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app.py

@@ -0,0 +1,22 @@
+import streamlit as st
+from product_similarity import page1
+
+# sidebar
+st.sidebar.title("Deep Fashion")
+st.sidebar.write(
+    "Deep Fashion, giyim ve moda sektörüne yönelik yapay zeka tabanlı çözümler sunar. ")
+
+
+PAGES = {
+    "Ürün Benzerlik Analizi": page1,
+}
+
+
+def main():
+    selection = st.sidebar.radio("Özellik seçin:", list(PAGES.keys()))
+    page = PAGES[selection]
+    page()
+
+
+if __name__ == "__main__":
+    main()

model_hayabusa.py.enc

@@ -0,0 +1 @@
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model_shogun.py.enc

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QIgSHDbcS1Uk7mYSFQSlt_GjvwR_bilqMikbBG1G1gszOnwF-0KAoqGNMMqk_vuDJS1jAes-ia4e92jdtUQ2H8gfPzahto3hWfOdHsq3z_xpoD8oFrwUedxbrMuQgQOni4V-O4lflMD7q48XLGNDX4bvvybkEzZyYJjDg2pVNgPC6rJENGZeODHVzuZcfp6Nz3HQEB7xz5ro57c9na0KLU_qyO-XnAbk3FyBL-xxYytGiJZXmsJAI8BdGJG4MWvQEGenQrXVDtOht4LkGfecV_tmHgcsltsnpZtS6sCg5YUA6r3mA1cZsECO78EJ4yQiAj8n8onhkYkohSvdJ6UxNNPDUZc104elwa7Bk6MxnHOTnO8IuqCM6UQu2QZo5lN4imN_CXxBT7AC0BgUu1Pb7TKBP_A19wHGNlHp1ACXyhRXhPJlgAZA28GYOC77umLt5_pIQaSd281VxkcL65su0VT5sBSKCUGUvQVbT-FHTfuwAU0yhHluBZtN1uYN2xLPn80jL92lIySsH2XEUJJ9CIZdbW9_UWLOWkMI349Gd9mnwQu6q3diAneHIHE5w-wDE1k_2dWiRWiOC7THlhJAFMlo5uJGTJQsXqoIGFE2vQe7TJ5PFQO0cbVn2gvopIOxk-xGTPovKhtkX2SShEN9pGJPVMjbpDgbehCd0hCAvR6oY8gE8GIHYdxmlmocPxHuqvB3E8Do7o98MEKzINIjMJEonQp8qftnSMLkdtidguCfImDV_YIq3ewn-pKRfYtXCais6t5ejp0KzJZWzC3Fzd5JE-84wgQn22U_tsmjRkDMyG9EEZpj339GFhQ4fmh6mUIwsqWERQBsxuGxKWExl3PW-vbiDumHzdvzBeIVB6jQ8ZQsv7iKelBEwP2VCJIIINPcfK5d766Ix3ek0o4bVzCKnigSufHxAwL__0wFPAr0sNWTVqXvalO6d5WHwHmcLZrGMJHxe-9Z57z4I4Mu_Sre2-LOI_bUd1v0IsStRO5CSAwXjLFYH0dR64f-CAgMu2PzLpYDEzDkmks1_nYup3x4K0F96tCAAWibjjHZ_1OmYMBgy67sZfVYEVvl-t5hemge_J5LjMWk0nzqTcLBjBkHi-Df8abU5kzayDfioksjRTdwkHTagoT-tUyEKiHWk-gUJUvJxoGq54YGg6gDdn9lHqs8oIpB9Xzx9mStvXu2d7y1EY5AoIIDAhzHmmvriDOTDA_ui45eR8o47XecwddYTcJwMhpADjGU2jEkRWijmEl8DUjJsa6-4bV26BlmluYxpbxJKoRsHxepYLqMHPIyOnReRzT0PHN8FfhyIh8KLwYVR3sUbt6mZdrJo-yqLBWCB2alqbqI_W-J2u7PyUpNOnu3M16j-kOh5j8LCRNineNvlo1V9MHGFSZ9trWV6kS3qF-MJ24C4mXpGwYj5YY8jS1ceqWz3XqAKn8VJf5NaY4PDAPqSViTTPiJGvQUBKQ9s8VMlX5v-IXbBzeHcDPYJ_7Z7K7udNyZMt9y3GIsBJ1Hp6FwcbB3XzXUm94KHeYmw3e3Vp28RvMFX1YIDG4ILoQPpO31dehmbu5BrC8KvZssMBE8rmvEpEHOe1tzPrJHF3FYbNvnACCSSY0OvP4L1AEC7uSRxgqaibn8Q6xA3P-CaTZJS6UIWyPPQ8NOF1HyaBDxb1BoXtJdvb91HXjSIwWsw44N9ViVt-_7x6X4uHeRVcU5xV1Z0tvYqZO3SG2oziIqd99yvE03rUz10uUSVZFfSYQ_ZkyyA0vdK6Vjj0OzQOHYacDVNu-lG7SiF_mLeegsmTHg95Cwzd-r31feBJQ==

model_takemura.py.enc

@@ -0,0 +1 @@
+gAAAAABmBSHDX8uufbYjHmJGrr1mxCCvKYby-438g7ZPRsB0GuDXDKuYB1sIOnOE3y5h2odH_v0fP44wXTSkTPxPzyZbbGZ85lzM6iUYPbUHYEclIDMf0mijlaKB3NibjpD1QjnW_9uFU_JmwE69gq4yRaFp2wTf-AVIyNhUHJBzIrb05W31053ot2KR_-TDbRIkwmEPE03byN9yV-C1DtCJb9_eb0NvMwlLzpnBbLr3zEhwE0oteZB25YGZFjXjXwULSCJ-edV6naA54yvLUSgusCUPIwhEK9MXwJTi2vxFxf78poJh74jjxAZI1huQqdAGeZVcYR8R_0w4vfUH1wGm3Q9g28GZBpeGykmiHehGmvvSqZlHJS10a2zF_UFw80QMyeRZ7fTtlXx3gDQxIpSQwee_221o6maYijCYodEEUP-wfNPkM3J5bc5efysTKF5JzBVc6K385vqNy2dmz1xsay_ChN3YPhZium49sY1_gXb_BOiNBy4sPFLzaQ6TWobzxfG_8qOk2ZBu5MsPvcSDlJmSlggSZ2RnbFE1BDwObneSgkIja7S8CIZi_1l1jOe5klg_xmCFFD6kSR62c7godD9Wotke6JIAbCUBu3IaypYziYDioXyIhRqzNlw-KJqErM9gQdyy3E_mHxvCi3BxD8s9_48GOUR9M9hbqk8ow1Bl5YXhnP4yEZv6_HlBt0h0I4YGO-8o4qNcbtjPKMtGQPi4fwZs3mlT5nwRbVOylmva3XjhIwbdcxcMEqEmPuRhPYQxiwz7vvxvl18I6kOc3EGvlzE66u9V2ETqAzcnJBB_xAdpyE-SDvBZKFLTPrG4PCaOBMxJBAI7edQjvB5I_hH0QWhVut_zt-JIagpgghoTSTUnRAdlimz6tXeNnlfYc3GwiRqAZmrY1JVGImveTvEX3izjETtjctaXHKA1Vr4DsavGoC4P-iSOtw9WLNA5zWy61E7BG_S1mV8WKH3aepfLnPLK0YVqyjWtw2uy8lFwWsqPHd0dZluUmy2W6ogoEVLaffAca-wau-3dIYNaWgQz8uIOkfIA3znQ2APGifFHs5Udh2wPzUu0A09b9u15pAbiHaPx-X8CGuhLFEt4iQMXfynBVsW1TnP_UqDjW1Jst3_gFE-fKKJClpb3Q6YamiMTzD2kSXWkyGpP2Qvj-Fo_8WjmLmM41cE7tEuRvHyCKylWqKVmmecCa0JcGr3Bsx6NAbij9nBc3mW6V6Xs-HaFJ0ZgN7FrEZH1HL0a3tbKGZTjuhk1-G82FuhXKe5g-TRG5y9w2U8dqCH0FJxHvphkmj_DCgz1ahGA3T2C4NogVGIEANKS2NiLr311osJV8lw5uumfoobMbml5SZ3RWSzkPL-M-Gh7pSZm9TIUZpckqCpBO1eyFaa0cs9iojUyHRSn5IEGyipOuxSob8q2SR1gh9z-WW1atFvsb5xRrqjHnxwbT8KH56biaS4o4rf3vf3a06zs--M7e3MA5SD9bRY7f4MVWqFA6xHAlu2J-7M1JmzIbfIGPggdrRedpyoZLuzt8z-evcXk8C43ZOV0NN6zn4PTs8J6YlOL3UA7tqo9E9ZzCofK-7sayUX3cmythydt1sfeUzjX9ad2UmtRRw6B_dPgA3XHq1D2s_1jKQZA7JmVqg9_l6GTl9TiRG1MIqw6XioymopLkmndmRQIz7LzGHqz4pbBwTlYNpEEIQ1cujMsjH2AgW6bjLiy54La1dyEpfb04o08kpKqNOIKe-53PG9I5e3KzWMgtWvvAL04_4tUccml1huJGf756878tjA-0JApNdKDtD5BMfq021IvcizANrmsHxc6-gN-6Vhps2XaU3tEJgAeNEWrbCbGijUBmo_Ajv64R_BGGMUZrx0Vll4hSDFaRCxscoxhUedNiL7UL3a2RaKZGTuX3zNfGHIoI5N_18_eaDLqy83htit45Um9OzeEfU4CsbX6KCxSKijLCaARi8i1EQc4hl6Ant0PdqrvQ_YJO6prjV4XxCGu327tRX08T03BucUszr64lVdtdG4Fqll8eUeCaBdPwo1yNJ2QuOhYGCv3v22FhzFwHnsSEZMtUTY4vR5j9G57P972cDPKfw-5YOmCnGoF1g07YWyaxXWBuNBwY_HSRlO-rk1GV7eCWapwxTknKsgFG6tAc9YOq_cxOqP3NSRYl0wFZcP_edRbYRmaBqSa39kLIEioINVl47RsEVJOjREfzlbT56JVhHz0iOrne0LP1MqH4tMKXfqVIznsEr8wmoOwgn7mrerqJ7aixqoXgalbpKm0JsRtN6j1I5p9mufPKfVNkJS6AcWhMlEkazAsIam9itvFW-czf1x3cnydCWKBjs0Bfxqb-hwudFqaUX_nnEbrLEwgLVo6CWiAL02JoCZZKARA-WTCQhFMGhNRL6MynpDcDr9DuDz_KNRPcws6LrB813h8bwEhWB3ljIvRLHz8eqc5K8V_t9CNw71sfw8pq1P7pCkdFMOsIShdjDWUqwQ_IOT1d7M6VIrJo2atfme6N70kudJlm1EjhAnupZteI5ehvp6xU2XH2d01h2GyuPngUtGW5bZpuOfiSjL--UjD3lPWWDUpIyzWoa2RTEt0lQO8OqdoRbYZ2CoUyDk8LC5WQbg-P8YBHSZQkPAK4me7F5nYJQXuyFourgnG8_yg3xoylsFUB4sMEWEbhlFO7YSLQadkqrSa-tajk6VY0OH74g5TPrJyqOmKGlS9u_u0hQUyKo6q0BgpbMVAGySBcmEPFyg6Sdnm-sAP70i1kxFUU1gpz8BfSRECsCmqpRdBNDHAH4sxnXTicaP7u4EJnmQ30ySOWX6sGubLdyPl6VgmjOEfBkeRe3-sWD_jGLNDiKdYQTslh8DZpqVj2V9BjDMJKmgE6DYEr9b_lli2caOYmQcbrmrfgS4GIhQtVs9iRfsjs_a7utQ3KnIjiUHt6nm9G0F--FvuDb8fnX5-PAh5Z_bcT5eTYRNpOO-PxLTTa__fzX4sSwIPFFq3t8uG99QzWeH4NbqYOHpI4hZppOSACn-NOFCwqdNCjlqKcu62c_xC-Z6MjCQaK443gAhhqktec8sWkwDJ2nFO81suQPSjeE5Q-VyUuVoEoSRNjvWBB3xu6OYewC0yHtXePJYQCQ-8mP_Lt7h9MQ_LDX8QynXdqeigfRHeyKLgBFhf3w2fzRDDYRuf8gH90qXsms63tqQV6Ru44w37YdiXOPJKMLVAnr1SUXnWpebuQUiG5NHPG5R2tn8YDIGLyKEMdi6fGUDmrd5eRpR4-cD1p34KdpfjWPmWxOfB0t_cNk3MtSOeby3Kuz-scYziibe5n7ZIqJwKnr2cyuKFLJFZlFS-C-VjIxEKIOPfwCK6r4RenmhdozvBIXFip_2QNf97jTZjOq1WSrmYMLXwp0vVg02gs07XNVUyRr-WIn7uCJM8VzDAsg7FWGkS-l0k6TWsgooAFxBsQ9YPoW6M2fnumCahZ_wR9U322lQWeW2-8EsFmet5RHt398xArNudtxh2fAHSj2jcv7a6Jy81Mz29GKsduc3ue1-9yYNQ97z8YDUvnvn5CttIXtHh0KjlC387sUESGEQUCJVVVaCEdGCz1A3ow7xh2Z6y_yVdI4MY-iJ-MfXyLRLjT9LgsWYtGma9a-krePqgrg5s0eZEsp6JFpMsVGJYyeWODW4ZPzejdCXzDRgyueVdq4b9Y3K5EZnHjxMGVlKW43_QVUMH3VHkrJZwykfHupRsKusMjnimUJx1T_7ogZiq23hy0SP6YBoQm8MwAa9ntT256BvWW-HupRsz5xX8NGi4C_O_SRgbhwxO8pGuxDi8zMGm-9lzXB0Z8lv381J5bUEW3KwU-kZChuQroDlpg-UXONoknjR2iywT1HiRVdfTvTEmpv-MJW_woOIg6Q_7HPVOnVScus