLg+oq6GGeRwaG8znMBJ0PLuT2W7XDXTNFU0zthJ1CIiwQRF1GphBIMrAR7uYIO1F1eKg/DR/nBPFQfho/zgg7VDLp67fdWsngfBeofo+f0jz68R4rY6XL7+XW9qXeKg/DR/nBV7esetlXxvxzIX5JV09dR2uppo7NGHGmqGvcCZXn5oc3yG+6Cx0XV4qD8NH+cE8VB+Gj/OCDtRdXioPw0f5wTxUH4aP84IO1ERBpc19N+pt+9Wuj6x+AqPRniddLxXTd0eff0efl39SYV6b9TbD6y9H1j8BT+k/Da6Xium3rcmvo8/Nr6lhcUKWir+GmW01zur7DbZrRVx1N1jOnUURheHzA/FjSXfYnC+loqDhpiVNbLq+/W2G0UkdNdZDt1bEIWBkxPxe0B32oJOoZ4XNvuyeJ8ZRfc59AdPweh4n0p4jfU3y76fR7fO1v3e9TNVd6Kxf90/6S9Yar119TvD+r/foeA8bzeK+brn6vsfO8vd70FooiIKjznN7Tw7xqpvl6mfFRwFjAyGMySyyPcGsjjYO7nucQAB8VFn8cbfbsXud8vuOZHjMNE+GJlNdKFonrJJXcsUcDY3vEj3O0OXYIJG9Lnxywm7ZpiltfYWwTXqyXejvdJSVUnTiqn08gf0XP0eXmGwDrsdfyqL5tb854p4rBO/Cxjl2x+72+926hr7pBN6QkgkLpInOiLmxgt7NcSdkjYbraDbTfKOx+22XIq282i+2GssMMFVWWm40jG1fQmk6bJo2tkc2RnNsHlcSC0gjege88c4J48io48avtvv8AbLS67wWy500UclZBstEkepdaDgAWvcxw2NgKsuI3DTPOK8WbZDPjHoKtqbHS2O2WSWvglnnDaxtRLNI9r+m0dtNHOToOJ0SArMyPA7teuNTruyAMssuH1lndWl7fYqJKmJzW8m+Y+y1x3rXbW9lBDZePN6rfk3UWZV1pvuOXGpp6LqXKjoaSpa0ytY41MUL6jRgJPKA8h45x7PY6mGUfKEs+L3nKLe7H8iuYxjpvu9Xb6ON8FLE+BkwlLnSNLmhjjsNBeORx5daJgb8Jzu8/JdqeH1XiTqG92qgoLbSvFwp5Irh0ZIw6SMh45ByxB2pOU+1rXZSev4eZBPWceXst/MzJ6SKK0nrR/wDCXC2CAj53sffBy+3y/Hy7oJ/Y+I1pyLLq/HqETy1FJbqW6Gp5W9CSGoMgj5DvZP3pxO2jzHc+6J0fyibHd8dxu5Wiy3691l+pX11LZ6CljfWMp2u5HSy7kEbGhxA2X9ydDZ3qMY1iea8OcpoLtQ4v6ebX4nbLRURsuEMHgaum6m+qXO9qM9X50fORynTTsKvbB8nm/Y/auH12vfD23Z1JQY6bHc8crJ6Yy0kgqHzRzwvkd0nfvjmuHMDojW+4QencGzm28QrCLrbBURMbNJTT01ZEYp6aeNxbJFIw92vaRoj7QSCCreVL8MsepcbxClp6bFaHDHSudPNZ7c6N0cLydd3MaGucWhuyB59tnW1dCAiIgIiICIiAiIgIiIIIz/OHkX/ZaL/9y0PEHizbOH9wtdrdb7pf79dBI+ks9kp2zVMkceupIeZzWMY3maC57gNkAbW+Z/nDyL/stF/+5VzxFxvKbDxdsvELGLG3K2Ms81ir7Q2rjpp2xvmZMyaF0pDCQ5ha5rnDsRreu3q4nl6U/KGVW1vLxxeltVgtFyjwTMbjLcRK426jtrDU0gjIDusHSta3e/ZAcS4d27UebxgdleecIZcauDzi+UUt1nqIZIGtfIYYoyxruYczHMeXggEdwQd6Uf4i4vmGe33E7zfeHzsgsUdDVRVWHemYBHTVjpW9GomLi2OdvSa4aHNyF55Q7zOj4b8JM4wa38HpJccgknxW43ijuNJT18XK2lrJnFlTC4kczGNIJYQ15GwG7WiZm7FbHAzLrtmNhySpvFX4yajya7W+B3TYzkghqnxxM00DfK1oGz3Ou5JXz5Q/Ei6cJ+El6yWzW83G4UojbG0sa6OPmeGl7wXs20b9xJ2W9iNqO8Nq6PgmzL7fnVbaMZttbkldcrTdK+6wRR18VTM+YtDXuDmvj5gHAjvsEEhd3Fe7WDj3wryzEcHyzHr9kFRQ9WGlo7nDMdskY4cwY4lrS4NbzHsC4LK+rmN9e+NlJjtosMtfjOQxX6+TSw0OMRwQS3GXp7MjtMmMQYGgOLjIAA5u9E6WCflHYw7GKe5x0V4muU9zfZWY5HRj0n45g5nwGIu5QWsHOXF3IGkHm0Qo/kdFnN4ybCeItPgs0V1srK631uMTXSlNRJT1DYiJopg/pba+Iey5wJaT5HsopRcIs7tuQ0nE82OnqcnGT1d3lxSOvjBZRz0TaPptnOozO1rGPJ2GnZHN8ZeRK8048SXTDaasxw12P3mkyu1WW62260sbaqmbNUwh8b2Hnbp8Um2vYSCHba7Y7XovMd94S53lFqzbLpLJBRZJdr/ZrtRYy+ujcRBbnR6jknbuMSyBrz2JaPZHN569L00kk1NFJLEaeV7A58TnBxYSO7SR2OvLsrF/MfOFn+bTFf8AVdN/s2qUqLcLP82mK/6rpv8AZtUpXPlH61frPzZVbZERFzsRVfneXWrA7Ld7/e6oUdroA6WeYtLiBzaAAHcuJIAA7kkAK0FRPH/BLhxGwC72i0SwRXZtTBW0gqiRC+WCoZM1j9fRcY+Un3b37kGvp+OVugxy9X2/Y7keJWy1wMqHzXuhbH12vPKxsYY95Ly7Q5Dp23NBHdYQ+UXZKW35DNd7FkGP1tltT71JbLpSRx1NRSN2HSQgSFjtHTSC4EEjYG1p83t+dcZOHl4tFZhbcRuNO6kr6E111gqY6mpgqGTdI9Heoz0w3ndo+1vlGlF+IeBZ7xf9aLzV4p6uTRYhX2O22qW40809bVVJY5zi9jum2MdNoHM4El2yAgs+ycb7ffLxJam49kFFXy22W626CupY4XXSBnKD0Nydnbez2Jem4c4JAGyK+sPyg77fvk13PNrlYr1ZK+KhdMblbqOlnjGzIOvTwyVHtsiDQXNkLSfcHKbVuF3mXinwyvDKPdus9muNJXzdVn3mWVtKI263t2zE/u0EDXfWxuA2XAc4p/k0ZRwyqsXdHcaSz1dvt1dHX07obm57pOmWDnDo9hzd9QN0Sgnd0492+w19faW2LI8huFpt1Nca6S2UURa2CVjnCQkytG/Ydtg9o/RDgDqR4/xVseUZLb7NbTUVD6+xR5DT1YYBA+lkeGM7k8wcdg65da9++yieM4HfLfnef3CooenR3TH7VQ0knWjPVmhjqWyt0HbGjIzuQAd9idFQ/AsCzbhnPw/vEeLPvc9LhcOOXG3wV9PFLR1DJGyBxc94Y9nzmksLiNAgFBPo/lBWKsx+03G32q9XWtutTWU9HZqKmY+sl8LM6GeTXUDGxtc0e054Htt950pVgWf23iHa6qroIaujmo6p9FW0Fwh6NRSTtAJjkbsjfK5rgQSCHAglea7b8n/JaKw4Peb7glsy6stj7xT3PFaqpgeOnVV0k8U8Ekh6Zc3t84tJa/XY7C9DcJ8apcbxXlp8LoMDlqZnzTWm3uie0HfK173RANLyxrd63ry2dbQXfH8xv8gXJcY/mN/kC5IIxxQqqKg4aZbU3O1Pv1thtFXJU2qMbdWxCF5fCB8XtBb9qcL6qir+GmJVNstT7DbZrRSSU1qkGnUURhYWQkfFjSG/Ys3NfTfqbfvVro+sfgKj0Z4nXS8V03dHn39Hn5d/UmFem/U2w+svR9Y/AU/pPw2ul4rpt63Jr6PPza+pBulW3pWi/dHejPUt/pL1T8T659L2el4zl9H8/L58333XN9fL71ZKhnhc2+7J4nxlF9zn0B0/B6HifSniN9TfLvp9Ht87W/d70EzREQa/0DQ/gf67v2p6BofwP9d37VsEQa/0DQ/gf67v2p6BofwP9d37VsEQV568Y392T7mvgK3076A9YvEaHhfD+I8Py83PzdTn765da+lvspn6BofwP9d37VHPSmbfdk9Heh6L7nPoDxHpfnHivSniOXocvU30+j7W+nrf0/oqZoNf6BofwP8AXd+1PQND+B/ru/atgiDX+gaH8D/Xd+1bBEQEREBERAREQEREBERBBpGeH4h3kSeyaihpZIgfpta6VrtfHRLd/Dmb8Vtlsb3j1DkMEcdbE9xjcXRywyviljJGiWvYQ5vY67HutIeGluJJ9I3vv8LtUf216MY2HVEZ8zE2iNm7VvZapZSLE+5nbfxjfP1vUf20+5nbfxjfP1vUf21lpMHinp9zU75YIpwBLGyQDy52g6XyKlhgcXRwxxuI1tjQCun7mdt/GN8/W9R/bUP4b8KrvTQ5F605ZWXuR96qX251vuk7RTUJ5ejBJojcjfa5j38x3KaTA4p6fdLQnaLE+5nbfxjfP1vUf20+5nbfxjfP1vUf200mDxT0+66mWuqqqoqKlmqJ5GxQQsMkkjzprWgbJJ+AC6fuZ238Y3z9b1H9td9Hw7tNLPHLI+vrzE4PZHXV808YcCCDyOcWkggEEg6I2NKaTBjznp901O3h1RS27AMbpZ43RTw26nY+N405rhG3YI9xCkSIuCuucSua589ZM3m4iIsEFgPslFI9znQ7c47J5j+1Z6INf6BofwP9d37U9A0P4H+u79q2CINf6BofwP8AXd+1Q26NtUHFWyWp2U+DqJ7fPM3F+kw+MDSNz9QjnbyeWg4A77hWEqyv11skXygcWt8+KTVd+ms9VJTZI1p6dJEHDngJ+Lz3+xBPfQND+B/ru/anoGh/A/13ftWwRBr/AEDQ/gf67v2p6BofwP8AXd+1bBEHwDQAHkvqIgjHFCloq/hpltNc7q+w22a0VcdTdYzp1FEYXh8wPxY0l32JwvpaKg4aYlTWy6vv1thtFJHTXWQ7dWxCFgZMT8XtAd9qcUKqioOGmW1NztT79bYbRVyVNqjG3VsQheXwgfF7QW/anC+qoq/hpiVTbLU+w22a0UklNapBp1FEYWFkJHxY0hv2IJOqu9FYv+6f9JesNV66+p3h/V/v0PAeN5vFfN1z9X2PneXu96tFVt6Vov3R3oz1Lf6S9U/E+ufS9npeM5fR/Py+fN991zfXy+9BZKIiAiIgIiIK79E/+0L6T9fv/hbw3qH1/wD5vm9JdPq/+DzdP6uf6KsRVT6Uwn91P6O9D1v3RvUzxHpfnPhfRfjuXocvU11Ot7W+nvX0/oq1kBERAREQEREBERAREQEREBERAREQEREBVT8n2lwmlpM9GE1lbWRSZdcZLua0EGO5Es8Qxm2t9gezrz9/cq1lX3B661t2p8uNdhbMKNPkdbTwsZF0/ScTS3lrj7LdmXZ7998vmUFgoiICIiAiIgIiICIiAiIgKGXT12+6tZPA+C9Q/R8/pHn14jxWx0uX38ut7UzVZX61WSX5QOLXCfK5qS/Q2eqjpsba49OriLhzzkfFh7fags1ERAREQEREGlzX036m371a6PrH4Co9GeJ10vFdN3R59/R5+Xf1JhXpv1NsPrL0fWPwFP6T8NrpeK6betya+jz82vqWFxQpaKv4aZbTXO6vsNtmtFXHU3WM6dRRGF4fMD8WNJd9icL6WioOGmJU1sur79bYbRSR011kO3VsQhYGTE/F7QHfagk6hnhc2+7J4nxlF9zn0B0/B6HifSniN9TfLvp9Ht87W/d71M1V3orF/wB0/wCkvWGq9dfU7w/q/wB+h4DxvN4r5uufq+x87y93vQWiiIgIiICIiCFcQsazK+3PGqjFMvixmloqwSXSkltzKoXGnJbzRhzjuIgB+nAebh5Ad+yiyDL/ALot2ttdi1PFhsNIJ6LIIbgHzTSgM5oXUvLzA7dIQ4EjTB73AKYogrrHuO+M3XBJ8tvDa/B7XT1YoqhuXUxt0sMpLQA4POgCXgc29b337FT+kq4LhSQ1VLNHU007GyxTQvD2SMcNtc1w7EEEEELDyHGbRl1qltl9tVFebbLrqUdwp2Twv15bY8EH+ZRe98GMavmaYvlL2VlJc8cjMFCyiq3wwdLWum+IHlc0dj5e4e4aQTpFXNvxTPscr87uUeYRZO24Rvmx6x3KiZT09un08tjfNHuSSMkxj4gNdobK1lfxVy/AeG1hvGX4FcbxkVVVmluFrwuM1wpWbk5ZxzEbZytYTs9uf6igtlFFjxPxVvEH1GdfKVuWGmFY21OJErojze03Y07s0kgHYA2RpShrg9oLSHA+8FB9REQEREBERAREQEREBERAUH4TW7I6G03qbI8mpcoNdeKqrt9RRhvTp6JxHSpwQBvk04b7+fmVi5/x44c8P4rzS5Dm9jttxt9M+aothucLa9o6fUAbAXc5e5pBa0DbuZut7C58A8axfE+DmJ0OFeJOKvom1tudWE9V0U5M4c7YB2eqT3HvQT9ERAREQEREBERARanKcss2D2GqvWQXSks1ppQDNW1soiiZshoBcfeSQAPMkgDuVGK/io+S8YbT4/jV1yi0ZHGKoX+3iMUNJTkNIkke5wOyHtIaBsjeu4IQT1R3M+ImM8O6WkqcmvtDZIaudtNTmsmDDNKT2Ywebj/J5Duey0NLjGbX2uzejybIKKDG7nE6ks8ePRy01dRREOaZXVBcfv2nDu0aBYCNbIWTjfB3GMfw+xY5U0ZyOjsszqijnyHlrqhkznPcZed4J5/vjwCNaB0OyBBxFrqrirV4dHiN7ioaah8U7J5oALc+Q8pbCx29udou35EFutaIK0/DDAskfURZNxOjx65Z5TPmhoq6wwyRxUlHIG/eG9TufaDiSd+YGzrZs9EBERAREQEREEY4oVVFQcNMtqbnan362w2irkqbVGNurYhC8vhA+L2gt+1OF9VRV/DTEqm2Wp9hts1opJKa1SDTqKIwsLISPixpDfsWbmvpv1Nv3q10fWPwFR6M8TrpeK6bujz7+jz8u/qTCvTfqbYfWXo+sfgKf0n4bXS8V029bk19Hn5tfUg3Srb0rRfujvRnqW/0l6p+J9c+l7PS8Zy+j+fl8+b77rm+vl96slQzwubfdk8T4yi+5z6A6fg9DxPpTxG+pvl30+j2+drfu96CZoiICIiAiIgIiICIiAiIgxZLVRS3CKvfR0766JpZHUuiaZWNPmA7WwD8FX1PwGsWM4llFmwmqrsIqb9P4ya5W6ofLLFUEgmRglLg3eiC0aB5neRO1ZaIK1uNBxQx6HAqCx19kyamp3R0+T3K+MfBV1Ee4wZ6dkXsB+uq4tcdfNHfuVsLfxKrZeIeRY9cMRvFqtFqpBWRZNO1poKtoawva1wOw8FzvZ0diNx7dgp0iCF4JxmwziViEWUY/fqepsUs/hm1k4dTN6vb73qUNPNsga139ymYIIBB2CovnPC/FeJWNT4/ktkprpZ55RPJSvBYDIPp7YQQ7ue4O1rLhwpFRnmMZFQ5PfrPRWSlNH6u0VVy22riDXBvVi1suaXN07fkwDXmUE8RV/QzcSLGc4rLnDZcnpY+efGbba+akq5h98LaeollcYwf3poeO3d5I8gtddOPtpwfDMZvfEC3V2FVV7qvAi3TROrH085c4Na90AcNO5Rpw7e21BaKLw58oT5eNz4HfKr9Wm0slywu3UFLT3i3vgjbKKiQGY1FNIDzEiKWFvK8hpLXjTdh69XWDi/jOY4JR5ZjdwjvtsrnNipfDOAdJM46ETgdcjgT7Qdot0SVlTTNUxTTtkTVFB33LMpDzNmsdOD/ANGaeaXl/wDq6jd/mhfPHZn/AByw/oM398urw08Uf34LbmnKKDeOzP8Ajlh/QZv75PHZn/HLD+gzf3yeGnij37Lbm/Or/Kk8N/VzjHZsvgi5abI6ARzP186pp9McT/4ToB9hVkf5NT5MD7eyPi9kVO+KeRkkNgpngtLWOBZJVEeftNLmNB7aL3aO2Feg+OnAyp+UNYrTacrq7aKW217LhGaGmlie8hrmmJzjIT03B3tBuj2GiNKw6AZXaqGmoqOXHaWjpo2wwwQ2+ZrI2NADWtAm0AAAAPqTw08Ue/YtzWAig3jsz/jlh/QZv75PHZn/AByw/oM398nhp4o9+xbmnKKEC+ZVbmmeqitlzgZ7UkFFFJDMW+/kLnuBdregdA9hsb2tdFx2x7JMZya5YOX57X2FzYqi1WftM6V3lGC/lbvzJ7nXK4eY0tWJg1YcXnXHJLLIWPcbhS2i31NdW1EdLR0sTp555nBrI42glznE+QABJP1KB1cXETLWYNcqCro8FgBbVZFZq2mZX1LvmHw0crXhgH740yDv3aR5ELOs/CDH7NnOSZY011Zc7/C2nq466sknpmwgAdNkLjyNaSCda+k7WgdLQiP33j5T1nD235Xw6sNdxRgr680EMNkcIw1zS8PkkdJrkYDGRzEaPM33EFSCW1Z5LxWgr2Xy1wcP46HlfaBRl1bNUnftOlJ01rfZI157cCPIqWWy10VloYaK30kFBRwt5YqemibHGwfBrWgAD+RZSCvcD4GYvgVhvNnjZWX6jvFcbjWjIag1/Wm9nRIk2PZ5Ga7fQae5G1YEUTIY2RxsbHGwBrWNGg0DyAC5IgIiICIiAiIgIiICIiCMcUKWir+GmW01zur7DbZrRVx1N1jOnUURheHzA/FjSXfYnC+loqDhpiVNbLq+/W2G0UkdNdZDt1bEIWBkxPxe0B32pxQqqKg4aZbU3O1Pv1thtFXJU2qMbdWxCF5fCB8XtBb9qcL6qir+GmJVNstT7DbZrRSSU1qkGnUURhYWQkfFjSG/Ygk6q70Vi/7p/wBJesNV66+p3h/V/v0PAeN5vFfN1z9X2PneXu96tFVt6Vov3R3oz1Lf6S9U/E+ufS9npeM5fR/Py+fN991zfXy+9BZKIiAiIgIiICIiAiIgIiICIiAiIgIiIC+OaHtLXAOafMEbX1EH5cXb/J68YOMPETJcrvFLZMIivl1qrm+irbl4uWATTOk5AYGOa8t5tbJbvW+3kvQHA75HdZ8mG822tfnVVfobpXMhmtUdL4elbIIpHNl7vcXPaGuaDodnnsvY6h/ET99xb/XDP9hOurJv1Y+PylY2s1FVXyprxcbB8n7NbhaK+otlygow6CrpJXRSxO6jBtrmkEefuKr/AIuUWSYbesCwXHr1eblLldXWVFwrrjkU1HPO6np2OEMVQI5fDNeS5/JCxvzCG8oJW+Zsj0qi8p5DZuJGK47ZLRkGQV1tobnm9rpaF9vyCasroaSVrhPBJVmGJz2lw23mBIDtEnQKwuImXZLwryrLMIsOVXWptNXFY3NutzqzWVNidW1zqabU0vM47jAe3nJ5SdhY5w9crBu98t9hghmuVbBQxTTx0sb6iQMD5ZHBkcbd+bnOIAA7kleY+IlfeeE9fxHxW0ZXf7jQv4dV+QQzXO5SVNXbquJxia+Odx52B4cSBvs6Iluu6+cWeHDI+EeBVVxyXJbnX3DIsffWVdReqhvtvnja98bWvDYvnkjkA5SGuHtAFM4eq0XmXiG3LbnxeoeG2O1NxltNoxyK5hkmV1Fsq6x753xGR9U2GaWYMDGjlJA2/bi7sB0TWXiA7KeD+I5bldwopat988dJZLpIJKuljZG+nZLM1kfNI1pDTIGNd2cRylxKucPUKw+ElHBR8N8fFPBHAJaRkrxGwN53uG3OOvMk9yfeuVqt7LRbKShjlnnjpoWQtlqpnTSvDWgAvkcS57jru4kknZK7OFn+bjG/+wRf/aFcX9GfWPlK+SUoiLzkEREBERAREQEREBERAREQEREGlzX036m371a6PrH4Co9GeJ10vFdN3R59/R5+Xf1JhXpv1NsPrL0fWPwFP6T8NrpeK6betya+jz82vqWFxQpaKv4aZbTXO6vsNtmtFXHU3WM6dRRGF4fMD8WNJd9icL6WioOGmJU1sur79bYbRSR011kO3VsQhYGTE/F7QHfagk6hnhc2+7J4nxlF9zn0B0/B6HifSniN9TfLvp9Ht87W/d71M1V3orF/3T/pL1hqvXX1O8P6v9+h4DxvN4r5uufq+x87y93vQWiiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgKH8RP33Fv9cM/wBhOpgtHl1imvlvpzSvY2to6hlXTiUkMc5uwWOI7gOa5zd99bB0daPRk9UU4kTKxtaDL8RtOeY3X2C+0njrTXMEdRT9R8fO3YOuZhDh3A8iFi51w8x3iVZ2WvJLZHc6SOZtREHPdHJDK35skcjCHscNn2mkHufiu5+Q10R5ZcYvTZB5hkUbwP5HNkIK+es1V+TV8/Rm/wBtd+imd3WC0q2z35OVpvOHY7jFgpIaOz0uSUt5uEVXWVDpKiNm+rqUl0hkI5QCXDy+cNKV2XgZgtgxq9WGlx6B9svR3cmVcslTJWHWgZJZXOe7Xu272fdpb71mqvyavn6M3+2nrNVfk1fP0Zv9tTQzy6wtpR2z8BcFsNiyC0UljPhL/TGjub6isqJ56mAsLOmZ5JHSBoa5wADhrZ1pb/IsBsGW4g/F7vbY66xPijhNJI9w01hBZpwIcHNLWkOB2CAd7Wnt3Fu23bKbvjdJa7vPfLTHDLXUTaYc8DZQXRl3ta9oAkaW79Zqr8mr5+jN/tq6GeXWC0ozd/k/4JfbTZ7fXWaWeO0dQUNT6Rqm1cIe4ue3xIkExDiTsF5B+wLdW/hhjFqnxqaktbYJMbhmgtXJLJqmZK0NkGubTuYNHd2z7x3KzPWaq/Jq+fozf7aes1V+TV8/Rm/200M8usFpb1dPCz/Nxjf/AGCL/wC0LVNu12uQMFDj9wp6h/stmr2Mjhi/6zvb2QPPTQSda7b2pdj1mjx6xW+1wvdJFR07IGvf853K0DZ+s62tOPanDzJnXMx7X7myGwREXnMRERAREQEREBERAREQEREBERBGOKFVRUHDTLam52p9+tsNoq5Km1Rjbq2IQvL4QPi9oLftThfVUVfw0xKptlqfYbbNaKSSmtUg06iiMLCyEj4saQ37Fm5r6b9Tb96tdH1j8BUejPE66Xium7o8+/o8/Lv6kwr036m2H1l6PrH4Cn9J+G10vFdNvW5NfR5+bX1IN0q29K0X7o70Z6lv9JeqfifXPpez0vGcvo/n5fPm++65vr5ferJUM8Lm33ZPE+Movuc+gOn4PQ8T6U8Rvqb5d9Po9vna37vegmaIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiCusVu3ieNGc0HqD6G8NTULvW/ocvprmjJ6fP0hzdH5uud+t+TfJWKoXYLXm1PxOyuuu14oqrCKiCkbZLbEwCellawiodIemCQ52iNvf/I1TRAREQEREBERAREQEREBERAREQEREBERAREQRviXQUt14cZVRV1zlstDU2mrhnuUG+pSRuhcHTN132wEuH1hYXB2rstVwtxZmPZAzKbRS26CjhvDZA81QijEZe8/wyWnmB7g7B7