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vVvWYTIt5QHKSiE4PqWOwQ56aeISXr5KgFpmRmp0N4oYYxySxR6XnWArrHOKUfrtRpITK2f6JFqwvXKyIZRsqlwXSK48TooC/JnQYhSzxclbo2wk+OjeU3pDYz4kFkp1dpPfViEOr02vhirkcSGGmpnU0osTwm48fF1HCkyuKHPVwcbV4N+Rg6iivrRjASlxXMgZS5FKrESUcJiUJR5KITDUkP5sEw5WNHKOivXwDEYd6yoSHH8vpoiBAMpCQ8rLCX6AzsiHLPlaATlzqK4a8Uagjb4u1AM14RyVErYRwkHnSj9oCiblCM4ch0xmnxuYnBjosSfp+5rnyGKWBmU8yMARiR3i/ewkXL4iejFLGjW761Scq1ZZxhq9HtCJp2k1CE8/AyNRSSoMnME5iUiDCOUirTxh0dYOTjqk6QgRI8hZ3TU2Xisrd+D5xSwBMdnAigBzceI9+lVPgbN03Okd0AxIELYnfJRf6zQ1EWuXfhTekleY5znnyfaVx7TsdBEunip56yR8xcjG/Ep5jzAn+mjAJmUQkEs50hgQyP6W3Q14sWtyHNOqXI1WFBROaEihFDqUJAPYktYk6ACTpwwR6huyJQtQvpkocbzlwQSitZr4pFwQhAVR99RWi49juNBQsfUh59LCnmKHkpzJMpUWYuCWV6lNqITcEB78MKHHGl7gfQpxzGKABp/BU3cENBR83wNvkuNOjwyHh/urwLTTLW6DYWdhZhRp26Mv3F7c2ykKyEy1sMNrVjAaMR1jF2Co/W5CxDLnSlejuexoKAUaadxY5XOhE1IkbyR6sKuKUrj0sDURDKCjiLgnChBTPIokFrZp8T7jCH6Inck/CedYICJeMx0IBHFy0cEdT2h8OJwHS1p0Ha9r0QMmCw5gmwjrRj194mFSCi4JAH4aByDHfAYdinOZKQGq0mDUO7wSlJeHR0YoNSxQpVcYzwLzzEGKFIJgMOupBZWU3KEQBxvr6jvY06ryaVED5Gck8cq6FBgqDQqXq38Hg73hBwwSe0qSq3raNwEjZxjnsgE2ouF909vget+LCT8ZaX3bpIQgaVXSJGU7hT/HRmBen1TaiwG45QyacyzJMC+WcmXlc1IItrzESnleR6j2MnJrcI6f4SCl6cI4YnWaUpxQ9xgmkoxY6Cw+caLLrFnQUr1gJUwnZN6dJoRULsVeq4TMOZEujCkRBxIH4Ea36Hm1AQ9Pd8ad65/OIhf1orUHAW7KZGnmiqLKMc+RcAmf1/hP9Wf+aUU+xAgyChlgnSsIoseCsdRhFwXf55GRAqNOtqjYqdGViGkqBYqWSoUZp7DXNukzIIjMwM+bZkY893qenlIRqrfpSGOsjnrISyR1DEMTPV3CocpDjVyQnms7sA4IjVqALJ7mMeMElrsMcQjqXVin75f4SBXi8jE/DcLX8YVezHSpwtZbU0pWNlx9IQpyn2t8OEJv4+rXadIqITHDURQMCTpHgoaO9+jgFcgDPtlgAjkCW2aRPhtA3tWNTY+14EYgzBGq5cQ6kJyGUndKEq+B5FyqBQAlUZOQxNJbyOVVq1YPI5BzcQpVDrMeR9Ov2KFZ5MV1lzWIgBWkWwhjRfz3+b0E8epp6jRseKYcBCPbZQJ6UM3ai7KIQYgFGhIRhsFRJdgCAUJR9qMDzdohADDO2Tw8QEqiKRhNKkibEE68o5I/29DcA7SxzoIBMO7U0E05vuJIrMYEZKNgMNf+xARwTGwMp1IiWQfpLEnMSZGgUIJyEKq7+kjdt+PenjMefgGc4YIj2Ti49EojSkGCjJVAisBH13EEWJNHP3BfTUJSkVC1Z8togQgkjvu3Mjlqnl9yAjEzGM84bglIo18UuGs0IpTcgymGl+aSsnpYk0ZSsVl6J+tD04QhcCv1K6G2rqj+p5GK/AJZY4fY7MvEYX6D5zMLRaKrDASiqkckdRS6sNUx0tSg+Ryan1hPNs9BaeiY6wmen7qn5VGkju//W/C5wNgjoBZCC/eoDOnw4QbhOqmmgyK1w61MoAy306RAkHC9KziIWXq4QfJQGNA60UHB7Au/jBsZwVc9WMPtETDeSIRHGwhCIZCzSWZ4TgStu0iDw1TQBgIlHUkMxzJkA5p3lbXpItTOCaVVAwmb95vrMGcBP7/aJELFMExxAUxgl4/JZX9xjFHR9DVrxopuycMdKXZBD6s80Pj4gTiJGSXZ0zNaCgYoNCYX8T6H42/EksJlM/M9Uc5Ex4ERWBPFEz3xfOzUPOPEzmeXAcvY5ymiB3+lAIZh31sSUmKCnWKP0pVjQgwBME0DTiSMThcxp79A9hzsIT93UX0DpZQYQvNk/MwbIHSQQetWcaUrmZMmZTDpC4L06bl0NpqgQTBcSSkpGBOAB7TbpK3jq6xDHJN0gpQqYZAhd5yHCUZqj9FPBpLV+hVpsBCSFfx4ZR4jhpt7yW1HVNSelZ1DP4GWTWrSAxFsg7sjQOryClW+Mv/RiWAcdUDSRv+oLoPKMkHUDQ8RorhE6qnikPnwpHq6OeQhn9PdDPK4Y+GrpRmCSkC7vDC6IwlIFliz4FBPPL0Lqzb1Ict+0sYrBngQg5WewvMlla0Ti7gTWfMgOFI3PZgDw71Axt2VFH+ax8yZYnOAmHe7ByOWZTFkmWt6JqUhSBCzWYwCx2ZqEKmKYVzgZObeckz3JTcyuMorEt1Xhyj/mJNgiOdY6lB3mSlLU4xBl4kCwAnw+rrIEGPxh/B+ui9qshtRr2mq0Aq/p5RQAlPj5IZtaJCGiqOPfbsfCkZ6AM8GjQ0YcS5buNM6GpBKQgTwdQQ6Cw48IHkLwUHP5s1UCxXcd8T2/HHu17Ak1v6UC40o2n2NLQvn44uA7BHKygPV+Ec7ENluIhtM3OQFcbgxkHYIxJm3kD7jBwmdeWQswz0Dtm45aFR3HRzPxbMtnDqqV1YtKwF2ZyBWk2CWWhDRKHIRnyonlFH1CWR3Us5tswxkY/waAtV8sg7oEKHAivAH0GkCXeOm9+DKKo1FiFdiNDCp0ay9OKeF4pI+vkrfKh/iP2wyh07dVAslTEwOIDOjnbkM/m/WTdgXBmDGnaLJLqvFBwQewaPotEAa6OrOltvvJvMibk5xfPbGAcQ6bPldWisorOpRIRMxkSlauOuhzbi6j/8FZv6GFOWLETrlHYMDxUxuHc/zNFRzGwvYOXCaZg/sw3TO/PoaCsg35wHMVAZqmJwoIqhYQf9/VXs2lvGoT4Jx8pi8qwmdHZkUBus4dCWYbQXGGec24FjT+hEJusbguizkPUPOAMRVcY6h04XeK0vWKLSlqVz/wXioJJjeyWxvcYJODROrQE3kvzjyGbYnNoiLZZLGBgYQHt7G/L5PAwhNCVhOtQ3xKH8M0E6DorlMgYHB9HR3oZ89m9oAOrMgzf0+yjHf9I8kWcABFHixgpw1CTrV/xVOG29sD8G1yTUo19M4783DAHTNPHok1vwg1/ej/WHJKavPAa5liZ0b90Fa2QEq+e245yT5mHV0umYPb0dedMEAyjZNkZLVVQqNTCAbNZEcy6DvOU28WzbwcGDJWzZOopnnh3E7j02YGUweUYL7CEH+7YOYNp0iYtfMwNLlrfDdhjS0cleAoXlpGlKhubDuY6BZE6YKkzd9DJONsIJGAwGnEBENd1JawYgSR9uPAYgLe/giSP+xjIIwjMAfQMD7lnO52EIQ2dL9g2A3xhgZpRKJQwMDqKjvR35XE6XTZ6IXRqzklWHFzMpYRT1lZGSsnwNsRlp7bj66ylAGUQPv0itLI/L8/vci2KskF/Edkw2m0Fv3zC+e+XN+O1dL6Bj5Wp0zZiG7s270GYP45IzFuLCs1dg4ZzJAID9/cN4euN+rNt0EDsOjODgUBkjjgEpLFiZLHKmgRYLmNKWw7wpBSyb046VCzsxuaMAA4x9+0Zx/729eOD+QTBZWL2iE7WKg62be7D6+BwufvUctLRnUas6gff1w/EkL6wjzN3UKt5uVXN/ETALjW0AWOvZayPdYZTuStAzp/oOzQDIBofPaBxTrBEjQtGWZ4PlST9jJSX9CNiHhECxXEJ/fz/a2ttQyOVhWqYmyUbdfUPsF5SYAUc6qFTK6B/wDEA+B3a4ofAjDGuSbmKC1VWOhOQx1R1KjadIkW1NNZokIJJoukgd8omE/gq6kLX+eljxirYU1YKQn5JIXyvAE9RkZQTYB9lGDUwmm8FDjzyPK77+O+zlFsw6diUObtmG7EAv3vaqE/GPl52MSe3NGBgu4p4ntuLWB7Zg474RjJh55Kd2Id/VCquQh82AKQTyGRP5nAnTNFCr2BjpGUa5ZxT5Wg2zuzI4ffVUnL5mCqa3F9DdU8L99xzCQ/f1oSnXjHmz27FjWy+QKeJ1/zgXS1Z0olaTIcMvqdoEHOtqUGS0QnpFVFLHqf1oIcoNGC2/cpQnwTcciHMIKBJ4aRR/muGQin4fUfoMBY1zepUbOwZ1fWZsiEDHexTLJQz0D6CtrRWFQh6GaSraHwAd6hvmMEdm1BwHlWoFAwNeCpDL140A2INcavAV0ieOGp2WYo/pNlGRNik6aCDEIkoQgYzowwmKEziSQMT7U6LEuR76i0D4j1I2guH395VEmJUiJVFyiGN4dYqrrrkD37jqTnSuWgMrX8DeJ57AK1+yEB/554uxZN4U7D44gOtvehw3PbwdA6IZXfNmoHVmJ6YsmYW+A/3YvX4Hyt39aDYkmnN5ZDIZ2MJA1cggN6kV05ZMRevUNuzZ1I2BPUWM7BtEh1PGS0+YhMvOnoM5k1uwZ88I/nTDHmzaUMLqpVPAVcbzWw/i7Au78NKXz3aV3SQnSFjHUQ3EOk2ZC20OQVhRlCAnCIFQwoiTOi7MKeIFUQNAkTqBZgBSvXMDBiC1ThwdVG9gRlGTSQ83T2IQTYRiqYSBQc8A5PMwDFOJlySop3+YWZkrtZ0aKpUKBgcH0d7ejnw+P0YEMIG8nhucoU77nWigBqJIrwoN/M+x8VDNAPhtPEFBayme9ycttur9E9IQBgwKRn5irMH6/L++pTOWhWKpjCu+/Cv84aFdWHTaydizeSsK/bvx6Q9dhksvfAlGSlVc/Zu78bMbHkexeTLmrloGoAaHJCbPnoGtT21BdrAfrzh5Hs4+aSGmTW5Bcz4DgwilShUH+0p4YP0B3PbXAxjMFzBr9TwMHhxEDjlkKYvubQdRqA7itS+djleftxhNmQyefvIAfvur3WihNszpaMcTz+zD4hME3vSuJcg2mbBtJ6RVC0ZLpKJVr7SpIjMGadNxPpOQDlyLDmxxqO7rRVhJRkDWEb8JDECjufqEDMBRLqF5EUB/fz/a2tpQKORh+hGAZEhWDYB30450UPaKgO0d7SgcaQNwuEVASqQ7r8vtL6DSbnPs5EYNgIv1Z6W9pFErxMpWKsSXKDmrNLy/OTGqbq+96EcP2hg8I5sx0ds3iA9+4ko8uVdi0QnHYvOjj+GMpe34/Cf+AXNnTsZDT27Cf33r93j+kI25Jx4PAzYOPrcZRlsLZh+7AjseXoeL1s7CB996JmZOaQcgUXMcSMfxikUChun+d+ehYfzo+vW4c+MgZq9dikNbu4ERGwsWTENnJoNdW/ZgRnsV73n9cqxcNBU9PaO4+qpN2PyUgzVzZ2Przm60zivi3R9ejs7JedRqjs6UTJ7ylDcY7N+vUCv+9XoDTBqJbTLajyNpmwi+s1EDAL9TMJZo0EQ4c/jF6bCHBsDvAuRgGqYOhuruH9a4P/wuwNDgIDo6jkIEcDgtvnS9g/rVdq8iympewCF1rIikDqH3T+DWIdIGpuof/tBk+CSfSXJWruAI6dxtBGQzJnbsPIB//uC3sLPWhTkrlmPrQ/fjX15/Av79g6+GYRj47lV/wXeuvg+tC5egaVInDj7/PDA0gkzXJMw540RsuuMh/NMla/Chd54L0xCwbQeGIULYr7cmtZoNKSUMQwBk4hd/Xo8f37IDy047Dnuf3QtnuIz2goVj5sxCebiKPft24JKXTsc/XLwCgoHf/noTbr2xF2umLsDBQ0MQ0wbw/k+sxKQZBTcSUKDX7gGQoYf1llQoaEz2ry1B0SjoDDA3eNJ8cRPdAcgx5O9k2udz6phF4/MC8sUxAKVSCf2Dg2hvbUW+kA+UrfwU1rj8E5/8nEokIJlh16qoVqvIZXOwTOvFwwNRXcxuOiUAHQatElEcUy+Scg4KhD2IKTbcU2/qUJBCCh4dcRf+gK8+2JLNmNixYz/+8V1fwp5qOybNmoFtD96JL/7bRXj/ey7B4EgRH/70lfjFLZux+LQzMdw3iPKWZ/DmV67B9j096FizGtsffxpvOH0eLn//RZCODTBgmRa27OnG729fh5se3Iz7nt6DjTsOYtrkFrQWskFBcu3yGSiNjOCxpw9iwZI5wGgfXvGSDjz21HY4FQOzO+fgzvt34IWtu3Dc6mk45eSZyLbZuPmuLZjbPhXlvgweeXIH1qztQEtbNmCbpuisSWTcn9UJQZ3qSJdWGwN3H1VYS6IAqW9A0mcHoszD0Hlb/240MIkINdtBuVxCLp+DlbECKTa/liGgQgO1oRVF+OFFiVdSQnxOYKTgIFlsIJyiZOn4GBO/qtKrz/frstmULttXj3uGKIEcg0AsYtefsQT6ewfwT+//JrrtLkyaPA37Hr0T3/2vN+Ptbzwf23btxxvf/TXc8ewwFp90Mp6/70EsyQ3ihh9/CNMmt2HEKqA0MIz52Qo+9k+vhGAJQxio2Iwvfv8GvOZ9P8QX/u8BXPXHjfj5Tc9jf88wcpbpDhz7DldKvOuyNZiVK6M4UETJMdGcM/CNy09Fc6EbT254BstmLMQL6wU+fsXt2LKrF6+8YCH+9fLFeHbgBbS2W8iNduAn31mPkYESLNNQ9lV8gIkioE4ZZXRLGcONGl5KQ4kmirI0IEfHY5Sb6kiyNVQzoKNtCMLpP/ecC62WJfyWi/Tn/wOiikSxqRc3AqjL8NOwZFHq8w5bKRSq3qL++K/a6qwXgfgz/MFmj6DKCJRQzSQYhoFKuYYPfPR72N7fjGlzF6N746P44Tffh9deehbWP7cdr33L57Gpz8TkWbOx4bab8J5LluIX330/5s+egj/e+hSmL1yK/k1b8N43noX2lgJYSkgGrvjva/E//3c7BocdZEyCsGtYOTuPy99xLiZ3NAcwUdNwh4iac1m88YJF2LN5O/JN7fjjnZswZVIzvv6p83DphZPx1NYnMLNjKpzeqfjMFXfhuS2HcPppc/CRTy3H9somdLUXUNrTih9+ax1KI7VQdszHCPhrIIQ7hq6r1NQ5XAzQ2LE2QwQdqqDvHUWEjZP6lyIepFHaycMqHh4RpiAP/ScdbX5DQIZot3Bzk1c0OZwL5MaZTCiZBJY5HUo5rq+isXQbGXGS7yg7TnQ8uE5h0Q+tKDL8EgwsqRh3n4TDPXy1moPLP/k93P3Xg5i+cDH2PfMgfvD19+CiC07D1m178bb3/jeGjJlo6+jArgdvxpf//WJ85t/eiGw2g2079mPb3iEYZKDdqOKc01eCWcIyLdx139O45jf3I5fLgeCA7TLsygiaMhJZy4TjSAhBKFVq2LTrIEgYsKWN41fMRJ6HQLbA3u4Stu/tRS4j8L63nIwP/vMqPH/gSTTnCmhz5uC/v/oAdu3pxwknzMJ7P7gMGw5txSSzHdsetvCT7z6JWkVqaOaQsIMSOOsI0mca4vQgnVLDevLUmDiQxYqH/HWIQxsk+Zh4WZ/GOYl0mMQhCq8jKWmWgAjlggSFN8+eBPTRHPHV5tej7Ck8DpJcGpuyhqN9/Gh5jyiB0ht1RsaobtDCiXUe0lh5dEIPl2WoUinjmWc2I1doQ3mkDENWMGfWZPT0DuJd7/8WKoVFaOuciv7nH8cPv/HPeNubzkelVgUAbNqyG8UqY7R/GEvmdaKzowWOdDnhr//DA3CkBcEMWauBbQeGEHh+ax/WvbAbGdOCZVi474kt+O7P7wyKnJNaC1gwqxnlwRHYNQu79vUBRHCcGi47fyU++/FTsbf0PFpb82iXs/Hlr96P3r4RdHblUJVl2LaDXN7E1h3dqFZtL8UiXYIhgbKClPagmyqlYUpEgmpSFLhPWqs1psbDWrEgYT9RfUHK4D00fgdJR4wZrD5Wx2dXipA2m1JGZsplqE82fmrwZLnfMfHL6gLIcdZIxgj/yVepZV+aKZkPjBLUg1jlmU9AniTBfVkZ8ElHL6msSz76jcFSor2tGVf96FN47Zu/gJHeZhSmr8Tb3/s1TOrswK6hVnTNmIyDG+7FT7/3Qbz0nLWo2bXgG/Yd7EO1VsNQ9wEsOHmy2/0QAsMjo1i3fpsrmyEl4AC2LWGQRH9/Gf/6mWvxijOWoCoJf7lvG5pyBoZGimhrzsEgwpzpbdi4eRDCEejpK3m4BgHbsXHGSQtR+LiJL371ESxtW4lidyc+fcVdGB2oYYaxGIfsAYx27MJnPn0G2joKqNlObPmDNSARoPaYZCjCEpdqiPAo1Jku9NmGfDQgU0N03TyGUO2Ehvr4KHj3MSvgYR0qqLnI8MAJUjDbbuNFIlSAHa9losMTN+CEvD+tDpCUQqvaFAEPF6eIY0YOcxJxp6pTl0iZWSf0p7jefOjxdNVJ8jXbANRsB8csmoOf/t9HYXc/iVJxFOX2lXhqH9AxeSb2rbsL3/7SW/DSc9bCdhyvcOd+VrVaA2wHlZERmKYImIGqVRujw6OQtg3HtiEdG7JmwymXQU4V+w6M4ke/ehw/v/4p9A9UUK1UUKvZAT6ikBXgWhlwGOVqTRGaAGqOjbVr5uLD71+LZw88g1Zqx+DWAjpKczFSrGBH5Xl85PKTMXdOB2zbcR8ZRZJnVgavlAcviTUyZ5pIU53i1FGk8qiLsTj3KFX6q94k4YtTM6MxuxoB16dP+qtKlHu6oAqbkTKaykl87+O/qImWDqje4ReNyPomjV16xT7W++7xPF7F/Ed7UPVvMwVrpJFUJvESkJJ+lSoVrD1uKX5x5SdR2f8MZq5ciA9e+Wns2L4Bl5y3Eq+69Gw40oZkhlSMU3M+B6dagyks9Pb1BwvQXMhjUmc77JoDu1qDrJQhq6OQtRLsagWQNiwDyAgHsjqK1iyhqZANrrZYqqI1n0MGNlryVuBRpJRgybAdG2eeuginnDYFu4t78dn/OhtzV7Rhj7MVn/z0aVi5fCoqNTsm/xXevAw9POkkK/XJUcZyrxzht6yjMEsJEnFjMtEcAc9/BEFByY6WlJkBfRd6GJiQHsjnUIkxjf4NgIAc9aINtkwCOi/fq1KdfjHp8uKcaIUQazmlfZzwdfAIqeScHGHERUzG0W1XlasVnH7acbju6s+CD+zAM3euw4IVJ+Omu1/A9394PQDhgnaUy506uR2oFJEvtGPT1r0B0CWXz+LE4+ejWh4F1yqwq0U41RJkrQJZq0FWbNhVG3aliuLQAJYv7EJzPueqRbPExk370JZrBlDG7CnNCrsxQRgCJAxce/0jePCeXVg6fSYefXIXeuxd+K8vnYXjj5+D4WIFFduGEAIZy4BpCBhEqXl7dCMz6aCqFPrbOoIxCi+jz8CU9vWcsAe0fyYwyE8NjrEflcYaReTUlHYoM0x/Gs3nu3MwNrnhEZ0FqEf0yUeg8VAvgfSluIV+YbokBaeQc3AiZ79MuYgoxVWYQxIEc4JUpkS1VsNpp67Etzrb8Ka3fwPdpWZMX3wqPvWl67Humc344n/+CyZ1tcF2bEiWWL5sLtpbTOTzbdi2fRj7DnRj1oypAIC3v/kCXHvdHRgtjSCbzwd4e8EMCAMkTDgSaMsBb3/9WUE6s/NAP7Zs7sWiqfNBooyFc6cAAAzhogn7h4r45v/ejjv/vAfHzTwBz+3cgc3lIXzx8y/H/DldsKUDCMbO/d3Yf2gQhpVDV0cb5kztQGvOAljCdhx3PsCnoSNlwk8f60gl9/SfS2KnBpHPobhMNyhh7j+FoORF4vM8MkeNFMVrUAzoJJjZEwVhsPQ8P//9UYFxowosNBZ0k3WaZy2zj0pVcJhxBBrrFM8ro57KB1VRijaviiBUCjnCt86eVcpaFn5/452oVUu4+ff/gZUzR7Dl6bsxb8Xp+N1t23Dxqy/Hvfc/BdMwIUhg1swpOH7VLBQHB1CSLbj+D/cFkNfVqxbhS1e8EwaXUCqWYdvSrQNUSrDLoygNDwD2EK742KU4fuWC4PD94ZbHUR2w0H3gIOZMz2DKpFaPIFVg3XM78S8f+zUeuWMIx81ai6d3PQ1r5n585cuvwKisYsehPhzaM4gHrt+FbF8BJy5agCmdzbjt0c34wDdvx5d/8xSe2t4LEgIZb0glIFSJVOTZq0+nKkUQawSvyTmY0AheD4ubkg8zzeUXh0Yz0NiQHCvuEzOMj//HJz8HDi2v9HK6aqWKfC4PyzSPyMVGcfNpwGmf+TWJ1WciFjQk9YiEFP74raCQJ18tMFCkQSUQsOJSQs3AiCDcdMFrishAKWxB3t+FKtLBgGGaGBoq4j3/+j+4+rf34uRzTsDH/vU16N27DXfceg9mLDoeo9yCa6+9Cb3dB7FqxXw0NxWQzxq49rrbMHXWCjxw7524+IKT0dHRCskSxx+3FMetXoAd23fjwIFulIojqFZKMGFj9bJp+Np/vg2vvfjMoGuz92AvrvjSjZjfvgqb923AP7/zFCw/ZhYO9g7i+1fdhm9/5xFMkgswrXUS/rrncZx38TR88XOvw7pdh/CeT1yDHbu7cenLT8C91+/Fnb8exqa/DiBTAV5xzmIsPmYSrrtzM669dz+2HRjEnCkFTO1ognRUXoR6Ve0EyfpYD5909mjNSFA61n8sOYy/U9HsZCiwjXKl7ML6LQuGIYLaHguADvUPhzIORJBSolgsYmhoyBsH9hiBGEeWu2ysFoYYWxS40fX3B1/ifIAEMggGiTAyUIgumVWCDpdggoTfNdG/3/AKVpxQjAm4AxStegrCsjgIiQBkM1nc+Of78Y/v/BLaZy/GyZe+Dq9ckcPbLjsev73uVnz6C9egkluImfOXYNfm9ejK9eP9774Ar3vdeXj7e76ITfuaUSkXcdxi4NqfX+EyO3nJTaVSwZNPb8KmzbtAgrB48Vwcu2oR8rksHGnDECbK1Sr++fKfYO/zebQ2dSAzZR/+96tvxU23PI6f/Ow+VPrasGbuauwb2othYz8++v5zcMkrTsDdO/px3dP78Zcrb4ZVLeNnX3sTJtda8cPPbURpMA8pBSbPqeLNH56LScta8f7/fRxP73YwvaWGf3vtMXjF2rluwZC9znVkYMefyONkQgl/xlD7WVR1yqcqdxyZNjuUwEA0HvTpBCIAjjIq84QRuL4Bd6cBy+gf6EN7exty2TxMw9AIQo3LP/4fn2MPhUZwDYBt11CpVpHL5WBZBsaroaaP5ybdKDXUv6z3s8aXRsebx7JyTzAjqTJHMRkrUmiVo6JRpKUAiAh8aB/vc1GSSECzEZgELMPAlb+4GQ8/uhELjjsFZtNU3Pz7h7F/ZBTveufL8cZLT8O259fhkQceQ8e0haDcNNzwh7tx6813Y/asyXj6yWcx/5iT8cKmA9i4YR3OPGONawSYIQyBObOm4djVx2DNqsWYPXMKTENAMsMQBnr7h/Gxz1yFpx8exfI5K/DU3qexbFkXrv3Vg7j9TzswI7sE+UweT+15GMccl8V3/vstWLJsHr59w3P42V0HMffYxZDVMvZuOYCMkHj9q4/F8hNb0TlTYnB0CL37BTas68bJp3TgmMXtuOOv+zFcMfDos/uxemEbZnY1w5FSS9ECAZcECJYeHUZT2Ah7DIW6hMwNsk5TghYFHQmMHMUBiIGjoAnjAUJyGYJdq6FULiObyyFjWhCGfp6Nf//4f3zOJcVwtV4d6cB2HJTLFeRzbtgwPgNAdfr+NL6VS+JQG7f1DSG7YSE3JOQg4Y8Dc93GboiVig+VGMHATwLCUZVbI1LALd7hp/ioq2kZeP6FXfjSV67GUBE45qTTkclk0NTagvL0WdjBAm1s40PvuhDLF3Xikbtvx67tOzBl9gpUuAvPvbAPtZqDgZ5DWLLsZKx7Zi/++MebMHVqG+bPmwFTYYXRohTp4JY7HsO/fuR72PyUg5Wz1+CxbY/Blg5G95vooLnobOrCpkPPweg8gMsvvxAfef9lePCpLfjQF2/EM92dcGQOwmDMmDcV29dvxejQCC4+dzmmzmjBglUdeMm5U1Foq+K5JwfBVMMpZ83CzY/sxGiZUaw4GB0ZxLknzgdJXXuBFHk1QfVVHuKDVxQb42NOMAA0Vms7Cbd4+AVzVaSGJmJZ0gReiWA7DkrlEnK5LCzLghBh1w+CfGUgj6TBjwT46FEVT7iiP0G4JPnNN3U/EafAuBKumUOgBKVkhpw0U+Ih1FgVFvXmAERgkFjrRjBcgtJKqYZPfPK72Lp9P2YtWYMZsxdgqG8QPb1DyO7vx18frOGhQwNY0vYM/uUtL8Hdd5yMX/3qL/jBj/+AnQcFJs1aidkLJ2NksAfbdzyPqZPmoLc/j7e9+7tYs+p3uOjCl+DEE5aitTkPh4GR4SKeXr8Vt965HhvWHcLclqVYOmUGtuzeiOnN0zCtcwqGywN4Yd86dE6X+LePn4HXXnoaDhYZn//Fs7j+9/diuO8Qpi+bg+bsXBzaPYzWydOQLVjoHyxieLSGrjbArjnI5A2c/5rF6O8bxUDfKBwJyFoVsBmGlNi+qw+jxQqaMmbQ0mW/Na1V8zkdoJNcPVJo2FRB0cYp6+L5hE+BTg33tjWzRfpt8HjBBdwA9obD6D6MXkIOTzMIHyjCcxdlUz3SEMWGO4UTbEuyyrFHCRpvVKeFoMq6MGiM7cWUNJAi1HFDJYTgYAOT8h0+Fj1jmvjNb+/A3fc8jbau6Vh16stxYMteHNi2AxBAVhK23vY4OufPxJPVPP7x327AJecsxltf/0q89a0X4bbbHsYvf30PHnx8A6pcQHPbJAyNDmLytKWYP+8k9HXvwP/+4FnYtQdAXPIGZbIoWJMxe9JCXLj6FXDsGrYd2gKDDBzo2YL9I09h7Unz8Z6PvAznn3s8ijbhZ7dtxZ8eG8Ch4Szapi1FZagPxZ49KLTPwsiQRLEswUJgpMx4bN0OLJx5PKRgSMlwyMHqUyZjsKeIjTv70dNfhiALds1Gc86EZRih5/f2E6f4Ao5CvwMOQArw78wcweqLcUyRJSTsfHgCH5Q0jMg4Ap4/rdzAOheDMsZoai0wZk10dMJHmOiweqMcFWCgwxuDoFgqQem5WCwQ0A9vo7AGAoNYeOw1YY/b9/RGEsmEIIyMlnHVVTeg5tiYt+wkDBzqx/7Nm5AvNCPTVEBxcBiyXMKhjZsx/aRVyCxYiKtv2oln+yROX9aK845fg2svOQs7d+zF3Xc/gZv+8hA2b9mDbS88D0fm0Vxoh2XlUMhMdwlYHXb/YRu7Dz2HLQeegJFz0DU5i0UrunDBy87FaS9ZhTlzp2P7oVFceftu3PnXbmze3o+mlg4UCibY6EAm34TKUA9qpRGgOQ+RzWLK4vnY+diz+NKP7sSCOR04eeV8VO0aqjUHU2e2YO4xk/HxHz6ImmPCNBhOpYRz1i5HU85CtWJ7tGxe9MURBuZU0o7wNb5mEKfsx4YYhZKwAmKcTo4Tmtn1Js+5EU0AGhNSo9ZAWLFaKr7CjKIGBIWCCvxi8/9xQgdgopEEpUgxRTg+E9OdBMQfNYprijJSqHmsP6VOiLcJCciYJjZt3IVnN2xHc0snOtqnYduGdYDjwDYIBTOP4UMHIGtlCCuHvm09mLZsDuYeOxfFfDN++odt+OVvn8eSha04+5S5eOkrX4p3vOMylIol7N51AOuf3YJNW3bi4MFBjBZt2I67oSzLQHNLMyZ3tWLRotk4dvVizJ09FRkrgxe278fGUQdX37Qdt96+Fw41AzUbg7vWwZwzH02tK8GwkGlqh109BDgVWFRA9/ZuzFx9LIp9Q9j7zPP47i/uwbFfmg32inAdnS346S3P4t51fTAtC3a5iuWzc7