28KulsZ5MDRHrhviXmSSQfKXZ8bVnxvcLfKN43NFye5nh6iXmYfgOlP_vhdtUgCEwSIAj6oK8KdEYAax-_Hgf0j_CjfLkefw4zoprGC_1WHJ-F2Ay8kzYbAIxoPoBJWdGwURPnQAdqX4b8PhImhJ3iyhIWpVv_EmUNm0pm206EKITTvsO9KO2m98l0OrmXmsF0h_onPQMrw0fc0Hy_5FViDqoXsfCJ3BxD1OuXxKb1T-oLec_bxFcHlHWUz8qfaw6fI1RVtQo_FaZsXpGx8KiOre-tnTVfCzMYeVOkJZf9Xtkvpy8PK1XzcciWsn5dVAhSkPdaiqFGeuDOklX5xK631Ydm4zyo0gA8zgSqURmqMdHdg90_BhV_k6EL1o_GEQorQNRuDyn8Za2ExmFfPK2cciPSfJEOop5BlD9VPJgGsy169-aPfLygciN5uqSJJ27mPapGUdYadv10umeYgyPu1heO5l1IEYOBpzIn8D-lIxeDE6GOKwupWkSH3Cf4w0Km_WjIzgCQ7klYG4P5ThYTmMD2rtjolLJIF1z5qNYOGPq-H-6Bavq7lNSPOAVaFF2EWvas3g0hS4J-VwzaW57SYTrJiSd2ENO7uHaT1IA7c0ufn_eQmeE454Na6uuVUghyKFmr3Prep7GsEjc7s5WU-1TaKvnUhv2M0N25IFUSXSRkABddPvcLCTMTc0F_R4IeKAngEjUtvT0r2koNHT1LpTnphLMaE82wRO6DjR3o-U0FK07lhH2SibTU_TaqcdOzzvpCtCuK22_s1Bn3eRcyVLMx5OEN_gTqeGmbz4TWPe2GKQAru9gY90bYLJ4AdXvOUhBuZEHQZNRJ8RhhWEqSspBjLiIQujLAA9dC8kIx3Z7ar2NOEwWs7GNXlvy-VUmk4RsCxLK_1P49N2-_aV1Y_ClsnLHeDMQ4OJkKg2uIUynD2iJDCaOj3EqZPt8rsyDkFIGDzOAuLkAcr3DwlV2n7dl3xig2Tg3J9WfifjbS-6U5SBU22VfGoZAnRasjzfA2z-C62S50TZy0CXdBv2g-vBNzU1jkTLzdFy_HoI2drdln2wjHOxUQGm4j2eLq-pgg7jsuQQlilgn8ULSbfBhAEZ5Ydd6NF8wbXOz9MGaUSKK13eh9UCzJr0Pxm4wJSZ2wyzxRQrYkRIakwA==

preprocessing.py.enc

@@ -0,0 +1 @@
+gAAAAABmBSHm9z36l8Q3FDJhnMDifVxDkLNEFXCYDLazNzIG0icxiyMHbS9oyF1n0gVyH6x_R1RTedIMuz9uO8rR5mMzMQ17zfZoUfVSdg4v8jlVqxbjY91CMCXODXs8RensBtoPXiEG532-BVJjzrDKN6G65_CJpVW63T0Bg6PPDJEa7BLIBNws-FHIrgI_xv55D-SmZyl0AFgsNlrFQDjmWjPL-cFIEsqsnbGqzbRObnw_WU1gsPhyxPoMbxe2sJrQs-xngfkJ-m_IbF1Whc4tc8-Npkbw2A_PG5IzGxQuWxV9lf8bmlp42JXRPRsOzsukv-6WMF7-gSd--q5C0HtTRFNKIxUbeAbUCP9VQVFe8tnri89go9f_A2YdnVyExT0VNubrmkH5l62PQkUyakQMLkB0LF-pyWnsXlYzuvFSGp70L76hg0Z107D71DOi6xve2xdPvsgELQ7hwTZG8LFeTJOBW3PfnS_xQk2UvUQtI7jh4SXPyXMkk3uNuLEEwMLGyRutkPDVh_PfTQW8CL-vH_KnktjHdanO7Joxryvfr97-FkG9D6m-BdzHVWCaEUaN4HIy7JzgdoSAcJQhXq6vIS3rw0XYpOsCx4OeElpVo-SDTHcylzYY--Y4Q9IH0QzXyv2ZTnZRsVPsQ3xV-XMcs0xXm0mR9krtXWr2FFnsE5KMXWMd-95tYxFO6wi_2rEOXld-ib4f8SVy26inzyk03RXoY6vOF-nGSnu60_eHeJ6Gsk7fWRoeEXrEGrn6u-tktx98goVMH6gFCdKCmVhcbAayW_cvHJMOy9lXEuoB_dPyd2FwjQF_iq4WYAq6x3urWQpp3P7B_YzFwzpQTLbhVZssTSsBDHHi9m2ittbw2j-7M6yYxc-kCd339PDGwWtLHiSHGzTGSX5SHvktTZ9LdXSJM9E53FOpiLgeCcyiyCqrsQH0VcxRIELw6TJCKwUk9LQRqajbx4GQkB0pDO7pbn5QbUykY2lGMnmcSSIOaVV-MKKda52qwdj5BA-UQSMt6b0UeLL-MJGwzNIMS7fEH39u35H6kKO1_ynBvqKpkk1Pfg5FSYttiAIfTJJwsRQ8irkuSe-XErtD9IHDHOxIADvS5QJ23t13UoJKlTOt_LR0ZlcxbBmu_3QW_X5AOk-1zSrTPUV4HD9PCuZYxiuVSx3z9kv8aEsWCz-j7AxDqoGykvrt4iGE5xgGyEYUqBPQIGT0mcUGEobQPc3y3mbuyKJZPkn9XexK5Lc2_ZuXQBTexPPt6UvuQQ0SKlfCyKFBZpBirMhiAjopb640lLK7L6CF52Coa0ZkXGvcw8TAse6E5tOVSlDi0sw-St8prG7_8JCKP6_1G-TC6XssjAJjwpCPdWf-dLpc0nxJ14zY17hgaxFBM79bol3yHJSyXB5gMvE6YJlZ2BCQH-K0koVZO_ej5z4QbExy67nTYFr89IBnVhOAAzZvtsPYSLgQvmQAMlHq_1MQXPf6jJAbswjxJwEYVej1env7lMuPaK6Z1UQcd5ZzrivXH28hPbzPb4fxN2x6FlIoElWYgiJSnpV-zT_MaP_10CZAmQvgw7bnLdy9PtfK3IHdeY0VM7fvA7P8ppqV65ggT1VEkcmB5gRxqPFCscIKOoSagGwKC8k3fvUALn4p1hbnRlC_1_Gsxj6024xaiqIVQcb8Shq8VftOv7fZp2aMimBcu6w0ZmvB9T60hfDQeEii6QNtuVLlFTwvE0slU692jz7n5k3_bnY5S86sxMdTK9MM653pF1h9EhSBhLk-Jm9NeAJn6kjX8cuMzkTY9LYTV20aI2PH2fmWaz6agumkEpeDY3GiRMFvsfRVMqyJb7NappGHi5hFmWtQI7FCbI46mo7RNXE3UCtBUk2uZx4cxGaaBXWHyEsel8AQ7aQyaOn4RugAUu14DRaGb0jEWn021r592gqyKuVTpy0wsOxyJvayjhKQOP8B6nqS6muB6EwiJyjvcwU5UuRbi4yf__EBPetN1slWR8Vv1pu7zTldY_z3mh2PNIXWCBK1IxJwY0dG8zs_FlOj3aa5sVlA2ahPpgAU2HhCzdPr_e19FLyJ1DxOxzNVvOzLcjGNW4dsdWO1ahGgl9rnbX9cj0SS7gvd9QwdOixOKZg_tropJZVVxOYXchYBvJL3YyIRJYye7Rm94emrpAcwV45blN_qRbKDLpHTRPghVKbmD9SU8hJAfOMoOrcyiVuIQttnddcukaxFsx3s3aFPW8NHq_Jm0--Q-GsgT9URgOkFwfL1FyM57QBS681TObu5VOEFbPp_WpoPIV9qLvkv-58pIl3wtYFPpRPISvcZzdEZGS-ON0C62_LOed5-6-sLgGBPnTStFzThijEHjXPATGZxcEgOVhHE_j947U6zWvQgHJs5ozyxJPrmDUbYKYhXijTUvM3b29E-t-fW9hVgcitpJaP8NawItaPCSfgWIklcWJ_Am7nWAqJiOt8jzVjGjrDEOzt2Kg7dW_ASjnjrgK2SHDxg3DS7vnF2HcLfkDUFpv_SCuLhLlJBQdJdMXTM6H2gAXTyH9EVvBE_r4aFsJDM92vN4LEgvK7isO_CADr64_XRvw8AmnTzhStaOkO93fuQjpP9Tmws_6-bp2F2uHoqElSCZsRPM1hV4AOT-jAJaZNRVR-RHKVdeO16nCQwQXCXxoEBnNvjCZTjl9qDZIe4sQvfOvvLOYhMFhFygRI0ZukTFWL4WyE-bVecyd92JOKP1wF_Gsr8tdlkqJnGKupAbtttoA4OIe-DFGXP6p6VgElHARivSkxy3iA2JwRmRS-Lvinh-m0VnYuOH4m9WL98ykFIBKDo17YiNxJEFLh3hJ3anxrvr8EBfcd8EGlnH29vYnadSOQADEgJP3ck-FYM8mzVVVIvaVu-_i1YDC5P7xnu0CH5FEV8G7Hu97WDu28TsKnCJydR40MR84DCeXgcatzp0u9jYo6CwoEu3xpgoe6L7JsHFG04ULkWc7c6_5qhpBRzKNeUwRuleZmRP0sIGIvl8bsPiAmcYhqOhiGATiinmKlu1Qi4XIW8eu4RQdaGXBCi0MDCnMOnudIuzdOXubEvJDemca8TjIJWOha_ckKM9l9qOqbmhS-52_Uvnh31BrGZPHx8X6aAnJcwe3c0Hu9_SDRjNPv6XPUUSOKPpjnmIMSXQkWX-uOCeEoJlg03bYKydW71XBH33S8cDyWKo_pv3p19FnBbeOMmGgOVZ2QFxSeTnYDRCkSiOFc1-ov1JG6IDMmwQiCmR-VFUfp5QSsveZZPk677QNekHyxjfbBiZQ8DMw==