qYEbGj5Kuq7EMkxPIMQpsAjx6y4RBUVBvlnfSGJzmSbf1YHM7B4fzezoA85JJ12CxlVc1BidP8p+C4y5JPHms+JGihx551BJRirMhqG+zoyB4LdB2+UE8uu6nOIZrYc/swu2N3ejvdtMj4RVUUokj52HTm7HvB/wDwfIhaPitR3S343X5NieM2vIs7tdJILSy4NDXEOLTJG2T5zeZrfmggOIaCR5gJuixbVU1Fba6OorKN1vq5YWSTUb5GyOgeWgujLmktcWnY2Do67LKQEREBERAREQEREBERAREQEREBERAREQERQm88acJsuHXvKX5HR11js0op6+qtbjW9CUlgEZbDzHm3IztrtzbOh3QTZFXNy4t1sjsCnx3DrxktoynpyyXGANhZa4HdM9Soa/TgeWQnl1v2HDzCz7e/iFVZ1kdPXxWGhwxtMGWispXyvuLpy1hL5GOHTDGkyDXmS1vuKCbrXVuRWm23Kit1Xc6OluFaS2lpJ6hjJZyASQxhO3dgT2B8iq0j4HXfJuFcmI57xAvWSVs9X4qe82sNtUz27B6IbFsCPsQR7wfdoKXycKMTqb9Yb5V2Smr73YqZtJbrjVt6s9OwD6Lj9Lz9rz7nv3QQrhpJw9uvHnifdcav1bcswLaOhv8AQSRObT0Zha9kYjJiaC46dzae/wAvcriXFrGs3ytDdnZ0NbPxXJAREQEREBERAREQEREBERAREQEREBERAREQEREFZ5Jh1/ws2x/Cu043boqy+iuyGkq4XQ+Mil9maVr2eUg7O2Qd8o/0XUxxi/yg2J8OuMWPYTaI4b5TNuIpckuhkLIbewkxlkbvpyMcQ957tDWFg25xMXorihS5dXYBe6fA623W7LpYOS31d1YX08LyQC8gA9w3mLdtc3m5eZrhsH8aOI3yTOJ3DKokZcsf9IQtP/KLRUR1jT9fLGS9v/1NCyimatkXHtjiJ8sThz8nC5cQnYNklXxHyTJaz0lBQNmFRaLZUmKMFxqAQXsdzc3JE5+ukI9xa5lfnyPeMF345cD7flN/dA68TVtZHUeGYGRN1O50bGN9zWRuYwbLnEM25ziSV+eEHyOhxG4H2XOsNuEVBkYhfFdMVvEzYJJJYnGN0lM9/L8/lD+R3bbnAO7Bo9c/5N2rfiPAO5WvJP8AiGtgyCq6dPcz4d7o+lB7TQ/RLefnbsdttI9xWeixOGei2l7CRaj1wsP47t36XH+1PXCw/ju3fpcf7U0WJwz0LS26LUeuFh/Hdu/S4/2r6zLLHI7lZebe93wbVMJ/9U0WJwz0LS2yLiyRsrGvY4PY4bDmnYIXJakERYFiyC15Ra4rlZrlSXe3TFzY6yhnbNC8tcWOAe0kHTmuadHsQR5hBnoi4ve1g25waNgbJ13J0B/Og5IovBxNxaszK5YlS3ulq8nttKayrtNO/nnhi9ju5o8j98Z2Pf2h27qHUHHKvzbhbV5dgOC3nIaxlV4Wls92LbTJVD2dytfLsCPTt8x8+Vw8wgtlFALiziTcclxCrt0litGOdESX+3VrZJq0SEDccMjPvehsjZ97e2wey38Laz0tmc16y+8321ZFG+nZaJ3Mjp7fC4OBbDyjmDtOI5t77D4bQSDMc+xvh7Zjd8mvtvsNt6nRFVX1LYmOk0TyNLj7TtNd7I7+ye3YrU3Hi7YLbxTtPD54r5chuVI6ujENHI6njhAkIdJNrlbsxPAG970PeFj45wKwPGMFtmHU+NUdZjlunNVTUN0aa5rJyXOMv34u9rb3nfu5jrSnjWhjQ1oDWgaAHkEFb0WZ57k9tzeGgwpmM3S2vfBYqq/1bZaW5vBeOo5kJ5449tafiQ8EeRC6qzDOImWYpibLnmsGJ5BRzie9erVIJqauAJ1Ex0452N8u+t+Y+BVnIghUXCOwxcVJuIRdXyZDJR+BaH1khp4otAENh3ygnlbskHuNrb4hgeOYBQzUeNWO32KmnlM80dBTtiEsh83v5R7TvrOz2C3yICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAtXkuR0eKWee5VzndKMabHGNySvPzWMHbbif5B7yQAStoqR41XZ9dl1Da97p6CmFUW7PeWQvYD/KGMcP/EK78hybxWPGHOzbPoqNZPlN1zWWR1zndFRO+bbIHkQMHwdrRkPxLu3waFo22uiYNNpIGj4CJo//AAslF9Gw6KcGmKMOLQxvLH9HUn8Vh/owno6k/isP9GFkKv6zjXZqKpne63XeSy09SaSa/wAdKDQxyB/I7bubmLQ/2S8NLQQe/ZWvFjD/AOpsl5Tj0dSfxWH+jCejqT+Kw/0YUCuvHGz2iovQltN6lorLV+DuVwipmGnpTph53Hn25ungnlBIHcgDW+7OOLFJj1VcbTb7fdbzdaWiNVUG1UzZmUTXNJjdKXOA76JDRzOIG9aWucooiJnO2f36exrTf0dSfxWH+jCG20hGjSwEfDpj9i0HC+81mRcNsWutxm8RX1tspqiol5Wt55HRtc46aABsk9gAFJ1tprz6YqjzLy52Wqq8YqfEWSrktUvNzOjh7wyfHniPsu38dB3c6IPdXZjfFS13HFrndrrLHaTZ6d1RcuofYiiYwudMD5mPTXH4jRB8lSC2WJ3EWjMbPO5rXwVUwt1TG4bEkU5DA0j3jnMZ+w/FeT/kcioynCmuI/3jXE7+TKJvql41+V18vm68YfGYpg76iyYUSYp6ruyqubfI83vjiP8AA83D53nyj0V/k3OJOVZFwnsuNQYjBHh9hlraOoyR1za6SWqdKakRtpuUOaA2oaC7ZB7fWBdebfI04M59JJNcsCtlPUvJcZ7WHULuY/SPRLQ4+/2gfrXb8nj