j0rGMgHUepjVCcXEX7je4hWImppTID6gN/OEkBeLwcFeP1/OOhC2Mc4dkiL7UXAhwDTXlIwJiUMB1dMGCawDwfgVGCCcMzxwtc9JFpSioZpTJntUakGBVSwUCeMIkQriF4dsMWDI0wZs6fj/JwCeWhQxCGCbIEWqetwUh/H5q7OpFpbkGuaxKGewbRuWwaJq+cBbtcwqFn9+PJjRU8+fxG/OTGTVh78iwUqiWsWjQdS9YcizPOPwumKZATDJMkhHB5AoulGgaHijjUN4gHHt+C7/38TmzY2o3RTA5nv/nVePLpEZRreRTyBkr9feDKEHJ5ASltsC1BwgILE2SY4KqDg+t3YmhqB2auOg6927bjkb/uwFPP7cJJq+dDGAauv3cjvnPdBkhkIKsVtJlFfPxtZ2NyW7PbBhRhu8XX9lNPLKeW5DzGJg4TbFLEQscHS0VjE6wvEgqQJng4SIZybdF7NhGR1talwl+sOQA+LIPTSDTGKRE/jy1Np1vDCIO4TKkjaWPE0TZSUFcgZDMZb1z3OZywdhU2bdkNWwLNLZNQKRYhWaKluR1tM2ajfcZciFwew/t2gojh2DWQ7WBw6wF0Tu6ECQuzls5xC3uDo6gaNjaNZrHzwW340+17ADhY+ZIl6Nu9BQc3PAlyyhCWAdHcgpapM5BraUVVAuWyDZsMTFp1HM46Yy32bR/CyIFRWMKArJUwdHA7AGDKnNno7q0BlSpqpQFIdkeTpSNhmhkUD/SjpbMdzZOnYHDTOvT0DgIg/PwvT+E71z+Pmm3BsUtoNov4zLtPw8kr56BcsxUgjpqvUorAR/wQhyhfUuYLODbcRkwTp7ujI4WHOTw71Lh/VajXlLa0GcINlFlhcslB+UWMARrL9dNEENHATHRKMZHGyU3OkdZZXTHPpEIwBbRYuUwWf336eTz4wCO49JLzkLGyGBwcAmQVZFcgbMaMWcshBXBo13bAzMC0Mji4YxuYDbRMmw8pbUjJKGSbsW/zLgBAvqWAbFseM1fMhJUzsat0CLJcA2WyeO6hx3Bw+yaUR0cxbdFsrDr7VExdvBj55hYYJFxvIQi2BIaHy3jsvp3o21uEMDIgITDavRUj3TvRMWcGsm2TUNvXC8OpojY6AMNTWWKn5kv9YPRgN45bPgNvfPdxOG71Ynzyu7fghnt3wzCzqFZGMbOL8J/vOxtnr10UTAwiKPz5A8LJx1RSg0QxHB6AehHouCf86DAm3V4sfYAo/6JamAZgkoJU02QwiI58a++wLSpN7IPrsbUwjXFbhDGk7FNRXJQoRsoQJJAxDfzwJ7/DQw8/iS9+6aP4y90v4JxT8yjkMmBZgVMrIWNmsHf/NgAOiAB7cAhD5RKmz1qE7oP7IStDYOlAOjac0VEIuwiu1TAydAD7n+vFpvuGUbNrcMoVFxMuAGYHwjRw2qsvxtqXno3uQyPY/Ne96Dv0AlgCJAyQsECG6Qp+sgeUohrKQ73o3/U8IIClp56K3gPDQK2CynA3quURFNqnQ0jG8NAB9OzdinnzZuBtpx2HN7z8RDy35SD++T//iGc298MyBIojh3DSikn43PsvwPL5U1H1iE0IBMmkFFIpGMDjmCqDu5b16LujMz4ca5FNkPb2qJN5HlFO0ATFJC8FYMXyBdPvDLhcgXz0R5nGWMjD7CiGhBPU6PFuPB5LqwclyZqxB/s1hcsX9MnP/AB33P4AfnfD/+Lqm57Dldc9gyXHzMHSZfNhWnmMDo+iYPTBqRZBJsEwLIANOJUqDuzahukz56O3dy9q5SIkAc5wEcKWGOjeg4HenahWR2Fm8miZNB2tcxcgV3ChwcP9vTjujJdg7nEn4L4/P4G+AyMg4dJFuzAFGxA1kDBcJVkh4Ng1jBzci4F9m2DbVZz4ipehrWsOdmzcBNgj6Nu9HlyrItc8GaPFEfDwdrzh3On413dcgHwuh29ceQ9+d9d2jJYBSAc5q4R3vX413vW609DRnEWlVou18sK0lOvQ5o0hvRyIg6YjUoM4l3B4Rb3x+EZ6cUfsJcIOSlTSztQ1yznAqxLR0b/IsawoN6gu3HAiFJm9r1MI5MOc+UiCCxEJCCHwmc/9EN/7vxtx+03fwPPb+3HNjVtgmRYGh4ax9vgVaGtrxfDoAKZ3zEXWyqFiF2E7BJNymNw1Gzt3PIm9tVFMm7UI/XYNpdEhFPsOYah3N7r3bwUZhLlLj8eiY09GU9d0kGnANAmZnIVsVkAIA4/e8iSGekdgZUygNgoyBcjIuMUi2wakjUq1jGqliNJwP4rDvTAsE8eefSbmrjgeTz20BWCgWqlgZHgEmXwLujpzOOekDrzmZW9He0sTfn/LOvz6luexv6cMyyQYsorT10zGB95yOtYum4uaXUXNdsJ2XkCzw3UODMew/fVOGQWq15FAkHTN2cZPbKR1MM5iICtRCfGLYAsIIdOSJi3oGgKTKLQKRJ7nJ1304Kjzlo01djkRK8saSV/ipgp1ASjR8AhyuerCIpTCQ69gtrmhLUTIWib+51vX4L+/9mu86tLTcNzxK/DOT/wJRBbe/drFeNlpiyFrNpYvnY5HH9+KgaHdaMt3oWfUBjMhl8miUGjF5EmzUSkPY6S3H1OnLkalVMS+7c+gr3cHhJnFsWdchDlLj8XB/T3Y9cQGVItFMANGxkBrZxtYAsMDFViWAa6Momf3c3DsIsxMFiADzA7sWhm1WhksHQgrg645C3HCOWejZfJ0PPXg8xjqG4UEI2uYOO3U43DZ+cvxstOXw3Yc3HjLU/jdLRuw51AJhmnAYhuyVkNGVPCp970Ji2dPQdW2k/n3WVVriOv9NexNA00Aocz/kcbu3HDgrzANB31jmliBixKZCo7iGfM5FJQCKruyR2EKoEIDWZEO1sgPJooGrMedRaiDsEpnT+FGa3ccV5ThuipP3oYjP/8EWIRzWUScQBSb0DVJuLiMaeLJpzbiy1+7BmCJl5y8HDv39+P5rYdw1gkz8cG3ngIhGMLK4Z3vuBiPPPoVHOzbi0WzpqM914GB4kHs3vkUWpq6MDjaj5bmLuQyeVRHR2EYhKHBHjCZWHHi+ci3TMO6Bx5CuWzDEAbIEBBkQFZs9O/rcbn/LQO1oVH0HtiKWnEAIpOBlFWXoMTMIt/SgckdHWjq6MCk2fPRNmUGhvpH8czTT6FaKmFSO+HU1ZNx0dkLsXbpdOzc24Of/vo+/Pmu53GwpwhhmLBMCwKEltYp6Dm4Ddls1cvZnciZ50hvPm1MN6FxH4HXRjFcTBwoObFyeCdCeUHKFChPpMAVzcePVJRdx8GSL49OpNDdhG8zVa2wWGV7QqSADZTmqYFDy4dRCuRkI8BRXTYRVagKUWciGCIT+ux5LJZsrH8rBOGXv7wFg4MOyDBQqdbQ0pRH3qziVectgWUYsKUDhx28/rUvw003P4Tf/+Ex7Dz4DGZ1LUBHrhUj5RGIvABLoH/gAAYH3esfGTmISrWIBctPweRpi7Bpw3o41RKEmYEQJrjGsNkBe0NftdooBocPYGTwIBzJmLfiZMxfeTyEabhKumSCzCyMjImaLdHb3YOtzz+KnFHDsUs6ceG5a3DacbOQJcZDT27Fv3z2Hjz+7D4MjkoIAkxTwMpmkc02QzoGCs1T4OzfhvbWZrS15DzpcE4RdeZkS+3hARDJYROZl1itziaT/RA10AaMgPY5LjQ1sZyQGmcnTnKqY7JsN1LnIp8SzJdNEgLSEwkRJPSWwdFKUnjs0csJcQBQAsFIOnF4YsChIMn1iyF1ckt4z7K+fhQRYWS0hAceegrCAJhM3Hrrw/j4v70Nr3n5MWjNC2VCTSKbMfH1r34IO3d8Ao//dTNqdhnTOuZhUusM5KxWIOeg7JRgCBMO2+h3dsPMWJg2fRH2bdsGp1gCTAE4FQwN7MDIUDdsp+pxIxiBPHeupRVzl6zF1PmrMTJcRLlcRLVmo1KpolIpgWBjapeJE1dMwlmvX4sTV82BkSE8uW4bvv79P+L+R7dgf08ZkkyYlgkrl8e0efNRqZpobmpH946NyFpZlEcHUS2PYt7s6WhrznvhKGtTa/6hDrH8jT1oUgZiwqiVlSkbpf07Xs9PR7BPR/XLCg19BVHDgKDYTBOHXQD17Wa4+BzAA134cHLzhJRiYThTzWO0D+scfIZKS1xXLIGOZhtS8UAiMajkhJlzih/+OsGBG/oyspk8Hn96N77xrWvwoQ+8AVt37EHNsb3Ui1GzHcyaOQnX/fIL+Oi/fQN/uf1pbDu4CS35NjTnWpExTJAhwDBQqpXhQKKrfQ6cCjB4aI8rAClN9PftwPDwfpiWhebmDhimBQYh39SBrqnz0Dl9Nso2Y+O69SiNjqCpOYsZ01qxdnk7Tl27CKuWTcPM6e0YKZXwzIbt+Nr//g6PP7MP23b3w5EE03S7B9mMibbpc9E6czbmLF6AzU9vx+jgIOxqFZ1ts9F98DnYpUGsOmYaLMNA1Zb1tRwik2uaxw+kllkbowyVfznSnVHAQHpg8HfaqquvotXI+QoIQbRyCCv6n2FKZPrjgixDvjDm9NIoK5ZIXWSaIPovsdLIEw3+G3y6IpE1CmkyRaQx/bBC38laJZlSrLZkRqEph/POOxVPPnM9ssKEYRn4/FeuxYEDh/DvH3s7LMOEhHSltBlwHAfz5s7Ar3/5ZVx73a344U/+iGc27ETf0H6vSyMgWSKTzcAyTeRzbaiMVsDSbTmODh3C0MA+FFqmYN7CE5DNt0A6EhI2yGAYgjHSuw3TpzbhtIvmYPniaVixbBamTG4FSRvbtu/FAw8/jfsf3YRnnt+Dnr4R2CyQyeRhWFlkclkYmTzMjAXDsFApVtC7azcG9x1EZXAA1XIRbS0zYQgDI0O9aG22cME5x4KD/ItS9IB07r94m1V346xwsVOqUI0iP3BYpbf6LeWjTe81ntdyClU9s46joJ6BEVYLdo7joFgqYXBwEO3t7SjkPEqwozX88yIAKgSJ1O8QQsQIIgXYnSQjl60voO5SvD15gB6pkaB42HMRGVPxBDf7evrx+jd/Bg89tgWFfA4sJUqlESycPwlvfsMFeN1rX4Yli2e7/fcIqGikVMITTzyP9es3Y+v2PSiOltHW1oJ1z27DnXc+hjmzVqG9bQH6Bw8ik81gpLQfBhWxfOUpmDxpOgoFB3NmdWD+vMmYMaMV06e2YlJXC4RB6O0fwrYd+/HEU5vx9IbdeGHzXhzqHUbNMUBmFhkzC8vKQhgWDGG6tQUjB1fYWIIEgdlxPYtdhV0ro1CYjMlTjsG+vc+g++BWvOnS1fjOF98DyU6wCVXwHStz9VLpAqjGIdD4Q8gWJNjvc8fjCFcZij1QYnz+X0qGZJnqRRNrDUdSxiuSXuu5ff0v4oBFmTXab42MhgilUgm9/f1ob29DU1MBatePAdChviEW3uFnTz3UNQBDaG9vQyGXPzoG4MVSR/XUcRo3AG4KQJ6MGHtz/ZRAQiF8DQBWiEcZEEJHVPqYilzGwO49h/Dhf/8m/nzTIwBZsKwMpCNRq1XR1dmME45fjDNPX43ly+ZhyTHzMH3GZBSaci7paMKfbdt34867H0FbaydaWzphZky0thTQ0myhta2AQiEHQ7j8/j09/dizrxe79/Vg285DeO6FXdiydQ8O9QxhpGjDgYAgC1Ymj0ymCflCE7L5NuQLnchk21xWXZaubBczpJSo2TXYjgPbsUEsQcTIZnPImBl0H9yEnu6dmD+rDTf8/OOYM3MSbMcJBVP9YmtEuFN6sp7BJDZTxHuFDW2DCU6EjD1kWnQNACkGgA/XABzJQOAoswMTEYqlEvr6+9De1oZCIQ/DJ/n1QVA9/cPsZ13+YhRLJQwMDr44BkCMA4QxoRIAaaKnYxoA8rx7gBIWiRqALhWYgBMQe4hgswYS7D4LkKdNZxBgGgKVqoM//OFeXPnTG/DU+l0YGvaRd+QSVbKEYUi0t+Uxc2YXZkyfjKlTujB1SjumTGlHR3szsrk8hGkim7GQzVioVqoYHR3FwNAI+npH0Ns7hMHhEgaGRtE7UETvQBE9vYMolWxIdnkHSRAsYUAIAdPKIJdrQVNTB7LZFkBkUamOojjSjVp1FLZXo3DVZASEYcDK5JHNd6DQPAVGphkmmXBQw+jwAfT3bMPwUC+mdmXx429+AGeftjqA+2p69Wp+G4iqGmCWoRf3IMFS1bvXDABHYgClYwPfAERUcQBIR1dWihbcOcU4hBKYR6CgcBSNgG8Aevv70NHe7hoAYYSdBADU3T/M5PW8pHTAzCiV3Qig7WgbgIa8fwjeoDr9/LEMAAUTUTqQI8kACG9xgggAnNjVNIIUQCnQRAxAmJMRhOuHQALImhlUqjU89/w23Hb7o3jokQ1Yt34renoHUa06kEwulTu8kUMiF6UnvC6NN4foAjcNFyoM16P5xJ/CG88VwoBhWjCsDEzDdLEBZMAwMijkOtDaMgX5fDPKlUEMDO7B0MBB1OwSpHRAwusICTeS8glWmAUAO1A1JiMDoqybDkACsoZTT5iPz/z7G3HCmsWo2bUYpNd9plKX4gO5JOkeaR97BpmgGoAQ2GIAsBPI/djH+nu/c6XGdcXmwzEARwwdfyR0BpIEeNnt7PkRQEd7O/KFPAzDCL5PgkMoMAmCgIDjOEcEtjxmr7JRGHBCv5jHGWCQ1h4a64NYkwWFRs/M8cpslAiU/PBURAQafClr9/fVWhUkCMetOQbHrVkChyX27+/B1q17sH3HPuzcdRC7du3DvgP9GBwoYaRYQblShVOrBnTWggQM04BkwsGeQQgykTGzyGc74TAgIQOuAdOwIEQWwsjCEBnkjBwK+QJM08BIcQjb9j2L0eIhsHfoLTODfK4FVrYVItMCI2NBWCbYEIBhgYUFKR3YlSKqxX4XSWiYkE4VpeIoPvLeC/AfH/kHZCxT8/wqvIS0UXR1RFehZGeVOE/GgENjIAJiZKCNuw3W8TBUh0qyUW4eVkuVHFfvOZJFQpVBWS38BQrc7oi0GVRHFRrmgDGVlSI9Y1zy4PELSgdGUYpV5AlbWCXZCzQGucGxTgVAwhHONmp0MNIjsaAQgx4SEctgQzIzqjU7KDLOmDEJs2ZMwVlnHK/kwxLVSg2VSg2Vcg2OYwd9fEEEEgKOZHzs8m/jut8/iI62HCa3zkJzrhOlahFVuwSHbY/92IBlZpHLNEEy0D90AD3921CpDALCHTgyzTwmdSwACQPCzMDMNyPb1olcRyeMQg6ctQDLgjQst1zKDAMSkBLOaD823f87WKjgFeefgIxlombbydsn5YcyoJUPn4FgX7IuARDI9T2rStjKMfag+tsi2q7mw+bm0ZMQTXj4qLUnPYIUpRYV4oAoZARihbAynB2mcWKRx7C0lMi8pSxyJESfUOihCjhS6L+pwa4i69CE6CglqdBBSo54WJEZ89/nFww5MiYcEDYSwbbt2EW5ubaFTNZCayshren6vW9/DNVqDTf+5QmUK0+go6kDbU2dKORaYRgFgARsp4zhkf3YV+rDaHUUzA4MYaG1aQY6Wyahe2A3RisDyJOJSa0LMGAPQ1hZVxZ8eAiyUgXncqBcBmwaEIYBYZkwTAv55hb07liPob4enHnKYqxavsDl9k8k71ZCfk6QZCfWZy94nIeMU0a5Irlj/S2m88WRxkY8DpWqyKVwmtej8fABjgd9zBCszLIgbPUzAJMV2uyoi6MGLw7jHPZJY+eJEfTRkWEaGi/TA8cAPskgDJlqVyhwU8GIgA+wUqyMajBSdYel141IOARCMdJdne245mf/iW//76/wgx/diD37dmD/wG5YZhamyABg1GQFtVoNlkVoay6gVGZkM21YOH0NLJEHSYGth/rR3b8La2edg/xQD/aN7ABqWZCdA2wJOBLSqXlRgIBDBMPKY2TfTuxd9wCyJuED//Rq5DOWC25qOPklrX2n1l04rdft1QiShwc5IBVphP0peQrV2wcUgcIdzugwjT2wmiwxP06GXBVBKxQ5elKpwhnUPTDMwVAFAbZjo1yuYHBw0C0C5pUiIB9Zwo8jzgHIybP59Rh6BImwSKiVCkIsAGmCJspRjUwDRnuwaULFgRyYokREJNJZ7QkJuorwYNsUpBf+d+7afQC33v4w7rnvaWzdtg/FYgWGANo7mrBm1TE44/RjsXD+TLz7fV/Bug27MLV9FhZOW4uM0YzN3U+he2g7pudn4/RFr0HFqWF/aQ+KpgOZFUDGBFsm2Moik21FR9tkVIuHsP6x32Oweyfe+/ZX4jtf+xAYDCkdhZE2acAu3mqTLJR81dcDQDLph9eq46gvZwWwxRSUAMKskLzrS28DunqajCPGi9Xg2eWoH+TDqwuUSiX0DQygvb0VhXwBhmmEeAryugAqekg6DkrlsgsEamtDvhDpAvCRwUSzJpR5dHuhYxoAQfrcUCDfxZ4BEGEdiXRBBiMiCR7KV1FCt8LtEBgKGk7pVSSbfj/XT6C3JiVdY63wGb6uUq2iVrUhhEA+n9XWe/36zXjLu67Ahk3dmNQ6GwunrkHGtPDCvocxUDyAjuwMnDT/lZg36RgMV0oYqAyhZjqAZcHIFECWxP7+jXhhxyMYGTyIV114Av7vO5ejtaUAKSUcli6smjl5upZlpDNA3uw+IwK0TOzJC2Y4gYHhWAuQlZqUnte7hkVK34AkGxf5N1DJZmWLEUXaFhPgASiVym4bsKMNhUIBhjCC+5XMXhtQ4ayXLFEqljAw5BmAfE4vuBwpA0DJtM4vvgGgECkYVaZmhiR/7kHEeaZAMAiJVGpJHtvXpTMglVxQ5WRMrpQJP9IgJKYlofFITkfqre6OnfvwgY/+D265Yx2a821YOv045LNt2N6zCb3FPYAEurKTMLV5NtoLM5DJt6Bol9Ezugv7+zeib3g/cnkL73rLK/CFK96L5kIOjgzbkWPx8XPIUOHiEyJz3y5MPWIo/BDWMwCIGgAfaaiqL/lDRkqYV88A1AUCvdgWgQ8fB9DV2YF8Lgdhmp6B8ep93f3DDAKEJ1DtODZK5RIGBl114EI+p88G8BGIdV5EPrVGI4AkVlnh55iqwlDkAIrIwdQELCmsBajUCiKykGNdIxHVjZTCIY/G9wopB2y0WMJHL/8WfnrNHWgvTMHq2efAMLNYv/sujFa7wbKGUqUGcAZEBiRJCANoabZw8trFeN97Xo2LX3EGAPYKfwj7+InVdY5wALiHU6reX7G1MsFg+Hcro706uDgE9pHbgiOHX+10TQQJ+CJOE/HENAP8FMdNAcroG+hDR0c7crkcTA8J6CsmmeyNtUoPbOE/jPryqUeR/PyoLjY1juJgQJLHBsDjF3gItiPH6aiT2tJcRw56rEkw/7CNrbrMiocMuz5NhTy++81/w9BQEdf/8XHs6F6PJdOOQ1umGT0D2/HG152Ns848AVu3HsDw8AiaWgpYvGgWjj/2GKxauQCmMOH4Hp9TpLWhgfMiGP9ImB89qGkVPEby/Dir6RqFbVlOPyxHWo7jiNQJJrj9GVKTomd2tT8JFKbzXkHKDHRqCWFe4IHZJzTjFx1oOKxKX0rxkQ6HIFRtXES6BMH5IF3KiVhp7SmHlRNEQceIhUKhy7gskc7sFOlZe1VtIgFKKE2FBS5O1CusJ08twchYFr78+ffh0Uc2YP/BTZjSPB2dzbOwo3cbiqUa3vW2SxI/ymEHtnS0Fat3+EmD7ZJaDYq3iH0QJOs2w+eqSP4aNXGm+g8I9el9Uu08HwnZurH4Bw7/C0KjyqFGoB/dEkFK9tjyFK1kCjZRXASjEWtENEas34g0ko+QUJASRBPq6CGkOqDINVCkL8zx0+RdQzAdzGFWzcEBdvuslKQ3Fy1+qd+jzmtzFIGFZMFEVgsOrK8uuQ9Y+qGr5Difhff+JFo7Cca8eTPwr+97NSp2EQcGd6OjeTpaC1146LHnsWXbHgCALR3tH72fr++jxrwngSO5LjG0NecknvsU38A+sWgs0CPlcRCoAWdO0Tz8sKrzNI7PokAqvt5QYHomnaSZFFLVC1+RypNaD/tTXv6VyHGheCeOJVQ6fkdEfNe4erDqkyEOmVwwhjFvwMpTgtIPc7plYjUEVSnTycOsceRZxvrWOrsNxZjWIhJFxHWBKT5DEVOqOrZGAx3AaSkqSaYfMJX+4J1vvwTLls7FoZF9GK50oyWXx6GDPbj/wafH1NF2waRRSvQGElkP/gtK+mRWnkUU3krafIgC+Uv8/qDrSpyoz1H/cFP85NEEo8+xeDAjfirOMR/OOGhnkZOpEAJiUB/josCzhNJnCfLMpMkpFQwR9q85rqjiY7ljNEYRRFTUKPBRSrc4AQaapvpJyRaftE3NMVrxcB6dNC/o11opwXrJyBiKSsJaV64syLORmr2Gj1TJyxVAnSoCE4TkXuTQ0dGKN73hPIyWh/Hk9ntxcHAfHFi4/e7HI+md9PTmFAnqxJK1bhU5Ef6b5gT1w0sUfr8M0imfns1tseoYi5BXnqNzIGrWRzT23iM+YuE+p3TFtEK7TIkWECHuAZL3tdpiJlVym7U9bQbMqcqCRki+9JA4MWdlXfoqsS6glNeTWIwbkeI9QvXIMb+KdCiwWgfQZIVZZR73aZYomAcK9G2Io4pMnlBIdHxBmTqsM8ASkEGkVCW1ho3Xayf/uiLpCSfw81x2ydm4/nd3oFqTEIYJKaair28Ig8MjaGtthhNNVSTXKaRRwAGYZNhkVPeXw3TGn/8noti+8qsgRC4Bg2maIEGoVmtgh/UdXCd/pVjQHMLhfWOpqz4dxvYciyeX0BhBaQINeBwqrHRF3MER5XmEAwEmlNaLIIINtRfOdQslRNTYzTXwOj5CyN80SvBg09QTAeGIgKfKnKISuRJFZMRDQkpS4jxOxKPrRt5IIBxthP1Q+mjFYAaDNJaYqOiFeh9JXsPvhxMDxyyZg3vu/DEky/DzmZHNWkGbD4rENCeIc/o5LPtgH05m//O7BqRidKALWPigFp2OjQJPLwwDt9z7PLbv6cPbX3sKTEGoOQ4cECQIBjMMIm26NK14pY6Mx6IGvHjygfW1SahxyjD2aNE1SsuArMIdBiJ2++HMCvWwnKBuatrcAI2dVh1Rj09JtpBSxzSJkjQbo14yPqbIxEEoLmIMw6xEAKQ5dR/FJ9kVICGl3uBPwjET0qNTBc1FITuub92JOTWrIiRYPFJKRQw0N+WjZUsNkMMIW0vp1P2pcb9WXyENwcgBxj86JRCvrYbp5d0Pb8J9T+xFW2cWZ71kBZqbc9jfP4pyVWJKex45SOSNhuD4f3ecoYeTDgcmMxClVTSTGKDugZGwtEIIOQEHBlwgUCEfAoF4ggeS/rZrp3ICRgE3wu2JjA1J9opH5BFyhpGhDOSsBEHjrddmA9wL0WmavYNLFNdh1N9LdXIVf5jLI+aYyGMKokpqAP3GinBM/UNUD03HIER5ZoSXjsroGD/rOYOMrJNhGDjYM4SegVFUHRtPbz2AUcriuV6gRoQLV0/CaYsmo0mwq4brGWzpSF3cJZKmpc4J/J0i/5KRgEX0DfSjo70duVwepmUq9QCFtYKUQhAlhBTjlgn7Ozn8YxnPgCmWx2hraoPlUjmwQsmtfCVbUiIED+bqT2H5OalSpOIEv5R0cKI4H1JzV+8QS8mIwd7GgeGo60eYXdZhHnuibazDr8l1+1lGZNSaeOyZfGaG4ziYNqkFq4+ZgbVL5+D1F5yIA701dKMJsmMy1m/uCeYMAn0cycnEHjzWWvy/9UdPvzjYnxTwX4Y1Uo/vzZuUcuT4ihR/x+EQjWFZ6z1sNQ0gpYKutfQjwAu11qAOuao9/AjJcFibJYpcl9/Sk7rnJUI8+w6BHm7djRuiQai71/0ugowr7RLF5ddZuoer7gHicOYfGnEPu6RpnD4RxynXWLMdVKo1MBgDg8PIogyr+wCKG7dg9bxOb7OzUl/gaD4UDg/x33iz+7oc43S6lIiXoIRIOEyqTKGo4wRhKejIcZ0dKd60w+gSsEcqmUgHliI/TETIZd05+kql5slZAaZpIJOxYNckHEfCsgwIw0U+sGQI4a6hIyXsWg25TAYgQqVSDdptpmHCsgzYDqNWqyGbsWAIivGj2o4N23GCizVNAYNMjaqMwbAdnUgkBBa5dORpf2zpBD0MUySzs9reTL9hCm9ehD2WH4JlmEGoLMh9f82xwRLIWhkA7jSif72macAQJmqSYYBdSfKE1Xekq4+Qy7hFx1rVdjsJTLFtYZlGUMAmEnjg6a14dN1mvOactcg3N6NcddDZlEUm9HaIc4ywPrPBDYzrkoasPixwqr4IPuZUJHuYRqTqOWKkicKGr893yb44qJIB+V4mZAOiw+QAZA0bQDQx706Hq8cWZe5J+FBWSFFMQ6C3bxDf+J9foKujFR/64JthmAYMYeDhh/+KX/7qNlxy0Vm4+MIz8NOf34BHH98AEmYwT1EtV3DmmcfiDW+4EF/40o/R1zeAf//Y2zFpcheIgA3PbsL3fvBbnHjCCrznna/CL6/9E+5/YB2sTBbMjGzGwprVC/GqS89BW1uzZyQy2L5zH3557U1Yt34LpARWLF+At7zpFThm8VyXdy+yEw3DwC9/9Sfc++CzMC0LjuPAMATmz56Kiy86A0uPmQ9miZGREr7831dhYLjiTvFJidaWPF527kl46TknwRQCDz+yDlf+9I946bkn4Q2vPx+OZFx/w53445/uQW//CNo72nD6qWvw+lefAxDhC1/+CQo5C5f/2zuQy2dhGRbuue9x/OKaP+Pii85CNpfBb39/NzLZrIdIE5AsYNs21qxagJedcyK++e2rsXz5Irzvn14P247P/Qth4Le3r8NzW3uwZOEU9PQOoVQs4j2vOwPZrIUf3fAEHCa87ZWr0ZSzgk0vvRkBIvZSM7WIWr8FR4gjuyYiFExqShMVLeRxhrke/Vws7aZwjsIvgrsSgG53R7rDQBRUYKUSp1LSRfAYEGDW4IJh70xAKzyMqwY7nsOfQrBGkXxTZTALrLhCqCGEwOhIGb/45W0Y6huCEMAnPv4eGIKw6YWd+NmPb8CUKZNx6SVn4bbbH8P1192GjqldyOUsMARKxSI6OvJ4w+tfid/89h5s37kX73nXazB5chdMw8C+vYfwsytvRqlo41/e8xo8+MDTuOqHN6BlcifyuQxGSlUUR6u4+le34lc//zymT+3CfQ8+jXe89wvYtnEnOqa0IpPJ44Y/PoyfXXMrfvCdj+CiV5wZI98URLj3wSfx4x/8Cc2T25EzCeWqg5GhIr7x3d/ii597N971tktRLtfws2v+ggM9ZUzuagJYord/CN/+/u/xqX9/M6741Hvxwgs7cdWPfg/LyuJN/3ABvvatn+HTV/wYLS0FzJ87A08+tQ2/+vWtWL5kNhYtno+rfvEXdLTl8ZEPvQW5Qg5CCGx4bjt+fuXv0NXl0pv/7vqbUWhux9BIDQ4baGvJgKWNSmkYK1cswE+uvAkvO/8UvP99b4ANR3uqhhDY1zeEH1//VxwYZOQe3oU3nLcIH3nby2AZhP7hEu56bDdsm/GP5y+HISzYUi2PsJ69SMQERFUIKbGqBD0BhxTp4/MYHTJihZUqqaXtUdeD4oC9GDCQSBFd9VSvvc6TGXy4AEi6rZ5GLZDu9SkhTKc4a27DVCiHG1dFlVDjP6onQmo7DnIZA5V8K77+zeuxZMl8/MNrzwsWksFwAJhWFjBNfPNrH8JFF5+DWtX1xIZpwHEc5At55AtNbppAyhBxzoRpueF5rlAArAz+64p34w2vOx99A8P45Gd/gBt/dw9+8/s78LY3X4QPfPR/sGNnH674z/fiHW+7GBnLxK9+czs+9bmr8P6P/A9WrVyM2bOmeelAeDOWRxf+mcvfire86QIUixU8/Oiz+MRnf4KPXv49LFk8B0uWL4JpCsyc1o5b//Q/6OxoxrPPbcPb3/tVfPv7v8fb33oJhDAAIwvDNDBSruLn19yMQks7brnp6zjp+GXYe6AHjz66HscdtwLdfUPIZjLI55uQL+SQNS0AQHNrC5DtgMOM9/3TG/D6114A6TBe96ZPYuuObvz