product_similarity.py

@@ -0,0 +1,231 @@
+import pandas as pd
+import streamlit as st
+from tensorflow.keras.preprocessing import image as kimage
+from cryptography.fernet import Fernet
+import os
+
+from io import BytesIO
+from dotenv import load_dotenv
+
+
+
+load_dotenv()
+
+
+dec_key =os.getenv("FERNET_KEY")
+
+cipher_suite=Fernet(dec_key)
+
+st.set_page_config(
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+
+@st.cache_data
+def load_data():
+    # Read the encrypted content from the Excel file
+    with open('pantolon-v3.xlsx.enc', 'rb') as file:
+        encrypted_data = file.read()
+
+    # Decrypt the data
+    decrypted_data = cipher_suite.decrypt(encrypted_data)
+
+    # Load the decrypted data into a pandas DataFrame
+    df = pd.read_excel(BytesIO(decrypted_data))
+
+    return df
+
+
+# Read the encrypted content from model.py.enc file
+with open('model_takemura.py.enc', 'rb') as file:
+    encrypted_model_takemura = file.read()
+
+decrypted_model_takemura = cipher_suite.decrypt(encrypted_model_takemura)
+decrypted_model_str_takemura = decrypted_model_takemura.decode()
+
+# Execute the decrypted model string
+exec(decrypted_model_str_takemura)
+
+
+# Read the encrypted content from model.py.enc file
+with open('model_hayabusa.py.enc', 'rb') as file:
+    encrypted_model_hayabusa = file.read()
+
+decrypted_model_hayabusa = cipher_suite.decrypt(encrypted_model_hayabusa)
+decrypted_model_str_hayabusa = decrypted_model_hayabusa.decode()
+
+# Execute the decrypted model string
+exec(decrypted_model_str_hayabusa)
+
+
+# Read the encrypted content from model.py.enc file
+with open('model_shogun.py.enc', 'rb') as file:
+    encrypted_model_shogun = file.read()
+
+decrypted_model_shogun = cipher_suite.decrypt(encrypted_model_shogun)
+decrypted_model_str_shogun = decrypted_model_shogun.decode()
+
+# Execute the decrypted model string
+exec(decrypted_model_str_shogun)
+
+#
+# # Read the encrypted content from model.py.enc file