5K+JfJnfkbsVrrxVtvhgNQy6zxSiPo9XkDOSNhH787fMT5Dy77+fDY27BOId/wjJrLl2cR01xuFTu33XF6c0s1BAC0hoLt7ceU7J/hH6lnVnAfF7/AAYK7JmVmVXTDeV9sut0q3mo6w6f3+QsLRJITEw7cD3G/NWKiDXU2O2mivFZdqe2UcF1rA1tTXRU7GzzhoDWh7wOZ2g1oGz2DR8FsURAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBUPxgoX0nEE1BaelW0ERY7XYuje8PG/qDo/51fCivETCW5pZmMhcyG50jjNSTSb5ebRBY4jvyuB0fPR0dEtAXp/47KKcmyiK69k6pXk8/VlZBb6Seqqp46algY6WWaZ4YyNgG3Oc49gAASSVGBxcwUkAZpjxJ9wusH9tSyoikpauahrYHUtbF2mpJtc7fd/IWn3OGwfcSunwNMf/AOPF+YF9BnOqtNExbr9WGxG4+LODyyNYzMsfe9xDWtbdICST5ADnVU4nwdGN1Zsl04ZWfJYBXyPZksslOC6mklL+aVrgZDIxriNAEHlHtDzV9CipwdiCLf8AoBdy01YGkmJxJ2cu9xTl34f36qwXi9bo6DmrL9X1E1uj60Y67HU0LGnfNpu3McPaI8kqMey3E8jyyS2Y6MgoMlpYHCWOuigfRzspxC5sgefaYeUOBbvXcaVxopOTU7YmYn4c+XORW2B5bj+AYLjeOZHkVms98ttspaesoaq5QNkhkETdtI5//PyI0Qt87i1gzQ0nM8eAcNgm6QdxvXb2/qKk76WGRxc+GNzj5ktBK4+Cp/wEX5gWymiumIppmLRy+6MayZDa8lozV2i5Ud1pQ8xmeinZMwOGiW8zSRvuO31rdWOhfdcqx6jjBL33KCY6Hk2J4md/5Rn+cLXPfBQxt3yxNc4Na1o7ucewAA8yT2AHcq4+FOAT2Z7r5dYulcJozFBTk94Ijonm/wCu4gb+AAHxXLluU05LgTVVP+0xaPX7M6d6yURF84BERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQafIsRs+WQNiu1BFVhm+R522Rnx5XtIc37CFEZeBGPPcTHV3WBp+iysLgPtcCf8AzVjIuvCyvHwYzcOuYj1W8q1+4LYvxjeP0pv9hPuC2L8Y3j9Kb/YVlIt34jlf8kl5Vr9wWxfjG8fpTf7CfcFsX4xvH6U3+wrKRPxHK/5JLyrX7gti/GN4/Sm/2FybwHsLT7Vfd3j4GrA/9GhWQifiOV/ySXlGsb4c49ilR4mgt7fGaI8XUPdNMAfMB7yS0H4N0PqUlRFxYmJXi1Z2JMzPNNoiItYIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiIP/2Q==",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# from IPython.display import Image, display\n",
     "\n",
@@ -249,32 +238,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'analyst': {'messages': [AIMessage(content='เพื่อให้การวิเคราะห์จำนวนประชากรและข้อมูลเกี่ยวกับร้านกาแฟใกล้มาบุญครองได้อย่างมีประสิทธิภาพ ฉันจะทำการค้นหาร้านกาแฟในบริเวณนั้นก่อน จากนั้นจะรวบรวมข้อมูลเกี่ยวกับประชากรในพื้นที่นั้น ๆ\\n\\nให้ฉันเริ่มค้นหาร้านกาแฟใกล้มาบุญครองก่อนนะ', response_metadata={'token_usage': {'completion_tokens': 84, 'prompt_tokens': 234, 'total_tokens': 318}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, name='analyst', id='run-b7a8a633-48aa-4d80-9265-5ab306ff8ad8-0')], 'sender': 'analyst'}}\n",
-      "----\n",
-      "{'data collector': {'messages': [AIMessage(content='กำลังค้นหาร้านกาแฟใกล้มาบุญครอง...', response_metadata={'token_usage': {'completion_tokens': 16, 'prompt_tokens': 295, 'total_tokens': 311}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, name='data collector', id='run-4ea518ec-c5fc-4ed6-8a21-dbaad40f4c87-0')], 'sender': 'data collector'}}\n",
-      "----\n",
-      "{'reporter': {'messages': [AIMessage(content='ฉันได้ค้นหาร้านกาแฟใกล้มาบุญครองแล้ว ต่อไปฉันจะรวบรวมข้อมูลเกี่ยวกับจำนวนประชากรในพื้นที่นั้น ๆ เพื่อทำการวิเคราะห์ให้ครบถ้วน\\n\\nให้ฉันทำการค้นหาข้อมูลประชากรในบริเวณมาบุญครองต่อไปนะ', response_metadata={'token_usage': {'completion_tokens': 71, 'prompt_tokens': 365, 'total_tokens': 436}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, name='reporter', id='run-0c7f49b0-45a1-4754-b04d-e1e3dccee372-0')], 'sender': 'reporter'}}\n",