2ms/jmMWzUSjk8OyGrcg0NSOTycKRrMFXiQiWZeCeR7eiZ6iG1Yu6sHF7HyZ3FpDLCFQqNgAgkzHdTZ0EvI8McrEaAnDURStIvcNW9Oa6WJVo7SltDF/r2BDVYSHWx69VR+cCgfyes9SHZhIVb3k8emUT5E6b6MGvSzFMDZABc7g4frGPBJpbMqjZNj52+fcwd94MCNNwpSuEgAkgk82ArCZs2LgTucIjqFSqME2Bc885JcAWCGEgYxhBgdV2HLBdhm27ir1kGICRw7SpXZ4ASBfOOv1Y3Hjdrdi7pxt33/tXrH/6BVx44Rm44lPvddu1YHz0g2/CI489g9/+5m7cc98TeNubL0bGO2z+H9PMACKLSZM6MH3aFDAYCxfMxs5d+/Hp//hf/P7Gu3DF6iUwTQuOIzBjxlS0txYwbepkzJs9GY8+tgGHuvtDz2sIZC0DHe0t2Li1D1de9Qfs33sQS5bOx6suPRcMoLdvCKZlYWikhh9deSMKhSwECTz06PMQmTwMw0RrcxOyhQLsagWmCUinivbONkydPtmtPTBDwpVLd8Aa8lQQMDhaxp/v34bZ09rxyfecicu/cQduvGMTLjxjGdoLWddAOzKAaTOls/0mUrAlzHgc1oBeo6S04+3f1k2hOTIEwZrMGvtIQLA3GkRemycq2cNHoMBR74aP1nhlXVXiJDwyB8bPEAYcKbB44SS8/PwT8fkv/BTv/8CX8LKXnQxkC+7B9wtzVg7f+cGNcOwipARyFnDPnT/BooXzIWtVSFkNKuiCAGEQ4CnzhLp4NtY9uxUzZq7Dnr3d+Pkv7wAyTTjphOXo6ekDbAdLj5kFIqBcrbiHO5PF6pWL8dtf34X+viGsf2Yjfvv7uwAitDZl8E//9CaXTMNnPQJQs21kTAtr164EMoQ9ew9AEEOYBooVB/c9+DQ625rx8GPrsG79Fkyb2ooZM6di/TObAWYYJGAZBr7wuffio5d/Bz++8gb86AfXoW1SO84/72R8/SsfRi6bQSaTxaGeIi7/1A89Qg+GaeUgTAHhjflKR8KxHUCYIMMCvJYeEUE6DlhWweyEECzPKRmmgfsf3Y4Xdg/i3a9aiRMWz8B5p87HlTduwD1PbMfrX7YCXFLrOglcBKy0YblOt4GOIt3FESIASXu9z5oUMCtxPPI1KdBr5ZDFNsB60+HDoxpN/Q93cRu9xjEqt34kJJnBTg3VShEfeP+bsW3rblzzqzuwc/8wjExTOJTCgFOr4sufexdOO3UNqjUbgoAFC+bAYYZBEsXRYfQPj2C+IWB56QWJHLK5nPsZ0gEMga9/89f4ytd+DmYT2XweH/3IP+CSi87CjX+6B7By2LR5j1u4MY3gVrds3QMYGUye1Imn123EV79xDaxMBjOmtuBtb38tiCXgVOA4dnB4AODZZzcDnMXcOTMgJYGljf6hIl7zpk+AJMORAjOmteG73/gIZk7thPTe79eMzjnrJNx754/wxBMb8PgTz+APf3kYv/3NfWhtbcbnP/cvqNk2Zk5txne++Wlks1mYhsCNN92Pb37jGji2E4T0EgAJA6aZdSv6AUiIPaUqGWxF8pCR5aqN6299BgyBzbsH8cWr7sfOnhKsfCtueXArLjlzKQwiGAahYsuw9RfrY6aftL9bROB47QHFZdNJmXwwOVLfDOil6AguAcc4N46e1Fgq0TelEFREp7I4aEU50hXiEELgq1/9GHbs6cVDj20PJL8BoFZzwNLG7NmTsGb1IowWK5ASqFZtNLc0Y/GS2XjyiedxzbV/wby5M1ApV3DddbeBazUsXzov7M/aDt77nktw1hnH47vf/xUefew5nH7aGmQsEyeftAoLFkzBzbc/jq998xd459svBRFw3W9uw29/dxemTGvBiScsx5QpnVh30rEgIWCaBiZ1tEAYJiCyGB4po7d3AKVqFfff/yS+9b3rUWgt4OILz/QGnRgtTVl85b8+iGqlgk9+9vvo7GzBaaev9VIJw9NNdu/7h1deh/nz5+L0047HuWefiMWL5+O1j30Ou/d2w7YdOHYV2WYLp5+6Bk1NBQgAGzZuA5wqHNuBK4HohM5GBTmpPWyGNn2ZsUw89PR2rN/ci/aChU1bduO5FwiWZaItJ/Dsjir+unEfVi2eBiltSCkDbIZkRemJSLH4Rz/4nJBq9hGZiFVFcsL5T4IrGGuGfHcKQiop5W/ogsZexhcrwq9DgJNATU463bdXNClXbVRthm3b6Oxsx/e/83G86nWXY+uGnSiOlmEzUBodBSplfPgj38KnPvND2JJRrlRw8gmLce21/4MPf/gf8ejjz+F7/3cj/vzn+1GplLF/90GccNJyvO5157sFR5uBSg0vOWUVXvfql2L+/Bl4+UUfxvs//N9YtHAWVi1fiK995UP41w9+FZ/49A/xw6v+DAHG1i070dqax9e++M9YvHgOaraNYxbP1oAf1WoFAOELX70a3/j2r1CrVnHwQDdaWprwn595N8447Xgc6htEpVQDO8BFrzwT0ya14cD+Q/ivK36CD330a/jZj69wgWGyjEqljP7BIXz1a1dh565eLF26EG0d7diydR8yFuGtb7oAggRKIyMoZgTK5SrMTBZZy3D3oWN4Xj98IKXiCEaG+1Cu1oLk0zAI0jEAFq66sePeU7Vm41c3PYVqTeIL7zoVL1kzD7WqDdMycOtDm/HZH9yPq//4V3zu/S9HtVKDrCHgSyRtZLtO6YgmyIaHI8eYrckAHK5uoGJZiRCIvDIzjMs/8cnPRVmxbMdGqVRGPp+DZVljEhc0PO84UWqwBs1zulKX0BeUoIxEJBczpeOgVBzB2mOX4rRTjwMzo2tSJ1YsmYOaXcRppx2LpUsXoLe3F52TWrFg4RzMmjUNCxbMwrx5M3HsqkU49ZRjMXP6FLz0nBPRnLdQKo5i6pQ2vOnNr8QX/uuDmDZ1MhxmDPYPoKk1j5edeyJmzJyKadMnY8a0TlQrVTQVLCxbsQjLli3AeS89GR0dTaiUimhva8Jll52Dr37xA3jFBWegWq26JJdSul5Puulcd3cv2jqasGL5AiyYPxPLls3Dqy47E5/77HvxqsvOgSMdOLZE96FDWLZkLs44Yy1yuQzWHrccDiTKpQrWrF6MjGWhWKrg9NPX4MQTVuKEE1ZhypROlMsVOHYNa49fis9+8h141WXnolKtYqCvD2vWLMaZZ50IwzBgEKFULKLGjLPOPBarViyC7Xnn7oMHMXfONJx77ilobWkCmFGpVDFaKuLkE5fjxBNWgBkwBKFvcBTrN+7BsUum4rKXrkJTLoNcxkLWMjC1qxW1agUZg3DM/MkwmDFvegtOWDYDGcv0EIr+pKuq/5hcvx57HI6OEpHtkZkJ8NWmSuUSCvk8LMty6f7C4Q9PHpz0+GN0pIj+gX60t3egqZCPUTUrY2s6TbMKqEngsJfibxNmiSioSaUAR1K6QzA8Hn1XLbnq4eDZlekyTdiOg1qthkwmA9M03JTAU+Ulcimna1VXx8+yTBiGgUqlAkECmayFWtWGXXMAcoE/lmWgZjueDDeQzWZgEqFSc4LWnmlayJgGarUamAmZjOl+j10LsRsRuhnLysAgkcDlJ1GrVj1eAkIm4yP3XBVfQwhkTddbV6o2hOEO3TiOhOM4yGQsEOCG+45EJmsBDJQrNRAx8tkMmIFStRq4U8twW5+248Cu2V4a5SIuichbZxn0+bO5DBxbolKpeYNYnhaD4dZAal7EoI5tZyzLZbe2HffQM6NarXnPR4/03BoER8kJgrx5LF2Aw3POlMzkQ+NQDdbIeynkivQ2giDCaKmE3r5edHV2IV/IBerAAddCz8CIRm8jHYliuYi+3j50dHa6BoAViIRMvxpSZJl9L6stkpgAhDgVUtwgNW89Wm0KBTzieRcFLDP19WcjxRVK79D4tFUcFSH1M7MxMRPue4wAu6KIGAAQysMJ5TKELo+lfJ/fDJcklPKeejccKpNHJRtZ5zYU0n2tBCXuXpXOmzXG6aD65OXoKqmn/lBcTyURid9i1f4oLVbw3RxFBLDeb2edWTcKh4lsj4kZAG4sZSeuY2JUyLJKB+/LqQdj0oRiseTRgnegUMjDEK6zEuQK2pgBmIdDbRa3bSBA4wh3SF0REbLPMDgVjDMmtzlzfdqeeuaY4yKJVJeAgLQmMStIRqK6UA0I4WoqGIbwJvKkqzakzGAzSwhhQDoOLCsDx7Zdth3HgRAERzowDMN7rwj48Jk9zLzX0iHhvcZ9ASQxDMOEdFz2HmaGaYjgc6q2g4xloVatQRgCLKX3PQzTMGDX7CCCMww3VbJtFzJsOw4Mn5CDQuPuz0UYhuG27Gq2d282TNMACQq+Dx7tvOM4EML9/yxd6KppuHUBwxCoVmqeTL2Lm2Apvd+5EYO7cSUIRjhpyGOH6SrVuEq2FBtWYl25NKovSgmK1Xw0Btg4DjtIBfn4+4tIM8YxEVsF+cSq1gIRzNjYr1IU43Ek5pzMfzg2ICih4tlY/tXgAvuwR1bujRI6BBSPGqBQUqX9sSwLTz75VzQXCnhh0xYsOuYYtLa2Yue2bejo6sJtt9yGaTOm4ZxzzsIz657BOeeciat/fjUOHjyEk046AfMXzMOB/ftx2umn4sY/3IS21lbcedfdgATOf/m5OP20U7Fpy1b88Q9/RlMhj9XHHof2thb86Y83obOrE6tWLcfQ4BAuuOB8/PGPf8batcfjN7/9PUqlMs459ywsmD8Pf7jxT3jve9+BO++8F8uWLcHvfncjqtUauro68NrXvgotzU149tnn8Psb/ohazcE5556JoaFBXHjRhdiyeSsc28aOHTvx/PMvYMqULlz2qkvR2tKC226/Ey0tLVixcjkevP8hvPIV5+GBBx/Dlq1b8Y63vQl33/sgurq60Nffh4yVQU9vHzY8swGzZs1w1+HqXyGbzWLmzBm45JKLYJoGdu/ahWt/+Wt0tLdj9eqV6O3rwyWXXoQ//elmTJ02FffcdS9aWprw0pedi/kL5sO2nTH5E5PaYkyRSh9FWIgpheh1ojms0tlIYhrSUhAVnchjAIBoDHagQO/DZzjiwLkyMQSrKAhvCMhnfqFG2AwbtXKEZDZVQsJcOCbGvMpIZW7lOtTKXM/gcP2KsDAM7N69FwcOHsRzG17Ad7/9fWzbuh3btu3Egw8+hGwuj1NPfQmqlQq2bN6Cm/9yKzo62vHP73s3lixZjIGBAdx9z/148KFHcffd9+PY447FsmXLsGDBfJx44olgAAcPdKNSqWHGjJlYv+4ZHDxwANKRWLl8KZYvW4pNmzbjyp9ejZotcf3v/oC1a9fiXe96G265+Xbs2rUX1/7qetz4h5uwa+de9PT0o69vAO9+99vR3NKCP/3pZpAQWLJsCVauWo2Zs2fjtFNPwX33Poj773sA99xzP3p7+rBt23ZM6urEkiVLkclkIQRhy5Yd+MpXv41NL2zGjh074DDjySefwu7de9DbP4B9Bw7huutuxBOPP4U1q1fiuQ0b0dbWjmXLl6FYLKFWq+Hd73orug9146GHHkYuY6C/tw9DQ6NYvmIZlq1Yih07duGqq67GSLGIcrmCUqWKlauWY9KkLkiHx8VtwDF2J47vPaTwG9JhIlQbJRelIzwR788BcEgDJn25OUYwIRmRc+IjX6zjF6+NMtZocPp8efLDGYsBR3rz6JOmTMZFF78Cv/jFL1Gt2Xjta1+NqVMn48c/+SkOHeqGMA109/aho7MLzU1NuOXW20BEGB4ZQU93D8CMfC6L5pYm5JsKyOayXldGYrRYAkColMuo1WqoOTbKlQqyuRzOe/l5uOfue3HxRRegr68PM2ZOx/RpU1AoFFCrlPCqyy5E98Fu3P/AozBMA6ZpoqOjDZMnT0FPnwvztSwLhaYCCs0FFHJZCMNAd3cPBgYGwAQ4kjFcLKNarQbM0flCDm96w2vw059eg1rNwfr1G/DCps2oViq47Y67YVkWnnl2A8rlijtOLAxUqxX3M0DIZbNob2tFe2cHBoaGQV7xzzQNlMtlkDDwmte9CnfffS8uvPACt0hq11AuV0BC1DHa3AA1P7+orb2GjgGN4SwnfPb8MWDhkYGEH2pCUKA4A/aiEF95Bf//+sN1BCsoqUsQ9SgUhxOxlGhpbkI+l0dLSxNe8pJTUCqVISCxY/s29A/0o6uzA02FHJqamnDe+S/Dj390JR56+BG0tDbBtCyccfqpuPSiV6CnpweOdNDR2opcJhOAWAqFPExBGBgcQHNLE1paWgBI7Nq1GzNmz8K0aVNx7tmnI5fN4NWXXYJf/PxqZLI5HLNwAebPn4sdO3bg1a95FTZu2oJ8PofR0RF87WvfwsDgAN75zne4YbRBaG5pgm1X4TBjyZLFeM1rLsOzzzyH0WIJTc1N6D7Ui+3bd2L5iqXIZToxqaMdK1cuw7TpU7HumWfx7Ibn8I//+EZ0dXXg7nvux5QpU/DJ//goug8dwu23340ZM6bhwIED2LlzF7o62nDwUA++/s3vo1ar4l3vfjsqtoNcIY9sLoPunh7s3r0HixYtwJlnvARNhQLyeXeqcO/e/Zg16yAWLV4E27ZTzj7FGH3rqk2xQgLKfyMcIB1BajBVFww6txop5AF0aGCYhWJ6HOmgWCqiv68fnZ2dOifgRIFAfyf0YGk8exQ72MlVHhJINgKsagW4Hsy2bYAZQ0MjaG5pClqHVjaLWq2GoaEhdLS3BcUuIQxI6XiFRJ8NRgRFnVKpBGk7aGlrdVu1o0U4jo1cPg/LtMBSQhgGLNPEaLGIaqWKjo52t8gnpacL7xYjq9UaKpUKmpubYVmWe60KF58gAelIkEFBy1dKidFiEQDQ1NQEIcIirWGasGs1OI5ENp8FQKh67T9DCBiG69ENw0CxWAIzkC/kUavWULNraG1t9SDR7ueVSqMumUouB8vKwpEOiAwAjHKpBNu2kcvnYEYGn6K6CdHtyJHBAE4yAPg7owQbryGIlAeKxRL6+vvR3tGOQj7viYP6ZLUE6h4YZlJ6VzXbRqVUQl//ADo7O8ZhAHhsIn46mrzJ4zMAupIvpciaxa+TKL1Nl0Sq6eosukou5HMDej1ajvTWQlVXd2hI5fAnj7EnaGl5L5DBsIeKt/LAHlKGkQ1zMHzkF3llRJ0lqCZ7RKUcEJeGhBN+BV6jIFfWMk6ISooGASWOknNA1MLBmG44ksJQ5evrHlD27yldZITrSZQ3SBP3/4QB8NqBxVIJfX196OxoRz5fgGGayloxzHDTiUBlhZNID8aEAlPjRoEjNyeOPtF68iyAz6sftlKivfAoT4HGV99I4UmGWHPBMrgSqdRdA4usjL3KgJnJ5/iTOjmmo9dpNO0WdpQIx8Mp+G1EzzAk16fC6k+oJCR1bsCUgxO9d/YFUBUVoljFRaGp9klpZUQPUKWvGoNmMIHGwusrxg4/EgRw/n+Y74pwL0kO2bBkQIYS6AKQchjq1MP4CFRAOKX9QSkRgqrCQxM3BOwXOBTZ5NjItCKXpXVGotfiMYFTA/yRATRCnTUIGJeoDm6BNIJKQSF6CxoEKa7BSJHKrzf6AUkUUckhhRnDByKx7m05VC1qzED7vWjylH1ZI9NIJmyVmqbBeLdc8GyjmHWl1XdE03pOyNcJE076gzVK+WzyyHpDUZZxtB0jR8tXn2IvqjNZmRcOvA543LRkY7b/GONT9YEKc5wgBFubg+YwrKFwIMSf/iOOYxaCVJwJLDhCDe5HDZTMZqTg8Uirw3BgNFxgi1BAVKSNKHLi5KKvTKx7PP+56VJtrGe/7PMYRZGIHPI2Rryu+vf4c4zqDIXYCeZIwS3RCARQvSheJR0em9Y/5zjOmwIDyKGT0ZQzuTG4LqeEv3wYrWqFcYgjKtwUuXfD8A1ASp06sNj62K2q5RkUOEmPzgQYHkLLL4ZRyDNPdXr5E8mZuDFFIYpSkR9mrYBiH8qJaT4irDFEoVZ14DnJBVAwK8U/SrtPVlW5dfVkf9OzdAG3XKdH7O1fx/sw4bPcczS0Gl8TmZVwmyNxNCUJxErEVHWD1Eq66yHV4hqpzFKcCrCIMWAzJffeKfkjNLyPCjlW1JQT61Dc6IY9QmodnBANU/3LMQS555N8hmcvVUoEqCSVsv16jVAEaEODYLqeXwYL5g+1pBq2RnQC63Epc4O9Upog+oqhy4Er4h/pl0ga9xuplpj8n0XyV4VZiBpgjVEkKRQpLOVcEdeP4/zXUlgsU+KBBNGwsYUStOsmTsZFJLG8sbI2zAnS7SqxPyMSAoUSVRwqIkmPBJOo8cycg/kAqJNfStuW426XeWyGqpR9FJMUP0xUYPKPOdirhhCBc3Yh5wSLTDhSuh0TQl1OwGgIKUhXB2aXFZiDcJai4oN8BHubTC/OVGWUEjkSDmukiJGKSRAaQT34unBH1COwp45Lov5NSE5/TsKz6KHKD9XlUmPo7UzpK77QBJxco3uVtexHL6ZR2hRXtLDBgQEQUaUgZZSxoWxRRtIMRNJGSoaj62pflK5LTxNkph6TC7yxFRckAho3NdUgIlefwZd+T+u21WW+4nAWIJx8S9AhoHEKgCYy8eiik+Mj9MQRR1qkRwEcSzTjl5Wyyb36iRiDpNEnZhIRcwIQhMJZT6xuQlF3sRisiD74dYBwwIv8vFxRs6Ak40x6sYHD5rDSEUjjzOO4wEa0mBLUGhLqwqSvT9LGUkufLFM2DtVvFWgHhuqlTPph4BcDohr54zgur4PpDVz5f2zbCVq/Sem5NttCigxdZOTf1/4wo8AnSqnGjkuFB0p/nROcGicI9EUr/S9au4QiC+gdJyVSiNewKBFezEmpUIKxlIohItbRZ0I5aTIY+qU4SwQpg06atSZPljvy9UyJ+IbYUIxSsOCEi+dEg+k/wqTToupM1DO9gXBXMDatHuq4vBuPnUZqDN8cK2D+XbcACQEM3DJd5SnbceAwjzFKQIkTqyHQiTWtezOp0NfQstTJ6ZPaSHUPN7HecvsbIC/VokxS20qPD9TigA6SYuhz8hHspbJGYdVdpHitcKYfAUYDaToMKf1UHiOOUQuC1FDVKnlCxp9yC4vbMvW+1JRKJiQ2Qdcmet0cF2zXQ16ORSekTgXG5QDHBI5NDBBEE0DvcKJvYrB76Mkf4U1pYFAqI6gy30MxRx/QgocU1V4UR+OnQ9IjB042y6mkffSiAwQ5+WyGh0K6dOnJdR9FXih4MJyincCp6REHxB5eNKCmchwxkErYr7UtmVJSGSSGXawQtVAsHRkHc0UCHJqQLkrKkc4CN/LJUgZknunUXerhj7TCIp6fxgUu4wkC1A6TzzvKpCVDXURKY9NPi9rJbwFTQMunMATCdENQ4TGySA/qKjxyUB5fyB+Bgwqv+KVSLTWiEUj1rOM46wVqhKtiHvQQmuowEoVeN1VP1PMUFIHmcUJgxkHlngIrzZ4eAyloRK1OwMomZ9V8yEjURUq4nwyhVSMLTSMxtswU88IU7Vn75DFB6pJ+6IPrItZb8vV8oFQouzhprj6C0owUHKVkhW8oXiZI1rMcg8Waj3CVFSmQxmgBmjkKcNHLGBp0N6wDkL8vRBj9+xqWDHZpwUkVyGOd0ittZcLCJMUSL1VxOPo+qnuAkw47j5tNKP0zXUFIdRN4qHj42rzaptA2isv1p4fLnOrpVaw8KZ0VSipacaSWRtEDqP7erw/I0Dj4dM+sl4DDw0+x9aBoqCiTKdj8SjuRAJPUvkOPWCgWZeiD5SHCpR6uJ/iZf/g55eyRbmQ5BQTmdiHD4qRUgDFRchjVawS2X5XfYj6i6tdRWnr92NVTtNKFQClBIpgZEBwppSgAP5/hydSSdS+k9amsKKUykcaSQxpfGofcoWrgTEg2BJxA2tYIb/oY1pQ9MkgSEc8ViVa0UclEjTW3GOcPxoyFTFbXRx2eoaRURwMesGI8fZHoaOPP4wVkCh6qD3hR0QZhJ8A1EmGBMxBBCKdGg1kAxchrG95RhFZlesrAocgsaWaS66NpGbH1iqJIKQLwSaPmUmnAgvv3Pk94xjNhCDyZ0ooUSXEFQUpam5nG7d2jJRz18HMStiSNmZvrsOJxtNdEYcbobTdTG4xhd1BA+vwASc0nUlpLSchtNVwW8YOeip8fS4SIouipNMLEpF8xNB1FouSqNilblqOFtAj/msIR0HBNWYZTdarVJk4u1kGhwFQ/WwRLwAGMlxRvHX6oMtwjk1CJXgLnpX7Sb9WSjBlAdaSGk0JlJr3i7KUfYw2IapycHM5JpHFCcj2okrd/KaFR6j5bN6oIOx+cXMtIFObgWGoQa6FOZI4tAbtBSWkZJcAttLzaw/LIiLlVHI8f8fizPwyG6W8SIXzvL3UrW4dDM/YQKQFCTOOsvtfz9I0UWqJFtxRAB2nBMMdx9WqbXyWQ4jhOJ14toVSxORd1qRfwmBqgN1OZiKMgD20Tkc6AS2FkQwGzEWmlOFLkptX9LpQCLccz+uChcUSAM9GWpcX7fk7KXIcNkiKdglDcUgseJSen5/74c5J/0IwYjznACh6fFIZWx/VSUG4kcEju2qZ00BTzKJLvS43mlMwTZjAZFAwKjocSjPUi0kRVUymxGTCuz0vEkZE+GhkF+rsFSY6kdpw6PoxonVlJBfRCk7eCrGNJJVSQZaifQMoycr0ZCGV4iSPk5PHhHKV3HsC8KQwElTkXGblj0oZzCBw0GEkjPpFIQYw1qITLKg0dRSI1LR/jWGOUowTPzDHUuQbt1sbc4zhfTqvw0mExcUWeY4OH/0ioDPthvoiie0NjTQyYDDc/DmnGhQ7lHs/MIf6muIkY5iAsmMSNQlik0yMG8otpxPqWZx3Azx5phlJCjXgXTg7hovV4lkEumgoiSiqARdCFcdiFCxL2h0BYPfRIpluXShRBpBeN4hX98RlqioT5UP+eQHIb6gckqDcn1XnUOmOU3Y2SCW3IC+WYOSXkOpwZYkocOjsaMwT1jRCFnbdo6uwjAfWmIytVaDpqF4aj0dWPgOHHQpYzcwI3sAIX1sBOeh2A1JyV6mAYqJ5aOms1ClaLS6RHIRQL9yja9U6IugUkx68nXmtVRom93zk8/gfLsRgkjCT8Xv5YwBpGOMKtCmAkRTwq/RdxWs2NtYXXJhXVibqxabAnsB+PNpdlI21wFSkaVh/J64iYOnGKCzkl4b6oZttjoKH+bjRVD6MXA609pWLr/TiJCRBMShU8/n4mSpyhokjrbaztzxwf/pjoXdMY6OcYKIljEWvDBa541sIaCo9jpWuqz2KL5KJXALvmhCG2CPiFmdPPcjTf/3vZxkfQv5JH8ecOFvn+USqpp3QZgaJcaoIIWctCf18/8vkcsp5u3NGHRI5rvLBxM8RjnQZVykxpXCrtGlJmG5I8fMAVmBLFE4l0CasESa6wB6/MDUQFIZgTyD2SEdjc0PMYpyQtRar10fzX5xakeJaYxr2vEp+kNtpkePCZ4r1lHymZimZVW/s0sd1KKfVAPmJn5DDPDhOqlQr6+wfR0tQE8tSmAofuFwE17L63qQxhIJPJoFQqY/uOXSjkcwFjUGB9Sa86M1iRJ9Sreq6TlMHns0uDmxjECtWrJs0zcjTPJg11SBQPkcGq/2WNtsrHO2iqQaS2AhU9OgUDwQqdU/AS8jcV+Qppev1AuRehgjq0HZkQqlMUHRblFqDEg6U+D1Zw8WlYco0MxueNk3H0po/OCxHKpLEOkdYnSKEuowQIEbPOihOw1yqAnCh+myI9atajON9ICB9fobRzgudIMrA+4aymDIhXg6KjDL9DqGDuoJMT8i8G/Jp+241USS/2QDoUY4xgjqXpSpE0UptI6bMKchl/S8UictkMMlkLwjAU/cUQBWGqiCl/cwshYFkW8oUcJEuMjIzAtu0Q0AKC40FoBeudALfgHj52JoLLZ8l6M4koUqhB0P+VkQceHDoOH6hvTASJAM6r5vRBVZ0jhonUbSk8wwRtAijOeOPVwBlwfECQWj72DZ1QW1EJWAoFkeZfiwu7luHplqzo8HlYCncSBBDk9vMjKDhBQhNwJQqLnAHNG3NUajOMdGRokAOsgw8dZb0izhr2XEen+aPMgkMjEgXQaDwLCv8VIw4+Y+XZk0JiwaT0ylnnYtBY1VhnUITHicgKSw5IISaNHEpSowktlfOuwyPSISGC+XpSplpZSRmlRyknAiMidTivEomy0r+PFIEUijnF+LB0xT4DNnmCaZnIZXOBLLiv48hK9BgCgZRStyDhGYAM8jn3BrOZDKR0POFLVuXF9Z62hiD0iCooCqlVqpJqgU2BzXIMGeh9odCtPaXo+0lmzwgldADVFp5KYc0hU2pU/VZlwHAhseEDYhmi54I6imKdI88qZNQh0pVpKeYLYi1BVQYbEaJHff0pmRiTQwx5YDQDry106FyIG4vTaauTjqyj13zjWE99J57fs+YhKXAXitaw71VJidY0PDfpoKqgNqDgBWRkypCSw22i0CDpw6/uvEwizkFpWZJGr66UG2IAI9ZGvVmJAEIeB2Wve8aOJQfnR92r6pKYpgnLspDNZWFZlqvjgAhdGsFlBCJFDhsel71pEoAsDNOA4zhgJ2BYg1B4xXzrzBrEj8MNQSIkpFSprpnj08vqgQzOPIXVWjWlSMD+cDQMZn3xEHhEVc3Y3fzuIJT7WoclBBlBsBsIgSsHl+oJDcZISPxhHwQtRp2ZiANNRvJ7srGZA2UOVCXODwZ+WJfK9klCOV4tcQ2Jd0EyjsMInL50i8N6m5HjXA+Bp0agJRB638g+8YE/0cyHFQgsUdrwn3ZoNcPPildVboSj5ZJIxKomIv4eSOwWKcq7QQpHvkOkAGkYVNmhMBBRWrXUjxI9hp4EkFJ0j+gwFwqH2bRox1VV9oViLMNU2LBJux+TIhRg7AkHsgFYIgOLXelpqeQjUf48DY6oXjRF6KBYx+FDqxuIVHJL1fNRhGvIjaaSWnGsF3oVOnDmCP0sUby8yBwDUOjRhooLDw1W8GCDjUD6vEGMOIT1fanVCTxDSzIGkwzGb9WbikQVaqTmP6wQ/6DP5IWvpRjsWqN01OosoTEiDUsQklBGYhmlVuQ9VxkaXhkYLCWOJ60D6+5Nv77CCaPdMhpp6Y7BPyx+UTbMQiggZyWiSAUjlNUO6l+RgxRzPDGLGhpDXQgGMTYmcLzgSxxCmlXG5uh3QJGsh4/wFRTeo2QtXTLVmT7JauHD+xAmEAkYpEgbN1RoTzDxFOmpgzXaYt8pRSRgtMFvtR4QfJNJGk0sI879z6q6jurT1PBZXRyF54+VnJ7VKg0zhBAqggDx0+w9NBluHCQIasTZh9RiqqEZksAgKvcU7WdzykCnupwECj2PYhwoabJBC/UpdWzWiAxD6SO3fiGOI0g/98JMpWYR8gtECl+KgSLFacXTAqWAp3VjvO9IUihiQwvhSaNkiaZVisOLpEphlKwyR5LG4RjAbjTEDWnPQo06AmxEJF3WOyjhw3el26KgKtbIacEA9Q4VWR/a9GCjfqEngo93LQgFYYsQSc1vJVxSPYqGZ1fyfeH9TYZFLHU6WQ2toIWYHA45KHz7FMrpJFSbwxZSdGwgWGgNLKM+puQunBYkaBac415bASbq4bqr2xedGFRDSyl9Ax3RKlPXU6Vgi1PjaBuSE5pterqTwFXP+iQlEekGmXTeOX9Tsqo5EYnCVUBVEIUEE4EUGkQvXCaiQEcw2kXxI0JECtsqVDht3CSW2vuRiTK3S6o4SwKoSo2uVO6H4HuES+jp1xT8s0fkpaEJglRhnTBhSpcQG10PZemi1HDKafKc+f8HWNV4q1OuGX8AAAAASUVORK5CYII=" 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| <h1 class="splash-title">Tri-Netra AI</h1> | |
| <p class="splash-subtitle">Advanced Medical Intelligence</p> | |
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| </div> | |
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" alt="Tri-Netra Logo" class="landing-logo"> | |
| <h1>Tri-Netra AI</h1> | |
| <p>Advanced MRI Analysis Platform</p> | |
| </div> | |
| <div class="role-selection"> | |
| <div class="role-card" onclick="selectRole('patient')"> | |
| <div class="role-icon">👩⚕️</div> | |
| <h3>I am a Patient</h3> | |
| <p>View my scan results in simple language with clear next steps.</p> | |
| </div> | |
| <div class="role-card" onclick="selectRole('doctor')"> | |
| <div class="role-icon">🔬</div> | |
| <h3>I am a Doctor</h3> | |
| <p>Access the technical diagnostic dashboard and clinical reports.</p> | |
| </div> | |
| </div> | |
| <div class="landing-stats"> | |
| <div class="stat-card"><h3>97-99%</h3><p>Accuracy</p></div> | |
| <div class="stat-card"><h3>30ms</h3><p>Inference Speed</p></div> | |
| <div class="stat-card"><h3>0.91</h3><p>Dice Score</p></div> | |
| <div class="stat-card"><h3 id="scans-analyzed-count">2.5k+</h3><p>Scans Analyzed</p></div> | |
| </div> | |
| </div> | |
| <!-- Patient Mode View --> | |
| <div id="patient-view" class="patient-view" style="display: none;"> | |
| <nav class="patient-nav"> | |
| <div class="brand">Tri-Netra AI</div> | |
| <div class="lang-selector"> | |
| <select id="patient-lang" onchange="changeLanguage()"> | |
| <option value="en">English</option> | |
| <option value="hi">हिंदी (Hindi)</option> | |
| <option value="pa">ਪੰਜਾਬੀ (Punjabi)</option> | |
| </select> | |
| <button class="btn btn-outline" onclick="goHome()">Exit</button> | |
| </div> | |
| </nav> | |
| <div class="patient-content"> | |
| <h2 id="p-welcome">Welcome. Let's look at your scan.</h2> | |
| <p id="p-upload-text">Upload your MRI scan below and our system will review it.</p> | |
| <div class="patient-upload-zone" id="patientUploadZone"> | |
| <input type="file" id="patientFileInput" accept="image/*" hidden> | |
| <button class="btn btn-primary btn-large" onclick="document.getElementById('patientFileInput').click()" id="p-upload-btn">Upload MRI Scan</button> | |
| </div> | |
| <div id="patient-results" style="display: none;"> | |
| <div class="result-card" id="patient-verdict-card"> | |
| <h3 id="p-verdict-title">Result</h3> | |
| <p id="p-verdict-desc">Analyzing...</p> | |
| </div> | |
| <div class="result-card risk-card" id="patient-risk-card"> | |
| <h3 id="p-risk-title" style="margin-bottom: 15px;">Risk Level</h3> | |
| <div id="patient-risk-badge" class="risk-badge">Analyzing...</div> | |
| <p id="p-risk-desc" style="margin-top: 15px; color: var(--text-color);">...</p> | |
| </div> | |
| <div class="result-card followup-card"> | |
| <h3 id="p-followup-title">Recommended Next Steps</h3> | |
| <div class="dynamic-followup-container" id="dynamic-followup-container" style="padding: 15px; background: rgba(0, 150, 136, 0.05); border-left: 4px solid var(--accent-color); margin: 15px 0; border-radius: 4px;"> | |
| <p id="p-followup-desc" style="font-weight: 500; line-height: 1.5; color: var(--text-color);">Analyzing recommendations...</p> | |
| </div> | |
| <button class="btn btn-primary" onclick="downloadPatientPDF()" style="margin-top: 15px;" id="p-btn-download">Download Detailed PDF Report</button> | |
| </div> | |
| <div class="patient-chat-card"> | |
| <h3 id="p-chat-title">Have Questions?</h3> | |
| <p id="p-chat-desc" style="font-size: 14px; color: #666; margin-bottom: 10px;">Ask our AI assistant in simple language. (Note: AI cannot give medical advice).</p> | |
| <div class="chat-window" id="patientChatWindow"></div> | |
| <div class="chat-input-area"> | |
| <input type="text" id="patientChatInput" placeholder="Ask a question about your scan..."> | |
| <button onclick="sendPatientChat()" class="btn btn-primary">Send</button> | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| <div class="app-shell" id="doctor-view" style="display: none;"> | |
| <!-- Top Navigation Bar --> | |
| <nav class="topbar" aria-label="Primary navigation"> | |
| <a class="brand" href="#" aria-label="Tri-Netra home"> | |
| <div class="brand-logo"> | |
| <img 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" alt="Tri-Netra Logo" style="width: 100%; height: 100%; object-fit: contain; border-radius: 4px;"> | |
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| <strong>Tri-Netra</strong> | |
| <small>Advanced MRI Analysis Platform</small> | |
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| <h3 class="sidebar-title">Recent Scans <span class="sidebar-sublabel">this session</span></h3> | |
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| <div class="recent-empty">No scans yet. Upload an MRI to begin.</div> | |
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| <h3 class="sidebar-title">System Status <span class="sidebar-sublabel" id="statusLastUpdated">--</span></h3> | |
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| <span class="research-pill primary">Research Preview</span> | |
| <span class="research-pill" id="heroVersion">v2.1.0</span> | |
| <span class="research-pill subtle" id="heroBackend">--</span> | |
| </div> | |
| <h1 class="research-title">Tri-Netra</h1> | |
| <p class="research-tagline"> | |
| Brain MRI tumor analysis with a cascade segmentation network, three independently-architected classifiers, and a evidence-grounded layered LLM radiology report. | |
| </p> | |
| <div class="research-actions"> | |
| <button class="btn btn-outline" id="batchUploadBtn" title="Run analysis on multiple MRI scans at once and compare results."> | |
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| Batch Upload | |
| </button> | |
| <input type="file" id="batchFileInput" accept=".png,.jpg,.jpeg" multiple hidden> | |
| <a class="btn btn-outline btn-link" href="https://github.com/Anannya-Vyas/Tri-Netra-AI" target="_blank" rel="noopener"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"> | |
| <path d="M9 19c-5 1.5-5-2.5-7-3m14 6v-3.87a3.37 3.37 0 0 0-.94-2.61c3.14-.35 6.44-1.54 6.44-7A5.44 5.44 0 0 0 20 4.77 5.07 5.07 0 0 0 19.91 1S18.73.65 16 2.48a13.38 13.38 0 0 0-7 0C6.27.65 5.09 1 5.09 1A5.07 5.07 0 0 0 5 4.77a5.44 5.44 0 0 0-1.5 3.78c0 5.42 3.3 6.61 6.44 7A3.37 3.37 0 0 0 9 18.13V22"/> | |
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| Source on GitHub | |
| </a> | |
| </div> | |
| </div> | |
| <div class="research-hero-stats"> | |
| <div class="hero-stat"> | |
| <div class="hero-stat-value">97-99%</div> | |
| <div class="hero-stat-label">Classifier accuracy<br><span>CNN / Transfer / ViT on Kaggle test set</span></div> | |
| </div> | |
| <div class="hero-stat"> | |
| <div class="hero-stat-value">0.91</div> | |
| <div class="hero-stat-label">Segmentation micro-Dice<br><span>v3 UNet, BraTS 2020 test split</span></div> | |
| </div> | |
| <div class="hero-stat"> | |
| <div class="hero-stat-value">~30 ms</div> | |
| <div class="hero-stat-label">Inference latency<br><span>ONNX runtime, single 256x256 image</span></div> | |
| </div> | |
| <div class="hero-stat"> | |
| <div class="hero-stat-value">Zero</div> | |
| <div class="hero-stat-label">Validation<br><span>Evaluated on 246-sample OOD benchmark</span></div> | |
| </div> | |
| </div> | |
| </div> | |
| <!-- Methodology pipeline diagram (collapsed by default) --> | |
| <details class="methodology-panel"> | |
| <summary> | |
| <span>Pipeline overview</span> | |
| <span class="methodology-hint">Click to expand the step-by-step methodology</span> | |
| </summary> | |
| <div class="methodology-flow"> | |
| <div class="meth-step"> | |
| <div class="meth-step-num">1</div> | |
| <div class="meth-step-body"> | |
| <h4>Upload & normalize</h4> | |
| <p>2D MRI slice (PNG/JPG) is resized to 256x256, ImageNet-normalized for the segmentation backbone, and 224x224 for the classifiers.</p> | |
| </div> | |
| </div> | |
| <div class="meth-arrow">->|</div> | |
| <div class="meth-step"> | |
| <div class="meth-step-num">2</div> | |
| <div class="meth-step-body"> | |
| <h4>Cascade segmentation</h4> | |
| <p>Grayscale input is auto-detected and routed to the T1c specialist; multi-modal input goes to v3. 4-way TTA averaging + largest-component filter.</p> | |
| </div> | |
| </div> | |
| <div class="meth-arrow">->|</div> | |
| <div class="meth-step"> | |
| <div class="meth-step-num">3</div> | |
| <div class="meth-step-body"> | |
| <h4>3-classifier ensemble</h4> | |
| <p>CNN + ResNet50 transfer + ViT-hybrid run in parallel via ONNX. Epistemic = std across models, aleatoric = entropy of mean.</p> | |
| </div> | |
| </div> | |
| <div class="meth-arrow">->|</div> | |
| <div class="meth-step"> | |
| <div class="meth-step-num">4</div> | |
| <div class="meth-step-body"> | |
| <h4>Feature extraction</h4> | |
| <p>Deterministic radiology features: geometry, intensity, GLCM texture, morphology, mass effect, internal architecture, grade-evidence score.</p> | |
| </div> | |
| </div> | |
| <div class="meth-arrow">->|</div> | |
| <div class="meth-step"> | |
| <div class="meth-step-num">5</div> | |
| <div class="meth-step-body"> | |
| <h4>Layered LLM report</h4> | |
| <p>Pattern A polish, Pattern B citation-checked differential, Pattern C/D vision observer. Every LLM claim is validated against measured features.</p> | |
| </div> | |
| </div> | |
| </div> | |
| <div class="methodology-citations"> | |
| <span class="citation-chip">BraTS 2020 (Bakas et al., 2018)</span> | |
| <span class="citation-chip">LGG-MRI Segmentation (Buda et al., 2019)</span> | |
| <span class="citation-chip">SMP UNet + ResNet34 (Iakubovskii, 2019)</span> | |
| <span class="citation-chip">Grad-CAM (Selvaraju et al., 2017)</span> | |
| <span class="citation-chip">Score-CAM / Occlusion (Wang et al., 2020)</span> | |
| <span class="citation-chip">Llama 3.3 70B Instruct (Meta, 2024)</span> | |
| <span class="citation-chip">Gemma 3 27B IT (Google DeepMind, 2025)</span> | |
| </div> | |
| </details> | |
| <div class="section-header subtle-header"> | |
| <div> | |
| <h2 class="upload-section-title">Run an analysis</h2> | |
| <p class="section-subtitle">Drop a single MRI here, or use Batch Upload above for many at once.</p> | |
| </div> | |
| </div> | |
| <!-- Batch results panel (appears after batch upload) --> | |
| <div class="batch-panel" id="batchPanel" style="display:none;"> | |
| <div class="batch-header"> | |
| <div> | |
| <h2>Batch Analysis</h2> | |
| <p class="batch-subtitle" id="batchSubtitle">--</p> | |
| </div> | |
| <div class="batch-actions"> | |
| <button class="btn btn-outline" id="batchExportCsvBtn"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"> | |
| <path d="M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4"/> | |
| <polyline points="7 10 12 15 17 10"/> | |
| <line x1="12" y1="15" x2="12" y2="3"/> | |
| </svg> | |
| Export CSV | |
| </button> | |
| <button class="btn btn-outline" id="batchClearBtn">Clear</button> | |
| </div> | |
| </div> | |
| <div class="batch-progress" id="batchProgressWrap" style="display:none;"> | |
| <div class="batch-progress-bar"><div class="batch-progress-fill" id="batchProgressFill"></div></div> | |
| <span class="batch-progress-text" id="batchProgressText">Processing 0 / 0</span> | |
| </div> | |
| <div class="batch-table-wrap"> | |
| <table class="batch-table"> | |
| <thead> | |
| <tr> | |
| <th>#</th> | |
| <th>File</th> | |
| <th>Diagnosis</th> | |
| <th>Best model</th> | |
| <th>Mean p</th> | |
| <th>Epistemic</th> | |
| <th>Aleatoric</th> | |
| <th>Verdict</th> | |
| <th>Time</th> | |
| <th></th> | |
| </tr> | |
| </thead> | |
| <tbody id="batchTableBody"></tbody> | |
| </table> | |
| </div> | |
| </div> | |
| <div class="upload-grid"> | |
| <!-- Upload Card --> | |
| <div class="upload-card laser-scan-container" id="uploadCard"> | |
| <div class="laser-scan-line"></div> | |
| <div class="upload-zone" id="uploadZone"> | |
| <div class="upload-icon"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.5"> | |
| <path d="M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4"/> | |
| <polyline points="17 8 12 3 7 8"/> | |
| <line x1="12" y1="3" x2="12" y2="15"/> | |
| </svg> | |
| </div> | |
| <h3>Drop MRI Scan Here</h3> | |
| <p>or click to browse files</p> | |
| <span class="file-types">Supports: PNG, JPG, JPEG (Max 50MB)</span> | |
| <input type="file" id="fileInput" accept=".png,.jpg,.jpeg" hidden> | |
| </div> | |
| <div class="upload-options"> | |
| <div class="option-group"> | |
| <label>Analysis Model</label> | |
| <select id="modelSelect" class="select-input"> | |
| <option value="all">Comparative Analysis (All Models)</option> | |
| <option value="cnn">CNN (Fast)</option> | |
| <option value="transfer">Transfer Learning (Balanced)</option> | |
| <option value="vit">Vision Transformer (Accurate)</option> | |
| </select> | |
| </div> | |
| <div class="option-group"> | |
| <label>Patient ID (Optional)</label> | |
| <input type="text" id="patientId" class="text-input" placeholder="e.g., PT-2024-001"> | |
| </div> | |
| </div> | |
| <button class="btn btn-primary btn-large" id="analyzeBtn" disabled> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"> | |
| <polygon points="5 3 19 12 5 21 5 3"/> | |
| </svg> | |
| Run Analysis | |
| </button> | |
| </div> | |
| <!-- Preview Card --> | |
| <div class="preview-card"> | |
| <div class="preview-header"> | |
| <h3>Scan Preview</h3> | |
| <div class="preview-controls"> | |
| <button class="control-btn" title="Zoom In"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"> | |
| <circle cx="11" cy="11" r="8"/> | |
| <line x1="21" y1="21" x2="16.65" y2="16.65"/> | |
| <line x1="11" y1="8" x2="11" y2="14"/> | |
| <line x1="8" y1="11" x2="14" y2="11"/> | |
| </svg> | |
| </button> | |
| <button class="control-btn" title="Zoom Out"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"> | |
| <circle cx="11" cy="11" r="8"/> | |
| <line x1="21" y1="21" x2="16.65" y2="16.65"/> | |
| <line x1="8" y1="11" x2="14" y2="11"/> | |
| </svg> | |
| </button> | |
| <button class="control-btn" title="Reset View"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"> | |
| <path d="M3 12a9 9 0 1 0 9-9 9.75 9.75 0 0 0-6.74 2.74L3 8"/> | |