+# with open('preprocessing.py.enc', 'rb') as file:
+#     encrypted_preprocessing = file.read()
+#
+# decrypted_preprocessing = cipher_suite.decrypt(encrypted_preprocessing)
+# decrypted_str_preprocessing = decrypted_preprocessing.decode()
+#
+# # Execute the decrypted model string
+# exec(decrypted_str_preprocessing)
+#
+
+def page1():
+    st.title("Ürün Benzerlik Analizi")
+    st.write(
+        "Ürün benzerlik analizi, ürününüzün fotoğrafını yükleyerek benzer ürünleri ve verilerini bulmanızı sağlar.")
+
+    image = st.sidebar.file_uploader("Lütfen ürününüzün fotoğrafını yükleyin:")
+
+    st.markdown("""
+    <style>
+
+        .stTabs [data-baseweb="tab-list"] {
+            gap: 20px;
+            padding: 10px/* Increase the gap between tabs */
+
+        }
+
+        .stTabs [data-baseweb="tab-list"] button [data-testid="stMarkdownContainer"] p {
+        font-size:1.5rem;
+        font-family: "Segoe UI", Tahoma, Geneva, Verdana, sans-serif;
+        }
+
+        .stTabs [data-baseweb="tab"] {
+            height: 50px;
+            white-space: pre-wrap;
+            border-radius: 12px;  /* Make the tabs look like pills */
+            padding: 10px 20px;  /* Add padding to the tabs */
+            box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06); /* Add shadow to the tabs */
+            transition: background-color 0.3s ease; /* Add transition effect */
+            color: #333; /* Change the text color to a soft black */
+        }
+
+        .stTabs [aria-selected="true"] {
+            background-color: #e0e0e0; /* Change the background color to a soft gray */
+            border-color: #3d5afe; /* Add border color to the selected tab */
+            color: #ffffff; /* Change the text color to a soft blue */;
+        }
+
+        .stTabs [aria-selected="true"]:hover {
+            background-color: #d0d0d0; /* Change background color when hover on the selected tab */
+        }
+
+    </style>""", unsafe_allow_html=True)
+
+    tab1, tab2 = st.tabs(["Benzer Ürün Analizi", "Ürün Özellik Analizi"])
+
+    if image is not None:
+        st.sidebar.success("Görsel başarıyla yüklendi.")