-      "----\n",
-      "{'data collector': {'messages': [AIMessage(content='กำลังค้นหาข้อมูลประชากรในบริเวณมาบุญครอง...', response_metadata={'token_usage': {'completion_tokens': 20, 'prompt_tokens': 390, 'total_tokens': 410}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, name='data collector', id='run-6f6f843a-63ab-4081-b0ea-64faab06d1a6-0')], 'sender': 'data collector'}}\n",
-      "----\n",
-      "{'reporter': {'messages': [AIMessage(content='ฉันไม่สามารถค้นหาข้อมูลประชากรได้ในขณะนี้ แต่ฉันได้ข้อมูลเกี่ยวกับร้านกาแฟใกล้มาบุญครองแล้ว ดังนี้:\\n\\n1. **ร้านกาแฟ A** - ตั้งอยู่ที่ถนนพระราม 1\\n2. **ร้านกาแฟ B** - ตั้งอยู่ที่ถนนพญาไท\\n3. **ร้านกาแฟ C** - ตั้งอยู่ที่ถนนบรรทัดทอง\\n\\nต่อไป ฉันจะทำการวิเคราะห์ข้อมูลเกี่ยวกับร้านกาแฟเหล่านี้และประชากรในพื้นที่มาบุญครองเพื่อให้ได้ข้อมูลที่มีประโยชน์มากขึ้น\\n\\nหากมีข้อมูลเพิ่มเติมเกี่ยวกับประชากรในพื้นที่ ฉันจะต้องการให้ผู้ช่วยคนอื่นช่วยในการค้นหาข้อมูลนี้ต่อไป', response_metadata={'token_usage': {'completion_tokens': 172, 'prompt_tokens': 464, 'total_tokens': 636}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, name='reporter', id='run-e1f2d87a-8eff-4b43-bb64-36513b08ac54-0')], 'sender': 'reporter'}}\n",
-      "----\n",
-      "{'data collector': {'messages': [AIMessage(content='ฉันจะส่งข้อมูลเกี่ยวกับร้านกาแฟที่ค้นพบไปยังผู้ช่วยคนอื่นเพื่อให้พวกเขาช่วยในการค้นหาข้อมูลประชากรในพื้นที่มาบุญครองต่อไป\\n\\nข้อมูลร้านกาแฟใกล้มาบุญครอง:\\n1. **ร้านกาแฟ A** - ถนนพระราม 1\\n2. **ร้านกาแฟ B** - ถนนพญาไท\\n3. **ร้านกาแฟ C** - ถนนบรรทัดทอง\\n\\nโปรดรอขณะที่ฉันส่งข้อมูลนี้ไปยังผู้ช่วยคนอื่นเพื่อค้นหาข้อมูลประชากรในพื้นที่ค่ะ', response_metadata={'token_usage': {'completion_tokens': 134, 'prompt_tokens': 590, 'total_tokens': 724}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, name='data collector', id='run-d2a72444-eaaa-4a87-b2b2-112a4807a8cc-0')], 'sender': 'data collector'}}\n",
-      "----\n",
-      "{'reporter': {'messages': [AIMessage(content='กำลังส่งข้อมูลร้านกาแฟให้ผู้ช่วยคนอื่นเพื่อค้นหาข้อมูลประชากรในพื้นที่มาบุญครอง...', response_metadata={'token_usage': {'completion_tokens': 29, 'prompt_tokens': 778, 'total_tokens': 807}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_507c9469a1', 'finish_reason': 'stop', 'logprobs': None}, name='reporter', id='run-4bf2b299-5761-4b9a-915e-2fdf0d06d736-0')], 'sender': 'reporter'}}\n",
-      "----\n",
-      "{'data collector': {'messages': [AIMessage(content='FINAL ANSWER: ฉันได้ค้นหาร้านกาแฟใกล้มาบุญครองและพบข้อมูลดังนี้:\\n\\n1. **ร้านกาแฟ A** - ถนนพระราม 1\\n2. **ร้านกาแฟ B** - ถนนพญาไท\\n3. **ร้านกาแฟ C** - ถนนบรรทัดทอง\\n\\nอย่างไรก็ตาม ฉันไม่สามารถค้นหาข้อมูลประชากรในพื้นที่มาบุญครองได้ในขณะนี้ หากต้องการข้อมูลเพิ่มเติมเกี่ยวกับประชากรในพื้นที่ กรุณาให้ผู้ช่วยคนอื่นช่วยค้นหาข้อมูลนี้ต่อไปค่ะ', response_metadata={'token_usage': {'completion_tokens': 129, 'prompt_tokens': 761, 'total_tokens': 890}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, name='data collector', id='run-c5d43e75-3c64-45e7-bc56-25366d4a8ba7-0')], 'sender': 'data collector'}}\n",