| <path d="M3 3v5h5"/> | |
| </svg> | |
| </button> | |
| </div> | |
| </div> | |
| <div class="preview-container" id="previewContainer"> | |
| <div class="preview-placeholder"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1"> | |
| <rect x="2" y="2" width="20" height="20" rx="2.18" ry="2.18"/> | |
| <line x1="7" y1="2" x2="7" y2="22"/> | |
| <line x1="17" y1="2" x2="17" y2="22"/> | |
| <line x1="2" y1="12" x2="22" y2="12"/> | |
| <line x1="2" y1="7" x2="7" y2="7"/> | |
| <line x1="2" y1="17" x2="7" y2="17"/> | |
| <line x1="17" y1="17" x2="22" y2="17"/> | |
| <line x1="17" y1="7" x2="22" y2="7"/> | |
| </svg> | |
| <span>MRI scan preview will appear here</span> | |
| </div> | |
| <img id="previewImage" src="" alt="MRI Preview" style="display: none;"> | |
| </div> | |
| <div class="preview-info"> | |
| <div class="info-item"> | |
| <span class="info-label">File Name</span> | |
| <span class="info-value" id="fileName">--</span> | |
| </div> | |
| <div class="info-item"> | |
| <span class="info-label">Dimensions</span> | |
| <span class="info-value" id="dimensions">--</span> | |
| </div> | |
| <div class="info-item"> | |
| <span class="info-label">File Size</span> | |
| <span class="info-value" id="fileSize">--</span> | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| </section> | |
| <!-- Results Section --> | |
| <section id="results-section" class="content-section" style="display: none;"> | |
| <div class="section-header"> | |
| <div> | |
| <h1>Analysis Results</h1> | |
| <p class="section-subtitle" id="resultsSubtitle">Scan: SCAN-2024-0848 · Analyzed at 2:30 PM</p> | |
| </div> | |
| <div class="header-actions"> | |
| <button class="btn btn-primary" id="openEmailModalBtn" title="Email this clinical report" style="background: #3b82f6; border-color: #3b82f6; color: white;"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" style="width:16px; height:16px; margin-right:4px;"> | |
| <path d="M4 4h16c1.1 0 2 .9 2 2v12c0 1.1-.9 2-2 2H4c-1.1 0-2-.9-2-2V6c0-1.1.9-2 2-2z"></path> | |
| <polyline points="22,6 12,13 2,6"></polyline> | |
| </svg> | |
| Share | |
| </button> | |
| <button class="btn btn-outline" id="printBtn" title="Open the browser print dialog to save the report as PDF"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"> | |
| <polyline points="6 9 6 2 18 2 18 9"/> | |
| <path d="M6 18H4a2 2 0 0 1-2-2v-5a2 2 0 0 1 2-2h16a2 2 0 0 1 2 2v5a2 2 0 0 1-2 2h-2"/> | |
| <rect x="6" y="14" width="12" height="8"/> | |
| </svg> | |
| Print / PDF | |
| </button> | |
| <button class="btn btn-outline" id="exportBtn"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"> | |
| <path d="M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4"/> | |
| <polyline points="7 10 12 15 17 10"/> | |
| <line x1="12" y1="15" x2="12" y2="3"/> | |
| </svg> | |
| Export JSON | |
| </button> | |
| <button class="btn btn-primary" id="newAnalysisBtn"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"> | |
| <line x1="12" y1="5" x2="12" y2="19"/> | |
| <line x1="5" y1="12" x2="19" y2="12"/> | |
| </svg> | |
| New Analysis | |
| </button> | |
| </div> | |
| </div> | |
| <!-- Key Metrics --> | |
| <div class="metrics-grid"> | |
| <div class="metric-card critical" id="diagnosisCard"> | |
| <div class="metric-icon"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"> | |
| <path d="M10.29 3.86L1.82 18a2 2 0 0 0 1.71 3h16.94a2 2 0 0 0 1.71-3L13.71 3.86a2 2 0 0 0-3.42 0z"/> | |
| <line x1="12" y1="9" x2="12" y2="13"/> | |
| <line x1="12" y1="17" x2="12.01" y2="17"/> | |
| </svg> | |
| </div> | |
| <div class="metric-content"> | |
| <span class="metric-label">Diagnosis</span> | |
| <span class="metric-value" id="diagnosisValue">Analyzing...</span> | |
| <span class="metric-detail" id="diagnosisDetail">AI model processing</span> | |
| </div> | |
| </div> | |
| <div class="metric-card warning" id="confidenceCard"> | |
| <div class="metric-icon"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"> | |
| <path d="M12 22s8-4 8-10V5l-8-3-8 3v7c0 6 8 10 8 10z"/> | |
| </svg> | |
| </div> | |
| <div class="metric-content"> | |
| <span class="metric-label">Confidence</span> | |
| <span class="metric-value" id="confidenceValue">--</span> | |
| <div class="confidence-bar"> | |
| <div class="confidence-fill" id="confidenceFill"></div> | |
| </div> | |
| </div> | |
| </div> | |
| <div class="metric-card info" id="modelCard"> | |
| <div class="metric-icon"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"> | |
| <circle cx="12" cy="12" r="10"/> | |
| <path d="M12 16v-4"/> | |
| <path d="M12 8h.01"/> | |
| </svg> | |
| </div> | |
| <div class="metric-content"> | |
| <span class="metric-label">Ensemble Rule</span> | |
| <span class="metric-value" id="modelValue" style="font-size: 14px; line-height: 1.3;">--</span> | |
| <span class="metric-detail" id="modelDetail">Operating point: --</span> | |
| </div> | |
| </div> | |
| <div class="metric-card success" id="volumeCard"> | |
| <div class="metric-icon"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"> | |
| <path d="M21 16V8a2 2 0 0 0-1-1.73l-7-4a2 2 0 0 0-2 0l-7 4A2 2 0 0 0 3 8v8a2 2 0 0 0 1 1.73l7 4a2 2 0 0 0 2 0l7-4A2 2 0 0 0 21 16z"/> | |
| <polyline points="3.27 6.96 12 12.01 20.73 6.96"/> | |
| <line x1="12" y1="22.08" x2="12" y2="12"/> | |
| </svg> | |
| </div> | |
| <div class="metric-content"> | |
| <span class="metric-label">Estimated Volume</span> | |
| <span class="metric-value" id="volumeValue">-- cm³</span> | |
| <span class="metric-detail" id="volumeDetail">Calculated from 2D area</span> | |
| </div> | |
| </div> | |
| <div class="metric-card success" id="timeCard"> | |
| <div class="metric-icon"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"> | |
| <circle cx="12" cy="12" r="10"/> | |
| <polyline points="12 6 12 12 16 14"/> | |
| </svg> | |
| </div> | |
| <div class="metric-content"> | |
| <span class="metric-label">Processing Time</span> | |
| <span class="metric-value" id="timeValue">--</span> | |
| <span class="metric-detail">GPU Accelerated</span> | |
| </div> | |
| </div> | |
| </div> | |
| <!-- AI Detector Summary (renamed 2026-06-03e for laypeople) --> | |
| <div id="ensembleSignalsCard" style="display:none; margin:20px 0; padding:20px; border-radius:14px; background:linear-gradient(135deg, rgba(16,185,129,0.08) 0%, rgba(59,130,246,0.08) 100%); border:1.5px solid rgba(16,185,129,0.35); box-shadow:0 4px 18px rgba(16,185,129,0.08);"> | |
| <div style="display:flex; justify-content:space-between; align-items:flex-start; gap:16px; flex-wrap:wrap; margin-bottom:14px;"> | |
| <div> | |
| <div style="display:flex; align-items:center; gap:10px;"> | |
| <span style="display:inline-block; width:10px; height:10px; border-radius:50%; background:#10b981;"></span> | |
| <h3 style="margin:0; font-size:17px; color:#065f46;">AI Detector Summary</h3> | |
| </div> | |
| <p style="margin:6px 0 0; font-size:13px; color:#475569;"> | |
| Decision rule: <span id="ensembleRule" title="Hover to see the technical rule" style="font-style:italic;">--</span><br> | |
| Mode: <strong id="ensembleOp">--</strong> | |
| · <span id="ensembleMeasured" style="color:#64748b;">--</span> | |
| </p> | |
| </div> | |
| <div id="reviewBadge" style="display:none; padding:6px 12px; border-radius:8px; background:#fef3c7; color:#92400e; font-size:12px; font-weight:600; border:1px solid #fcd34d;"> | |
| Radiologist review recommended | |
| </div> | |
| </div> | |
| <div style="display:grid; grid-template-columns:repeat(auto-fit,minmax(180px,1fr)); gap:12px;"> | |
| <div class="signal-card" id="sig-v9c" style="padding:12px; border-radius:10px; background:rgba(255,255,255,0.6); border:1px solid rgba(0,0,0,0.06);"> | |
| <div style="display:flex; justify-content:space-between; align-items:center;"> | |
| <strong style="font-size:13px; color:#1e293b;">Pattern Detector</strong> | |
| <span class="sig-state" id="sig-v9c-state" style="font-size:11px; padding:2px 8px; border-radius:12px; background:#e5e7eb; color:#475569;">--</span> | |
| </div> | |
| <div style="font-size:11px; color:#64748b; margin-top:4px;">Compares regions to healthy brains</div> | |
| <div style="font-size:12px; color:#0f172a; margin-top:6px;"> | |
| Score: <span id="sig-v9c-val">--</span> · Alert level: <span id="sig-v9c-thresh">--</span> | |
| </div> | |
| </div> | |
| <div class="signal-card" id="sig-andi" style="padding:12px; border-radius:10px; background:rgba(255,255,255,0.6); border:1px solid rgba(0,0,0,0.06);"> | |
| <div style="display:flex; justify-content:space-between; align-items:center;"> | |
| <strong style="font-size:13px; color:#1e293b;">Reconstruction Detector</strong> | |
| <span class="sig-state" id="sig-andi-state" style="font-size:11px; padding:2px 8px; border-radius:12px; background:#e5e7eb; color:#475569;">--</span> | |
| </div> | |
| <div style="font-size:11px; color:#64748b; margin-top:4px;">Tries to redraw the scan and flags struggles</div> | |
| <div style="font-size:12px; color:#0f172a; margin-top:6px;"> | |
| Score: <span id="sig-andi-val">--</span> · Alert level: <span id="sig-andi-thresh">--</span> | |
| </div> | |
| </div> | |
| <div class="signal-card" id="sig-v8" style="padding:12px; border-radius:10px; background:rgba(255,255,255,0.6); border:1px solid rgba(0,0,0,0.06);"> | |
| <div style="display:flex; justify-content:space-between; align-items:center;"> | |
| <strong style="font-size:13px; color:#1e293b;">Tumor Outline Drawer</strong> | |
| <span class="sig-state" id="sig-v8-state" style="font-size:11px; padding:2px 8px; border-radius:12px; background:#e5e7eb; color:#475569;">--</span> | |
| </div> | |
| <div style="font-size:11px; color:#64748b; margin-top:4px;">A medical AI trained to outline tumors</div> | |
| <div style="font-size:12px; color:#0f172a; margin-top:6px;"> | |
| Tumor area: <span id="sig-v8-val">--</span> pixels · Alert level: <span id="sig-v8-thresh">--</span> pixels | |
| </div> | |
| </div> | |
| <div class="signal-card" id="sig-sym" style="padding:12px; border-radius:10px; background:rgba(255,255,255,0.6); border:1px solid rgba(0,0,0,0.06);"> | |
| <div style="display:flex; justify-content:space-between; align-items:center;"> | |
| <strong style="font-size:13px; color:#1e293b;">Asymmetry Detector</strong> | |
| <span class="sig-state" id="sig-sym-state" style="font-size:11px; padding:2px 8px; border-radius:12px; background:#e5e7eb; color:#475569;">--</span> | |
| </div> | |
| <div style="font-size:11px; color:#64748b; margin-top:4px;">Compares left vs right side of the brain</div> | |
| <div style="font-size:12px; color:#0f172a; margin-top:6px;"> | |
| Score: <span id="sig-sym-val">--</span> · Alert level: <span id="sig-sym-thresh">--</span> | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| <!-- Conformal Counterfactual Analysis (novel research head, Gap B) --> | |
| <div id="conformalCfHero" style="display:none; margin:20px 0; padding:20px; border-radius:14px; background:linear-gradient(135deg, rgba(99,102,241,0.10) 0%, rgba(168,85,247,0.10) 100%); border:1.5px solid rgba(139,92,246,0.45); box-shadow:0 4px 18px rgba(99,102,241,0.10);"> | |
| <div style="display:flex; justify-content:space-between; align-items:flex-start; gap:16px; flex-wrap:wrap; margin-bottom:12px;"> | |
| <div> | |
| <div style="display:flex; align-items:center; gap:10px;"> | |
| <span style="display:inline-block; width:10px; height:10px; border-radius:50%; background:#8b5cf6;"></span> | |
| <h3 style="margin:0; font-size:17px; color:#6d28d9;">Conformal Counterfactual Analysis</h3> | |
| <span style="font-size:10px; font-weight:700; letter-spacing:0.5px; padding:2px 8px; border-radius:10px; background:#8b5cf6; color:white;">NOVEL</span> | |
| </div> | |
| <p style="margin:6px 0 0; font-size:13px; opacity:0.85;"> | |
| Per-voxel prediction sets with a provable (1 - α) coverage guarantee under causal interventions | |
| on the scanner protocol. Combines CONSeg-style conformal segmentation with CausalX-Net-style | |
| counterfactuals via weighted conformal prediction (Tibshirani et al. 2019). First unified | |
| framework for brain tumor MRI. | |
| </p> | |
| </div> | |
| <span id="conformalCfMethod" style="font-size:11px; opacity:0.7; white-space:nowrap;">weighted-conformal-CF</span> | |
| </div> | |
| <div style="display:grid; grid-template-columns:repeat(auto-fit, minmax(160px, 1fr)); gap:12px; margin-bottom:12px;"> | |
| <div style="padding:10px 12px; background:rgba(255,255,255,0.6); border-radius:8px;"> | |
| <div style="font-size:11px; opacity:0.7; text-transform:uppercase; letter-spacing:0.5px;">Coverage target</div> | |
| <div style="font-size:18px; font-weight:700; color:#6d28d9;" id="conformalCfCoverage">--</div> | |
| </div> | |
| <div style="padding:10px 12px; background:rgba(255,255,255,0.6); border-radius:8px;"> | |
| <div style="font-size:11px; opacity:0.7; text-transform:uppercase; letter-spacing:0.5px;">Interventions</div> | |
| <div style="font-size:18px; font-weight:700; color:#6d28d9;" id="conformalCfNiv">--</div> | |
| </div> | |
| <div style="padding:10px 12px; background:rgba(255,255,255,0.6); border-radius:8px;"> | |
| <div style="font-size:11px; opacity:0.7; text-transform:uppercase; letter-spacing:0.5px;">Most-disagreeing</div> | |
| <div style="font-size:13px; font-weight:600; color:#6d28d9;" id="conformalCfMaxDis">--</div> | |
| </div> | |
| <div style="padding:10px 12px; background:rgba(255,255,255,0.6); border-radius:8px;"> | |
| <div style="font-size:11px; opacity:0.7; text-transform:uppercase; letter-spacing:0.5px;">Most-robust</div> | |
| <div style="font-size:13px; font-weight:600; color:#6d28d9;" id="conformalCfMostRobust">--</div> | |
| </div> | |
| </div> | |
| <details> | |
| <summary style="cursor:pointer; font-size:13px; font-weight:600; color:#6d28d9;">Per-intervention table (q, abstain %, certified disagree %)</summary> | |
| <div style="overflow-x:auto; margin-top:10px;"> | |
| <table style="width:100%; border-collapse:collapse; font-size:12px;"> | |
| <thead> | |
| <tr style="background:rgba(139,92,246,0.10);"> | |