+
+        product_category = st.sidebar.selectbox("Lütfen ürün kategorisi seçin:",
+                                                ["Pantolon", "Gömlek - (Test)", "Elbise - (Test)", "Ceket - (Test)",
+                                                 "Hırka - (Test)"])
+        if product_category == "Pantolon":
+
+            default_product_details = ["Desen", "Bel", "Paça"]  # Assign a default value
+
+            product_details = st.sidebar.multiselect("Benzerlik için öncelik sırasına göre detay seçin:",
+                                                     ["Bel", "Desen", "Paça"],
+                                                     default=default_product_details)
+
+            if not product_details:  # If product_details is an empty list
+                st.sidebar.error("En az 1 özellik seçilmelidir.")
+
+        with tab1:
+            # Create placeholders for both buttons
+            analyse_button_placeholder = st.empty()
+            show_results_button_placeholder = st.empty()
+
+            # Check if 'analysis_done' is in session state and if it's True
+            if 'analysis_done' not in st.session_state or not st.session_state['analysis_done']:
+                # Display the "Hayabusa ile Analiz Yap" button
+                analyse_button = analyse_button_placeholder.button("Hayabusa ile Analiz Yap")
+                if analyse_button:
+                    # Clear the "Hayabusa ile Analiz Yap" button
+                    analyse_button_placeholder.empty()
+
+                    status_placeholder = st.empty()
+                    status_placeholder.status("Analizi yapılıyor...")
+
+                    filenames = model_2(image)
+                    st.session_state['filenames'] = filenames
+                    st.session_state['image'] = image
+                    st.session_state['analysis_done'] = True
+
+                    status_placeholder.success("Analiz tamamlandı.")
+
+            if 'analysis_done' in st.session_state and st.session_state['analysis_done']:
+                # Check if 'show_results' is in session state and if it's True
+                if 'show_results' not in st.session_state or not st.session_state['show_results']:
+                    # Display the "Hayabusa Sonuçlarını Göster" button
+                    show_results_button = show_results_button_placeholder.button("Hayabusa Sonuçlarını Göster",
+                                                                                 key='button2')
+                    if show_results_button:
+                        # Clear the "Hayabusa Sonuçlarını Göster" button
+                        show_results_button_placeholder.empty()
+                        st.session_state['show_results'] = True
+                        status_placeholder_2 = st.empty()
+                        status_placeholder_2.status("Sonuçlar toplanıyor...")
+
+            if 'show_results' in st.session_state and st.session_state['show_results']:
+                image_dir = "general/PANTOLON"
+                df = load_data()
+                st.empty()
+
+                for _ in range(5):
+                    try:
+                        takemura_output = takemura(st.session_state['filenames'], image, product_details)
+                        filenames = takemura_output.split('\n')
+
+                        for filename in filenames:
+                            filename_without_extension = os.path.splitext(filename)[0]
+                            filename_without_extension = filename_without_extension.split('_')[0]
+
+                            matching_rows = df.loc[df['ItemOption'] == filename_without_extension]
+
+                            if not matching_rows.empty:
+                                for _, row in matching_rows.iterrows():
+                                    cols = st.columns([2, 8])  # Adjust these values for desired widths
+                                    img_path = os.path.join(image_dir, filename)
+                                    img = kimage.load_img(img_path)
+                                    cols[0].image(img, width=200)
+                                    # cols[1].dataframe(pd.DataFrame(row).T)
+                                    half = len(row) // 2  # Find the midpoint of the row
+
+                                    # Split the row into two parts
+                                    row_upper_half = row.iloc[:half]
+                                    row_lower_half = row.iloc[half:]
+
+                                    # Display the two parts in two separate dataframes
+                                    cols[1].dataframe(pd.DataFrame(row_upper_half).T)
+                                    cols[1].dataframe(pd.DataFrame(row_lower_half).T)
+
+                                    status_placeholder_2.success("Sonuçlar başarıyla toplandı.")
+                            else:
+                                st.write(f"Ürün isimlerini maalesef eşleştiremedim {filename_without_extension}")
+                        break
+
+                    except Exception as e:
+                        st.write(f"Hayabusa için Lütfen 'Sonuçları Göster' butonuna tekrar basınız.. ...")
+
+                st.session_state['show_results'] = False
+
+        with tab2:
+            shogun(image)
+
+
+
+    else:
+        st.write("Please upload an image.")