-      "----\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# graph = workflow.compile()\n",
     "\n",
@@ -296,29 +262,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[AIMessage(content='FINAL ANSWER: ฉันได้ค้นหาร้านกาแฟใกล้มาบุญครองและพบข้อมูลดังนี้:\\n\\n1. **ร้านกาแฟ A** - ถนนพระราม 1\\n2. **ร้านกาแฟ B** - ถนนพญาไท\\n3. **ร้านกาแฟ C** - ถนนบรรทัดทอง\\n\\nอย่างไรก็ตาม ฉันไม่สามารถค้นหาข้อมูลประชากรในพื้นที่มาบุญครองได้ในขณะนี้ หากต้องการข้อมูลเพิ่มเติมเกี่ยวกับประชากรในพื้นที่ กรุณาให้ผู้ช่วยคนอื่นช่วยค้นหาข้อมูลนี้ต่อไปค่ะ', response_metadata={'token_usage': {'completion_tokens': 129, 'prompt_tokens': 761, 'total_tokens': 890}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, name='data collector', id='run-c5d43e75-3c64-45e7-bc56-25366d4a8ba7-0')]"
+       "'FINAL ANSWER\\n\\nรายงานการวิเคราะห์ร้านกาแฟใกล้มาบุญครอง:\\n\\n### รายชื่อร้านกาแฟ\\n1. **ร้านกาแฟ A** - เมนูหลากหลาย บรรยากาศน่านั่ง\\n2. **ร้านกาแฟ B** - กาแฟสด ขนมเค้กอร่อย\\n3. **ร้านกาแฟ C** - Wi-Fi ฟรี พื้นที่นั่งทำงาน\\n4. **ร้านกาแฟ D** - กิจกรรมดนตรีสด\\n5. **ร้านกาแฟ E** - เมนูเอกลักษณ์ การตกแต่งสวยงาม\\n\\n### การวิเคราะห์\\n- **ประเภทของร้านกาแฟ**: มีความหลากหลาย เช่น บรรยากาศน่านั่ง, เน้นกาแฟสด, และการจัดกิจกรรม\\n- **กลุ่มเป้าหมาย**: นักศึกษา, คนทำงาน, นักท่องเที่ยว\\n- **โอกาสทางการตลาด**: สร้างความแตกต่าง, ใช้โซเชียลมีเดีย, จัดกิจกรรมพิเศษ\\n\\n### ข้อเสนอแนะ\\n- สร้างเอกลักษณ์เฉพาะตัว\\n- ใช้กลยุทธ์การตลาดที่เหมาะสม\\n- จัดกิจกรรมเพื่อดึงดูดลูกค้า\\n\\nหากต้องการข้อมูลเพิ่มเติมหรือรายละเอียดเฉพาะเจาะจงเกี่ยวกับร้านกาแฟใด ๆ โปรดแจ้งให้ฉันทราบ!'"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "list(s.values())[0][\"messages\"][0].content.replace(\"FINAL ANSWER: \", \"\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def submitUserMessage(user_input: str) -> str:\n",
     "    graph = workflow.compile()\n",
@@ -343,7 +300,7 @@
     "    \n",
     "    return response\n",
     "\n",
-    "# submitUserMessage(\"ค้นหาร้านกาแฟใกล้มาบุญครอง\")"
+    "#submitUserMessage(\"วิเคราะห์การเปิดร้านกาแฟใกล้มาบุญครอง\")"
    ]
   }
  ],

chatbot_multiagent.py

@@ -234,9 +234,6 @@ graph = workflow.compile()
 #     print(s)
 #     print("----")
 
-# %%
-list(s.values())[0]["messages"][0].content.replace("FINAL ANSWER: ", "")
-
 # %%
 def submitUserMessage(user_input: str) -> str:
     graph = workflow.compile()
@@ -261,6 +258,6 @@ def submitUserMessage(user_input: str) -> str:
     
     return response
 
-# submitUserMessage("ค้นหาร้านกาแฟใกล้มาบุญครอง")
+#submitUserMessage("วิเคราะห์การเปิดร้านกาแฟใกล้มาบุญครอง")