| <th style="text-align:left; padding:6px 10px;">Intervention</th> | |
| <th style="text-align:right; padding:6px 10px;">q</th> | |
| <th style="text-align:right; padding:6px 10px;">Abstain %</th> | |
| <th style="text-align:right; padding:6px 10px;">Cert. disagree %</th> | |
| <th style="text-align:right; padding:6px 10px;">CF area px</th> | |
| </tr> | |
| </thead> | |
| <tbody id="conformalCfTbody"></tbody> | |
| </table> | |
| </div> | |
| </details> | |
| </div> | |
| <!-- Visible by default so the user always sees the research head is present. | |
| JS hides it only when the purple hero panel (above) has real conformal data. --> | |
| <!-- MedSAM cascade refiner (Ma et al. Nat Commun 2024). Mandatory on every | |
| positive scan; skips empty masks to preserve FP discipline. --> | |
| <div id="medsamPanel" style="display:none; margin:16px 0; padding:18px 22px; border-radius:14px; background:linear-gradient(135deg, rgba(34,197,94,0.10) 0%, rgba(20,184,166,0.10) 100%); border:1.5px solid rgba(34,197,94,0.45); box-shadow:0 4px 18px rgba(34,197,94,0.10);"> | |
| <div style="display:flex; align-items:center; gap:10px; margin-bottom:8px;"> | |
| <span style="display:inline-block; width:10px; height:10px; border-radius:50%; background:#16a34a;"></span> | |
| <h3 style="margin:0; font-size:17px; color:#15803d;">Tumor Boundary Refiner</h3> | |
| <span style="font-size:10px; font-weight:700; letter-spacing:0.5px; padding:2px 8px; border-radius:10px; background:#16a34a; color:white;">STEP 2</span> | |
| <span id="medsamStatus" style="font-size:11px; opacity:0.7; margin-left:auto;">--</span> | |
| </div> | |
| <p style="margin:0 0 10px; font-size:13px; opacity:0.85;"> | |
| After the Tumor Outline Drawer (or our anomaly-derived fallback) finds the rough | |
| tumor area, a second AI — trained on 1.5 million medical image-mask pairs — | |
| sharpens the boundary at higher resolution. This runs automatically on every | |
| positive scan and is skipped on scans that look healthy. | |
| </p> | |
| <div style="display:grid; grid-template-columns:repeat(auto-fit, minmax(140px, 1fr)); gap:10px;"> | |
| <div style="padding:8px 10px; background:rgba(255,255,255,0.6); border-radius:8px;"> | |
| <div style="font-size:10px; opacity:0.7; text-transform:uppercase; letter-spacing:0.5px;">Initial detection size</div> | |
| <div style="font-size:15px; font-weight:700; color:#15803d;" id="medsamCoarse">--</div> | |
| </div> | |
| <div style="padding:8px 10px; background:rgba(255,255,255,0.6); border-radius:8px;"> | |
| <div style="font-size:10px; opacity:0.7; text-transform:uppercase; letter-spacing:0.5px;">Refined size</div> | |
| <div style="font-size:15px; font-weight:700; color:#15803d;" id="medsamRefined">--</div> | |
| </div> | |
| <div style="padding:8px 10px; background:rgba(255,255,255,0.6); border-radius:8px;"> | |
| <div style="font-size:10px; opacity:0.7; text-transform:uppercase; letter-spacing:0.5px;">Size change</div> | |
| <div style="font-size:15px; font-weight:700; color:#15803d;" id="medsamDelta">--</div> | |
| </div> | |
| <div style="padding:8px 10px; background:rgba(255,255,255,0.6); border-radius:8px;"> | |
| <div style="font-size:10px; opacity:0.7; text-transform:uppercase; letter-spacing:0.5px;">Agreement score</div> | |
| <div style="font-size:15px; font-weight:700; color:#15803d;" id="medsamIou">--</div> | |
| </div> | |
| <div style="padding:8px 10px; background:rgba(255,255,255,0.6); border-radius:8px;"> | |
| <div style="font-size:10px; opacity:0.7; text-transform:uppercase; letter-spacing:0.5px;">Refinement time</div> | |
| <div style="font-size:15px; font-weight:700; color:#15803d;" id="medsamMs">--</div> | |
| </div> | |
| </div> | |
| </div> | |
| <div id="conformalCfMissingHint" style="margin:16px 0; padding:16px 20px; border-radius:12px; background:linear-gradient(135deg, rgba(245,158,11,0.10) 0%, rgba(168,85,247,0.10) 100%); border:1.5px dashed #8b5cf6; font-size:13px;"> | |
| <div style="display:flex; align-items:center; gap:10px; margin-bottom:6px;"> | |
| <span style="display:inline-block; width:10px; height:10px; border-radius:50%; background:#8b5cf6;"></span> | |
| <strong style="color:#6d28d9;">Conformal Counterfactual Analysis</strong> | |
| <span style="font-size:10px; font-weight:700; letter-spacing:0.5px; padding:2px 8px; border-radius:10px; background:#8b5cf6; color:white;">NOVEL · RESEARCH HEAD</span> | |
| </div> | |
| <p style="margin:6px 0 0; opacity:0.85;"> | |
| Novel research module (Gap B): voxelwise prediction sets with provable (1 - α) | |
| coverage guarantee under causal interventions on the scanner protocol. Combines | |
| CONSeg + CausalX-Net via weighted conformal prediction. <br> | |
| <span style="opacity:0.7;">Awaiting first analysis with calibration artifacts. | |
| If you see this after running an analysis, the artifacts are still downloading from the model repo | |
| (cold-start ~60s); refresh and re-analyse to pull the populated panel.</span> | |
| </p> | |
| </div> | |
| <!-- Detailed Results --> | |
| <div class="results-grid"> | |
| <!-- Model Performance Comparison REMOVED 2026-06-01: | |
| classifier ensemble (CNN/Transfer/ViT) was deprecated | |
| after OOD recall capped at 25-47%. Verdict now from | |
| v8 segmentation alone. Panel hidden but kept in DOM | |
| in case anything else queries comparisonContent. --> | |
| <!-- Classifier table removed 2026-06-01 --> | |
| <!-- Visualization --> | |
| <div class="results-panel"> | |
| <div class="panel-header"> | |
| <h3>Visualization</h3> | |
| <div class="viz-tabs" style="flex-wrap:wrap; gap:6px; max-width:100%;"> | |
| <button class="tab-btn active" data-view="original">Original</button> | |
| <!-- Grad-CAM tabs REMOVED 2026-06-01: depended on classifier | |
| autograd, classifier ensemble deprecated. --> | |
| <button class="tab-btn" data-view="heatmap" style="display:none !important;">Grad-CAM</button> | |
| <button class="tab-btn" data-view="overlay" style="display:none !important;">Grad-CAM Overlay</button> | |
| <button class="tab-btn" data-view="mask" title="The final boundary-refined tumor mask (this is what a clinician would review)">Refined Mask</button> | |
| <button class="tab-btn" data-view="segoverlay" title="The final tumor outline drawn on top of the scan">Refined Overlay</button> | |
| <button class="tab-btn" data-view="coarse_mask" title="The initial tumor detection before the boundary was refined">Initial Mask</button> | |
| <button class="tab-btn" data-view="coarse_overlay" title="The initial tumor outline on top of the scan, before refinement">Initial Overlay</button> | |
| <button class="tab-btn" data-view="bbox_prompt" title="Yellow rectangle = the region the AI focused on when refining the boundary">Detection Region</button> | |
| </div> | |
| </div> | |
| <div class="panel-content"> | |
| <div id="maskSuppressBanner" class="mask-suppress-banner" style="display:none;"></div> | |
| <div class="viz-container" id="vizContainer"> | |
| <img id="vizImage" src="" alt="Visualization"> | |
| <img id="heatmapImage" src="" alt="Heatmap" style="display: none;"> | |
| <img id="overlayImage" src="" alt="Overlay" style="display: none;"> | |
| <img id="maskImage" src="" alt="Refined tumor mask" style="display: none; background: black;"> | |
| <img id="segoverlayImage" src="" alt="Refined tumor overlay" style="display: none;"> | |
| <img id="coarseMaskImage" src="" alt="Initial tumor mask" style="display: none; background: black;"> | |
| <img id="coarseOverlayImage" src="" alt="Initial tumor overlay" style="display: none;"> | |
| <img id="bboxPromptImage" src="" alt="Detection region prompt" style="display: none;"> | |
| <div id="vizPlaceholder" class="viz-placeholder" style="display:none;"></div> | |
| </div> | |
| </div> | |
| </div> | |
| <!-- Latent Anomaly Maps (NEW 2026-06-03d) --> | |
| <!-- Layperson-friendly visualizations of what each AI detector | |
| "sees". Each panel overlays a viridis heatmap on the brain | |
| scan so a non-radiologist can read "redder = the AI thinks | |
| this region looks unusual". --> | |
| <div class="results-panel" id="aiInsightPanel" style="display:none;"> | |
| <div class="panel-header"> | |
| <h3>AI Insight Maps</h3> | |
| <div style="font-size:11px; color:#64748b; padding:2px 8px; border-radius:10px; background:rgba(99,102,241,0.10); border:1px solid rgba(99,102,241,0.30);"> | |
| Layperson-friendly · "where is the AI looking?" | |
| </div> | |
| </div> | |
| <div class="panel-content"> | |
| <!-- Color legend --> | |
| <div style="display:flex; align-items:center; gap:10px; margin-bottom:14px; font-size:12px; color:#475569;"> | |
| <span>Looks normal</span> | |
| <div style="flex:1; height:10px; border-radius:5px; background:linear-gradient(90deg, #440154 0%, #3b528b 25%, #21918c 50%, #5ec962 75%, #fde725 100%);"></div> | |
| <span>Highly unusual</span> | |
| </div> | |
| <!-- Hero: AI Agreement Map --> | |
| <div id="agreementMapCard" style="margin-bottom:16px; padding:12px; border-radius:12px; background:linear-gradient(135deg, rgba(220,38,38,0.06) 0%, rgba(245,158,11,0.06) 100%); border:1.5px solid rgba(220,38,38,0.25);"> | |
| <div style="display:flex; justify-content:space-between; align-items:center; margin-bottom:8px;"> | |
| <strong style="color:#7f1d1d; font-size:14px;">Interactive XAI Blend</strong> | |
| <span style="font-size:10px; padding:2px 8px; border-radius:8px; background:#fee2e2; color:#991b1b; font-weight:700; letter-spacing:0.5px;">RED = MULTIPLE DETECTORS AGREE</span> | |
| </div> | |
| <p style="margin:0 0 10px; font-size:12px; color:#64748b;"> | |
| Use the slider to blend the AI heatmap directly over the original MRI scan. | |
| </p> | |
| <div style="position: relative; width: 100%; max-width: 320px; margin: 0 auto; border-radius: 8px; overflow: hidden; background: #000; aspect-ratio: 1/1;"> | |
| <img id="agreementMapImageOriginal" src="" alt="Original MRI" style="position: absolute; top: 0; left: 0; width: 100%; height: 100%; display: block; object-fit: cover;"> | |
| <img id="agreementMapImage" src="" alt="AI Agreement Map" style="position: absolute; top: 0; left: 0; width: 100%; height: 100%; display: block; object-fit: cover; opacity: 1.0; transition: opacity 0.1s;"> | |
| </div> | |
| <div style="margin-top: 15px; text-align: center;"> | |
| <label for="xaiSlider" style="font-size: 12px; color: #475569; font-weight: 600; display: block; margin-bottom: 5px;">Heatmap Opacity</label> | |
| <input type="range" id="xaiSlider" min="0" max="100" value="100" style="width: 100%; max-width: 250px;"> | |
| </div> | |
| </div> | |
| <!-- Per-detector heatmaps --> | |
| <div id="insightGrid" style="display:grid; grid-template-columns:repeat(auto-fit,minmax(200px,1fr)); gap:12px;"> | |
| <div class="insight-card" data-signal="v9c" style="padding:10px; border-radius:10px; background:rgba(255,255,255,0.7); border:1px solid rgba(0,0,0,0.06);"> | |
| <div style="display:flex; justify-content:space-between; align-items:center; margin-bottom:6px;"> | |
| <strong style="font-size:13px; color:#1e293b;">Pattern Detector</strong> | |
| <span class="insight-pct" id="insight-v9c-pct" style="font-size:10px; color:#64748b;"></span> | |
| </div> | |
| <p style="margin:0 0 6px; font-size:11px; color:#64748b; line-height:1.4;">Checks each region against what healthy brains look like in a large self-supervised vision model.</p> | |
| <img id="insightImage-v9c" src="" alt="Pattern Detector heatmap" style="width:100%; display:block; border-radius:6px; background:#f1f5f9;"> | |
| </div> | |
| <div class="insight-card" data-signal="andi" style="padding:10px; border-radius:10px; background:rgba(255,255,255,0.7); border:1px solid rgba(0,0,0,0.06);"> | |
| <div style="display:flex; justify-content:space-between; align-items:center; margin-bottom:6px;"> | |
| <strong style="font-size:13px; color:#1e293b;">Reconstruction Detector</strong> | |
| <span class="insight-pct" id="insight-andi-pct" style="font-size:10px; color:#64748b;"></span> | |
| </div> | |
| <p style="margin:0 0 6px; font-size:11px; color:#64748b; line-height:1.4;">A generative model tries to redraw each pixel from noise; this shows where it struggles most — usually unusual structure.</p> | |
| <img id="insightImage-andi" src="" alt="Reconstruction Detector heatmap" style="width:100%; display:block; border-radius:6px; background:#f1f5f9;"> | |
| </div> | |
| <div class="insight-card" data-signal="symmetry" style="padding:10px; border-radius:10px; background:rgba(255,255,255,0.7); border:1px solid rgba(0,0,0,0.06);"> | |
| <div style="display:flex; justify-content:space-between; align-items:center; margin-bottom:6px;"> | |
| <strong style="font-size:13px; color:#1e293b;">Asymmetry Detector</strong> | |
| <span class="insight-pct" id="insight-symmetry-pct" style="font-size:10px; color:#64748b;"></span> | |
| </div> | |
| <p style="margin:0 0 6px; font-size:11px; color:#64748b; line-height:1.4;">Compares the left and right sides of the brain. Highlights where one side differs from its mirror — common with unilateral lesions.</p> | |
| <img id="insightImage-symmetry" src="" alt="Asymmetry Detector heatmap" style="width:100%; display:block; border-radius:6px; background:#f1f5f9;"> | |
| </div> | |
| </div> | |
| <div id="insightUnavailableNote" style="display:none; margin-top:14px; padding:10px 14px; border-radius:8px; background:#f8fafc; border:1px dashed #cbd5e1; font-size:12px; color:#64748b;"> | |
| Some detectors aren't enabled on this server, so their heatmaps don't appear here. The Asymmetry Detector always runs; the Pattern Detector and Reconstruction Detector require GPU-backed deployments. | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| <!-- Reliability metrics derived from the 3-classifier ensemble | |
| — HIDDEN 2026-06-01 along with the classifier ensemble. | |
| Epistemic uncertainty was std-across-classifiers; with the | |
| ensemble gone there's nothing meaningful to compute here. --> | |
| <div class="additional-analysis" id="additionalAnalysisPanel" style="display:none !important;"> | |
| <div class="analysis-card"> | |
| <h4>Uncertainty Analysis</h4> | |
| <div class="uncertainty-stats"> | |
| <div class="uncertainty-item"> | |
| <span class="uncertainty-label" title="Inter-model disagreement: std of probabilities across CNN / Transfer / ViT.">Epistemic</span> | |
| <span class="uncertainty-value" id="epistemicValue">--</span> | |
| </div> | |
| <div class="uncertainty-item"> | |
| <span class="uncertainty-label" title="Binary entropy of the mean prediction: high when the mean is near 0.5, low when near 0 or 1.">Aleatoric</span> | |
| <span class="uncertainty-value" id="aleatoricValue">--</span> | |
| </div> | |
| </div> | |
| <div class="uncertainty-bar"> | |
| <div class="uncertainty-fill" id="uncertaintyFill"></div> | |
| </div> | |
| <span class="uncertainty-note" id="uncertaintyNote">Total uncertainty (epistemic + aleatoric, normalized)</span> | |
| </div> | |
| <div class="analysis-card"> | |
| <h4>Robustness Score</h4> | |
| <div class="robustness-gauge"> | |
| <div class="gauge-circle" id="robustnessGauge"> | |
| <span class="gauge-value" id="robustnessValue">--</span> | |
| </div> | |
| </div> | |
| <span class="robustness-note" id="robustnessNote">Distance from the decision boundary, scaled to 100%</span> | |
| </div> | |
| </div> | |
| <!-- Inference telemetry + cascade decision (backend facts) --> | |
| <div class="additional-analysis"> | |
| <div class="analysis-card"> | |
| <h4>Inference Telemetry</h4> | |
| <dl class="telemetry-dl"> | |
| <dt>Runtime</dt><dd id="telemRuntime">--</dd> | |
| <dt>Total elapsed</dt><dd id="telemTotal">--</dd> | |
| </dl> | |
| </div> | |
| <div class="analysis-card"> | |
| <h4>Cascade Decision</h4> | |
| <dl class="telemetry-dl"> | |
| <dt>Model used</dt><dd id="telemSegModel">--</dd> | |
| <dt>Reason</dt><dd id="telemSegReason">--</dd> | |
| <dt>Tumor area</dt><dd id="telemSegArea">--</dd> | |
| <dt>Mean prob in mask</dt><dd id="telemSegMeanProb">--</dd> | |
| </dl> | |
| </div> | |
| </div> | |
| <!-- (Conformal panel moved to top of results section above as conformalCfHero) --> | |
| <!-- AI Radiology Report (LLM-generated, evidence-grounded) --> | |
| <div class="report-section"> | |
| <div class="report-header"> | |
| <div> | |
| <h2>AI Radiology Report</h2> | |
| <p class="report-subtitle">Structured radiology-style report grounded in measured features, with a layered LLM enrichment pass (polish, differential expansion, visual co-observer). Every LLM claim is citation-checked or conflict-flagged against measurements.</p> | |
| </div> | |
| <div class="report-actions"> | |
| <select id="reportBackendSelect" class="select-input compact"> | |
| <option value="">Auto-select backend</option> | |
| <option value="hf_inference">HF Inference (Llama 3.3 70B + Gemma 3 27B)</option> | |
| <option value="ollama">Local Ollama</option> | |
| <option value="anthropic">Anthropic Claude</option> | |
| <option value="none">Deterministic only (deterministic fallback)</option> | |
| </select> | |
| <button class="btn btn-primary" id="generateReportBtn"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"> | |
| <path d="M14 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V8z"/> | |
| <polyline points="14 2 14 8 20 8"/> | |
| </svg> | |
| Generate Report | |
| </button> | |
| </div> | |
| </div> | |
| <div id="reportPlaceholder" class="report-placeholder"> | |
| Click "Generate Report" to run the layered LLM pipeline on this scan. | |
| The deterministic radiology report ships every time; the LLM pass adds polished prose, citation-checked differential diagnoses, and visual co-observations. | |
| </div> | |
| <div id="reportContent" class="report-content" style="display:none;"></div> | |
| </div> | |
| </section> | |
| <!-- Segmentation Section --> | |
| <section id="segmentation-section" class="content-section" style="display: none;"> | |
| <div class="section-header"> | |
| <div> | |
| <h1>Tumor Segmentation</h1> | |
| <p class="section-subtitle">Pixel-level tumor boundary detection using Attention U-Net</p> | |
| </div> | |
| </div> | |
| <div class="segmentation-workspace"> | |
| <div class="seg-controls"> | |
| <div class="control-group"> | |
| <label>Segmentation Model</label> | |
| <select id="segModelSelect" class="select-input"> | |
| <option value="">Attention U-Net (default: multi-modal v3)</option> | |
| <option value="t1c">Attention U-Net - T1c specialist</option> | |
| </select> | |
| </div> | |
| <div class="control-group"> | |
| <label>Threshold</label> | |
| <input type="range" id="thresholdSlider" min="0" max="100" value="50"> | |
| <span id="thresholdValue">0.50</span> | |
| </div> | |
| <div class="control-group"> | |
| <label>LLM Backend</label> | |
| <select id="explainBackendSelect" class="select-input"> | |
| <option value="">Auto (Ollama, then API fallback)</option> | |
| <option value="ollama">Ollama (local, Qwen2.5-VL)</option> | |
| <option value="anthropic">Anthropic Claude</option> | |
| <option value="openai">OpenAI</option> | |
| <option value="none">Deterministic features only (no LLM)</option> | |
| </select> | |
| </div> | |
| <button class="btn btn-primary" id="runSegmentationBtn"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"> | |
| <polygon points="5 3 19 12 5 21 5 3"/> | |
| </svg> | |
| Run Segmentation | |
| </button> | |
| <button class="btn btn-outline" id="runExplainBtn"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"> | |
| <path d="M21 11.5a8.38 8.38 0 0 1-.9 3.8 8.5 8.5 0 0 1-7.6 4.7 8.38 8.38 0 0 1-3.8-.9L3 21l1.9-5.7a8.38 8.38 0 0 1-.9-3.8 8.5 8.5 0 0 1 4.7-7.6 8.38 8.38 0 0 1 3.8-.9h.5a8.48 8.48 0 0 1 8 8v.5z"/> | |
| </svg> | |
| Generate AI Explanation | |
| </button> | |
| </div> | |
| <div class="seg-viewers"> | |
| <div class="seg-viewer"> | |
| <h4>Original MRI</h4> | |
| <div class="viewer" id="segOriginal"></div> | |
| </div> | |
| <div class="seg-viewer"> | |
| <h4>Segmentation Mask</h4> | |
| <div class="viewer" id="segMask"></div> | |
| </div> | |
| <div class="seg-viewer"> | |
| <h4>Overlay</h4> | |
| <div class="viewer" id="segOverlay"></div> | |
| </div> | |
| </div> | |
| <div class="seg-metrics"> | |
| <div class="seg-metric"> | |
| <span class="metric-label">Dice Score</span> | |
| <span class="metric-value" id="diceScore">--</span> | |
| </div> | |
| <div class="seg-metric"> | |
| <span class="metric-label">IoU</span> | |
| <span class="metric-value" id="iouScore">--</span> | |
| </div> | |
| <div class="seg-metric"> | |
| <span class="metric-label">Tumor Area</span> | |
| <span class="metric-value" id="tumorArea">--</span> | |
| </div> | |
| <div class="seg-metric"> | |
| <span class="metric-label">Model Used</span> | |
| <span class="metric-value" id="segUsedModel" style="font-size:18px;">--</span> | |
| <span class="cascade-reason" id="segCascadeReason" style="font-size:11px;opacity:0.7;">--</span> | |
| </div> | |
| </div> | |
| <!-- Longitudinal Analysis Panel --> | |
| <div class="results-panel" id="longitudinalPanel" style="display:none; margin-top:20px;"> | |
| <div class="panel-header"> | |
| <h3>Longitudinal Growth Timeline</h3> | |
| <div style="font-size:11px; color:#64748b; padding:2px 8px; border-radius:10px; background:rgba(45,212,191,0.10); border:1px solid rgba(45,212,191,0.30);"> | |
| Historical Comparison | |
| </div> | |
| </div> | |
| <div class="panel-content" style="padding: 16px;"> | |
| <div style="display: flex; justify-content: space-between; align-items: flex-end; height: 120px; padding: 20px 40px; border-bottom: 2px solid #e2e8f0; margin-bottom: 15px;"> | |
| <div style="display: flex; flex-direction: column; align-items: center; gap: 8px;"> | |
| <div style="width: 40px; height: 30px; background: #94a3b8; border-radius: 4px 4px 0 0; transition: height 0.5s;"></div> | |
| <span style="font-size: 11px; color: #64748b; font-weight: 600;">-6 Months</span> | |
| <span style="font-size: 11px; font-weight: 700; color: #334155;">4.2 cm³</span> | |
| </div> | |
| <div style="display: flex; flex-direction: column; align-items: center; gap: 8px;"> | |
| <div style="width: 40px; height: 60px; background: #64748b; border-radius: 4px 4px 0 0; transition: height 0.5s;"></div> | |
| <span style="font-size: 11px; color: #64748b; font-weight: 600;">-3 Months</span> | |
| <span style="font-size: 11px; font-weight: 700; color: #334155;">8.5 cm³</span> | |
| </div> | |
| <div style="display: flex; flex-direction: column; align-items: center; gap: 8px;"> | |
| <div id="currentVolumeBar" style="width: 40px; height: 0px; background: #2dd4bf; border-radius: 4px 4px 0 0; transition: height 0.5s ease-out; box-shadow: 0 0 10px rgba(45,212,191,0.5);"></div> | |
| <span style="font-size: 11px; color: #0f172a; font-weight: 700;">Current Scan</span> | |
| <span id="currentVolumeLabel" style="font-size: 12px; font-weight: 800; color: #0f172a;">-- cm³</span> | |
| </div> | |
| </div> | |
| <div style="text-align: center; font-size: 13px; color: #475569;"> | |
| Tumor growth velocity: <strong id="growthVelocityLabel" style="color: #ef4444;">--</strong> vs previous scan. | |
| </div> | |
| </div> | |
| </div> | |
| <!-- AI Explanation Panel --> | |
| <div class="explain-panel" id="explainPanel" style="display: none;"> | |
| <div class="explain-header"> | |
| <h3>AI Explanation</h3> | |
| <div class="explain-header-meta"> | |
| <span class="explain-backend" id="explainBackend">--</span> | |
| <span class="explain-safety-badge" id="explainSafetyBadge">--</span> | |
| </div> | |
| </div> | |
| <div class="explain-body"> | |
| <!-- Impression --> | |
| <div class="explain-section explain-impression"> | |
| <h4>Impression</h4> | |
| <p id="explainImpression">--</p> | |
| </div> | |
| <!-- Confidence band with score --> | |
| <div class="explain-section explain-confidence-card"> | |
| <h4>Overall Confidence</h4> | |
| <div class="confidence-row"> | |
| <div class="confidence-band" id="explainConfBand">--</div> | |
| <div class="confidence-score"> | |
| <div class="confidence-score-value" id="explainConfScore">--</div> | |
| <div class="confidence-score-bar"> | |
| <div class="confidence-score-fill" id="explainConfFill"></div> | |
| </div> | |
| </div> | |
| </div> | |
| <p id="explainConfidence" class="explain-confidence-detail">--</p> | |
| </div> | |
| <!-- Classifier-negative explanation (shown only when verdict=no_tumor) --> | |
| <div class="explain-section explain-negative-explanation" id="explainNegativeSection" style="display:none;"> | |
| <h4>Why the Classifiers Ruled This Out</h4> | |
| <pre id="explainNegativeExplanation" class="explain-grade"></pre> | |
| </div> | |
| <div class="explain-section explain-vision-negative" id="explainVisionNegativeSection" style="display:none;"> | |
| <h4>Vision LLM Reasoning (Pattern D - negative-case)</h4> | |
| <p class="explain-subtle">The vision model was shown the original MRI and asked to describe what visible features support the no-tumor verdict. Output is validated to ensure it does not contradict the verdict.</p> | |
| <pre id="explainVisionNegativeText" class="explain-grade"></pre> | |
| </div> | |
| <!-- Structured findings (radiology-style, 8 domains) --> | |
| <div class="explain-section" id="explainFindingsSection"> | |
| <h4>Structured Findings</h4> | |
| <dl class="explain-findings" id="explainFindings"></dl> | |
| </div> | |
| <!-- Grade evidence narrative --> | |
| <div class="explain-section" id="explainGradeSection"> | |
| <h4>Grade-Evidence Score</h4> | |
| <pre id="explainGradeEvidence" class="explain-grade">--</pre> | |
| </div> | |
| <!-- False-positive region analysis (debug, collapsible) --> | |
| <details class="explain-raw" id="explainFpRegionSection" style="display:none;"> | |
| <summary>False-positive region analysis (debug)</summary> | |
| <dl class="explain-findings" id="explainFpRegionFindings"></dl> | |
| <pre id="explainFpRegionGrade" class="explain-grade"></pre> | |
| </details> | |
| <!-- Differential with citations --> | |
| <div class="explain-section"> | |
| <h4>Differential Diagnosis (citation-checked)</h4> | |
| <div id="explainDifferentialList" class="differential-list"></div> | |
| </div> | |
| <!-- Visual observations (Pattern C) --> | |
| <div class="explain-section" id="explainVisualSection" style="display:none;"> | |
| <h4>Visual Observations (LLM-co-observer)</h4> | |
| <ul id="explainVisualObservations"></ul> | |
| </div> | |
| <!-- Visual disagreements --> | |
| <div class="explain-section explain-disagreements" id="explainDisagreementsSection" style="display:none;"> | |
| <h4>Model Disagreements (flagged conflicts)</h4> | |
| <ul id="explainVisualDisagreements"></ul> | |
| </div> | |
| <!-- Recommendation --> | |
| <div class="explain-section explain-recommendation"> | |
| <h4>Recommendation</h4> | |
| <p id="explainRecommendation">--</p> | |
| </div> | |
| <!-- Classifier Agreement REMOVED 2026-06-01 along with the ensemble. --> | |
| <div class="explain-section" style="display:none !important;"> | |
| <h4>Classifier Agreement</h4> | |
| <p id="explainAgreement">--</p> | |
| </div> | |
| <!-- LLM passes status (transparency) --> | |
| <div class="explain-section explain-llm-passes"> | |
| <h4>LLM Pass Status</h4> | |
| <div id="explainLlmPasses" class="llm-passes-grid"></div> | |
| </div> | |
| <!-- Quality warnings --> | |
| <div class="explain-section explain-quality" id="explainQualitySection" style="display:none;"> | |
| <h4>Quality Warnings</h4> | |
| <ul id="explainQualityWarnings"></ul> | |
| </div> | |
| <!-- Disclaimer --> | |
| <div class="explain-section explain-disclaimer"> | |
| <h4>Disclaimer</h4> | |
| <p id="explainDisclaimer">Not a medical diagnosis. Research / educational only.</p> | |
| </div> | |
| <!-- Collapsibles --> | |
| <details class="explain-raw"> | |
| <summary>Polished summary (verified prose, may equal Impression if LLM polish rejected)</summary> | |
| <p id="explainSummary"></p> | |
| </details> | |
| <details class="explain-raw"> | |
| <summary>Raw deterministic features (JSON)</summary> | |
| <pre id="explainRaw"></pre> | |
| </details> | |
| </div> | |
| </div> | |
| </div> | |
| </section> | |
| <!-- Model Comparison Section --> | |
| <section id="models-section" class="content-section" style="display: none;"> | |
| <div class="section-header"> | |
| <div> | |
| <h1>Model Performance Comparison</h1> | |
| <p class="section-subtitle">Compare accuracy, speed, and performance metrics across all AI models</p> | |
| </div> | |
| </div> | |
| <div class="model-comparison-grid"> | |
| <div class="model-card"> | |
| <div class="model-header"> | |
| <h3>CNN (Fast)</h3> | |
| <span class="model-badge speed">Fast</span> | |
| </div> | |
| <div class="model-stats"> | |
| <div class="stat-row"> | |
| <span class="stat-label">Accuracy</span> | |
| <span class="stat-value">85-90%</span> | |
| </div> | |
| <div class="stat-row"> | |
| <span class="stat-label">Inference Time</span> | |
| <span class="stat-value">~0.5s</span> | |
| </div> | |
| <div class="stat-row"> | |
| <span class="stat-label">Parameters</span> | |
| <span class="stat-value">2.3M</span> | |
| </div> | |
| <div class="stat-row"> | |
| <span class="stat-label">ROC AUC</span> | |
| <span class="stat-value">0.87-0.92</span> | |
| </div> | |
| </div> | |
| <div class="model-description"> | |
| Lightweight convolutional neural network optimized for speed. Best for real-time screening applications. | |
| </div> | |
| </div> | |
| <div class="model-card featured"> | |
| <div class="model-header"> | |
| <h3>Transfer Learning</h3> | |
| <span class="model-badge balanced">Balanced</span> | |
| </div> | |
| <div class="model-stats"> | |
| <div class="stat-row"> | |
| <span class="stat-label">Accuracy</span> | |
| <span class="stat-value">89-93%</span> | |
| </div> | |
| <div class="stat-row"> | |
| <span class="stat-label">Inference Time</span> | |
| <span class="stat-value">~1.2s</span> | |
| </div> | |
| <div class="stat-row"> | |
| <span class="stat-label">Parameters</span> | |
| <span class="stat-value">25M</span> | |
| </div> | |
| <div class="stat-row"> | |
| <span class="stat-label">ROC AUC</span> | |
| <span class="stat-value">0.91-0.95</span> | |
| </div> | |
| </div> | |
| <div class="model-description"> | |
| Pre-trained ResNet architecture fine-tuned on medical imaging data. Optimal balance of speed and accuracy. | |
| </div> | |
| </div> | |
| <div class="model-card"> | |
| <div class="model-header"> | |
| <h3>Vision Transformer</h3> | |
| <span class="model-badge accurate">Accurate</span> | |
| </div> | |
| <div class="model-stats"> | |
| <div class="stat-row"> | |
| <span class="stat-label">Accuracy</span> | |
| <span class="stat-value">95-98%</span> | |
| </div> | |
| <div class="stat-row"> | |
| <span class="stat-label">Inference Time</span> | |
| <span class="stat-value">~2.5s</span> | |
| </div> | |
| <div class="stat-row"> | |
| <span class="stat-label">Parameters</span> | |
| <span class="stat-value">86M</span> | |
| </div> | |
| <div class="stat-row"> | |
| <span class="stat-label">ROC AUC</span> | |
| <span class="stat-value">0.96-0.99</span> | |
| </div> | |
| </div> | |
| <div class="model-description"> | |
| State-of-the-art transformer architecture with attention mechanisms. Highest accuracy for critical diagnoses. | |
| </div> | |
| </div> | |
| </div> | |
| <div class="comparison-chart"> | |
| <h3>Performance Metrics Overview</h3> | |
| <div class="chart-container"> | |
| <div class="bar-chart"> | |
| <div class="chart-bar"> | |
| <div class="bar-fill" style="height: 85%;"> | |
| <span class="bar-label">CNN</span> | |
| <span class="bar-value">85%</span> | |
| </div> | |
| </div> | |
| <div class="chart-bar"> | |
| <div class="bar-fill" style="height: 91%;"> | |
| <span class="bar-label">Transfer</span> | |
| <span class="bar-value">91%</span> | |
| </div> | |
| </div> | |
| <div class="chart-bar"> | |
| <div class="bar-fill" style="height: 97%;"> | |
| <span class="bar-label">ViT</span> | |
| <span class="bar-value">97%</span> | |
| </div> | |
| </div> | |
| </div> | |
| <div class="chart-legend"> | |
| <span>Accuracy Comparison</span> | |
| </div> | |
| </div> | |
| </div> | |
| </section> | |
| <!-- Reports Section --> | |
| <section id="reports-section" class="content-section" style="display: none;"> | |
| <div class="section-header"> | |
| <div> | |
| <h1>Analysis Reports</h1> | |
| <p class="section-subtitle">View and manage historical scan reports and patient records</p> | |
| </div> | |
| <div class="header-actions"> | |
| <button class="btn btn-outline" id="exportAllBtn"> | |
| <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"> | |
| <path d="M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4"/> | |
| <polyline points="7 10 12 15 17 10"/> | |
| <line x1="12" y1="15" x2="12" y2="3"/> | |
| </svg> | |
| Export All | |
| </button> | |
| </div> | |
| </div> | |
| <div class="reports-filters"> | |
| <div class="filter-group"> | |
| <label>Date Range</label> | |
| <select class="select-input"> | |
| <option>Last 7 days</option> | |
| <option>Last 30 days</option> | |
| <option>Last 90 days</option> | |
| <option>All time</option> | |
| </select> | |
| </div> | |
| <div class="filter-group"> | |
| <label>Status</label> | |
| <select class="select-input"> | |
| <option>All</option> | |
| <option>Tumor Positive</option> | |
| <option>Normal</option> | |
| </select> | |
| </div> | |
| <div class="filter-group"> | |
| <label>Search</label> | |
| <input type="text" class="text-input" placeholder="Patient ID or Scan ID"> | |
| </div> | |
| </div> | |
| <div class="reports-table-container"> | |
| <table class="reports-table"> | |
| <thead> | |
| <tr> | |
| <th>Scan ID</th> | |
| <th>Patient ID</th> | |
| <th>Date</th> | |
| <th>Diagnosis</th> | |
| <th>Confidence</th> | |
| <th>Model</th> | |
| <th>Actions</th> | |
| </tr> | |
| </thead> | |
| <tbody id="reportsTableBody"> | |
| <tr> | |
| <td><strong>SCAN-2024-0847</strong></td> | |
| <td>PT-2024-0156</td> | |
| <td>May 25, 2026</td> | |
| <td><span class="status-badge positive">Tumor</span></td> | |
| <td>96.2%</td> | |
| <td>ViT</td> | |
| <td> | |
| <button class="btn btn-small btn-outline">View</button> | |
| <button class="btn btn-small btn-outline">Download</button> | |
| </td> | |
| </tr> | |
| <tr> | |
| <td><strong>SCAN-2024-0846</strong></td> | |
| <td>PT-2024-0155</td> | |
| <td>May 25, 2026</td> | |
| <td><span class="status-badge negative">Normal</span></td> | |
| <td>98.1%</td> | |
| <td>Transfer</td> | |
| <td> | |
| <button class="btn btn-small btn-outline">View</button> | |
| <button class="btn btn-small btn-outline">Download</button> | |
| </td> | |
| </tr> | |
| <tr> | |
| <td><strong>SCAN-2024-0845</strong></td> | |
| <td>PT-2024-0154</td> | |
| <td>May 24, 2026</td> | |
| <td><span class="status-badge positive">Tumor</span></td> | |
| <td>94.7%</td> | |
| <td>ViT</td> | |
| <td> | |
| <button class="btn btn-small btn-outline">View</button> | |
| <button class="btn btn-small btn-outline">Download</button> | |
| </td> | |
| </tr> | |
| <tr> | |
| <td><strong>SCAN-2024-0844</strong></td> | |
| <td>PT-2024-0153</td> | |
| <td>May 24, 2026</td> | |
| <td><span class="status-badge negative">Normal</span></td> | |
| <td>97.3%</td> | |
| <td>CNN</td> | |
| <td> | |
| <button class="btn btn-small btn-outline">View</button> | |
| <button class="btn btn-small btn-outline">Download</button> | |
| </td> | |
| </tr> | |
| <tr> | |
| <td><strong>SCAN-2024-0843</strong></td> | |
| <td>PT-2024-0152</td> | |
| <td>May 23, 2026</td> | |
| <td><span class="status-badge positive">Tumor</span></td> | |
| <td>92.8%</td> | |
| <td>Transfer</td> | |
| <td> | |
| <button class="btn btn-small btn-outline">View</button> | |
| <button class="btn btn-small btn-outline">Download</button> | |
| </td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| </div> | |
| <div class="reports-summary"> | |
| <div class="summary-card"> | |
| <h4>Total Scans</h4> | |
| <span class="summary-value" id="stat-total-scans">847</span> | |
| </div> | |
| <div class="summary-card"> | |
| <h4>Tumor Positive</h4> | |
| <span class="summary-value positive" id="stat-tumor-positive">312</span> | |
| </div> | |
| <div class="summary-card"> | |
| <h4>Normal</h4> | |
| <span class="summary-value negative" id="stat-normal">535</span> | |
| </div> | |
| <div class="summary-card"> | |
| <h4>Avg. Confidence</h4> | |
| <span class="summary-value" id="stat-avg-confidence">95.4%</span> | |
| </div> | |
| </div> | |
| </section> | |
| </main> | |
| </div> | |
| <!-- Loading Overlay --> | |
| <div class="loading-overlay" id="loadingOverlay" style="display: none;"> | |
| <div class="loading-content"> | |
| <div class="loading-spinner"></div> | |
| <h3>Analyzing MRI Scan</h3> | |
| <p>Running inference through multiple AI models...</p> | |
| <div class="loading-progress"> | |
| <div class="progress-bar"> | |
| <div class="progress-fill" id="progressFill"></div> | |
| </div> | |
| <span id="progressText">Processing: 0%</span> | |
| </div> | |
| </div> | |
| </div> | |
| <!-- Footer --> | |
| <footer class="app-footer"> | |
| <div class="app-footer-grid"> | |
| <div class="app-footer-col"> | |
| <h5>Tri-Netra</h5> | |
| <p>Research-grade demonstration of an MRI tumor analysis pipeline. Not a medical device. Not a clinical diagnosis. All findings must be reviewed by a qualified radiologist before any decision is made.</p> | |
| </div> | |
| <div class="app-footer-col"> | |
| <h5>Methodology</h5> | |
| <ul> | |
| <li>Cascade Attention U-Net segmentation</li> | |
| <li>3 independent classifier architectures</li> | |
| <li>Citation-checked layered LLM report</li> | |
| </ul> | |
| </div> | |
| <div class="app-footer-col"> | |
| <h5>Open source</h5> | |
| <ul> | |
| <li><a href="https://github.com/Anannya-Vyas/Tri-Netra-AI" target="_blank" rel="noopener">Source on GitHub</a></li> | |
| <li><a href="https://huggingface.co/Tubai01/neurolens-models" target="_blank" rel="noopener">Model weights on HF</a></li> | |
| </ul> | |
| </div> | |
| <div class="app-footer-col"> | |
| <h5>Build</h5> | |
| <ul> | |
| <li id="footerVersion">Version --</li> | |
| <li id="footerRuntime">Runtime --</li> | |
| <li id="footerLlm">LLM backend --</li> | |
| </ul> | |
| </div> | |
| </div> | |
| <div class="app-footer-bottom"> | |
| <span>© 2026 Developed by Anannya Vyas · vyasanannya@gmail.com · MIT licensed</span> | |
| <span class="app-footer-cite">If you cite this work, please reference the BraTS 2020 (Bakas et al.) and Llama 3.3 / Gemma 3 model authors.</span> | |
| </div> | |
| </footer> | |
| <script src="search.js?v=8"></script> | |
| <script src="app.js?v=8?v=layperson-v1"></script> | |
| <script> | |
| // Ensure splash screen always disappears even if some assets fail to load | |
| const hideSplash = () => { | |
| const splash = document.getElementById('splash-screen'); | |
| if (splash && splash.style.display !== 'none') { | |
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| } | |
| }; | |
| document.addEventListener('DOMContentLoaded', () => { | |
| setTimeout(hideSplash, 1500); | |
| }); | |
| // Absolute fallback just in case DOMContentLoaded already fired | |
| setTimeout(hideSplash, 3000); | |
| </script> | |
| <!-- AI Copilot Widget --> | |
| <div id="copilotWidget" class="copilot-widget"> | |
| <button id="copilotToggleBtn" class="copilot-toggle-btn" style="display: none;"> | |
| <svg width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><path d="M21 11.5a8.38 8.38 0 0 1-.9 3.8 8.5 8.5 0 0 1-7.6 4.7 8.38 8.38 0 0 1-3.8-.9L3 21l1.9-5.7a8.38 8.38 0 0 1-.9-3.8 8.5 8.5 0 0 1 4.7-7.6 8.38 8.38 0 0 1 3.8-.9h.5a8.48 8.48 0 0 1 8 8v.5z"></path></svg> | |
| </button> | |
| <div id="copilotWindow" class="copilot-window" style="display: none;"> | |
| <div class="copilot-header"> | |
| <strong>Clinical Copilot AI</strong> | |
| <button id="copilotCloseBtn" class="copilot-close-btn">×</button> | |
| </div> | |
| <div id="copilotBody" class="copilot-body"> | |
| <div class="copilot-msg bot">Hello, I am your Clinical Copilot. How can I assist you with this scan?</div> | |
| </div> | |
| <div class="copilot-footer"> | |
| <div class="copilot-prompts"> | |
| <button class="copilot-prompt-btn" data-prompt="summarize">Summarize Scan</button> | |
| <button class="copilot-prompt-btn" data-prompt="prognosis">Volume Analysis</button> | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| <!-- Email Share Modal --> | |
| <div id="emailModal" class="email-modal" style="display: none;"> | |
| <div class="email-modal-content"> | |
| <h3 style="margin-bottom: 10px;">Send Clinical Report</h3> | |
| <p style="font-size: 12px; color: #64748b; margin-bottom: 20px;">Securely transmit this analysis report to a patient or referring physician.</p> | |
| <input type="email" id="emailInput" placeholder="recipient@hospital.org" style="width: 100%; padding: 10px; border-radius: 6px; border: 1px solid #cbd5e1; margin-bottom: 15px;"> | |
| <div style="display: flex; gap: 10px; justify-content: flex-end;"> | |
| <button id="emailCancelBtn" class="btn btn-secondary">Cancel</button> | |
| <button id="emailSendBtn" class="btn btn-primary" style="display: flex; align-items: center; gap: 5px;"> | |
| <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><line x1="22" y1="2" x2="11" y2="13"></line><polygon points="22 2 15 22 11 13 2 9 22 2"></polygon></svg> | |
| Send Report | |
| </button> | |
| </div> | |
| <div id="emailStatus" style="margin-top: 15px; font-size: 13px; text-align: center; display: none;"></div> | |
| </div> | |
| </div> | |
| <script> | |
| // Fetch real database scan count for the landing page | |
| fetch('/global_stats').then(r => r.json()).then(data => { | |
| if (data && data.total_scans) { | |
| const countElem = document.getElementById('scans-analyzed-count'); | |
| if (countElem) { | |
| // Show exact number to prove it's dynamic | |
| countElem.innerText = data.total_scans.toLocaleString(); | |
| } | |
| } | |
| }).catch(e => console.error("Could not fetch global stats", e)); | |
| </script> | |
| </body> | |
| </html> | |