get a basic demo with puppies

app.py

@@ -230,9 +230,10 @@ clip_retrieval_client = ClipClient(
 # results[0]
 
 examples = [
-    ["SohoJoeEth.jpeg", "Ray-Liotta-Goodfellas.jpg", "SohoJoeEth + Ray.jpeg"],
+    # ["SohoJoeEth.jpeg", "Ray-Liotta-Goodfellas.jpg", "SohoJoeEth + Ray.jpeg"],
     # ["SohoJoeEth.jpeg", "Donkey.jpg", "SohoJoeEth + Donkey.jpeg"],
     # ["SohoJoeEth.jpeg", "Snoop Dogg.jpg", "SohoJoeEth + Snoop Dogg.jpeg"],
+    ["pup1.jpg", "", "Pup no teacup.jpg"],
 ]
 tile_size = 100
 # image_folder = os.path.join("file", "images")
@@ -249,22 +250,18 @@ image_folder ="images"
 # }
 
 tabbed_examples = {
-    "CoCo": {
-        "452650": "452650.jpeg",
-        "Prompt 1": "a college dorm with a desk and bunk beds",
-        "371739": "371739.jpeg",
-        "Prompt 2": "a large banana is placed before a stuffed monkey.",
-        "557922": "557922.jpeg",
-        "Prompt 3": "a person sitting on a bench using a cell phone",
-        "540554": "540554.jpeg",
-        "Prompt 4": "two trains are coming down the tracks, a steam engine and a modern train.",
-    },
-    "Transforms": {
-        "ColorWheel001": "ColorWheel001.jpg",
-        "ColorWheel001 BW": "ColorWheel001 BW.jpg",
-        "ColorWheel002": "ColorWheel002.jpg",
-        "ColorWheel002 BW": "ColorWheel002 BW.jpg",
+    "Pups": {
+        "Pup1": "pup1.jpg",
+        "Prompt": "Teacup Yorkies",
+        "Pup2": "pup2.jpg",
+        "Pup3": "pup3.jpg",
+        "Pup4": "pup4.jpeg",
+        "Pup5": "pup5.jpg",
     },
+    "Embeddings": {
+        "Black & White": "F0kxPHAqE7t3DoY79djWOwA6Cb2hjK88EkuIvXdEgTzS2yY93WXvOsKffL08qjU9oGVJvZtXD7wiQ-u7QTLhvGRqozpSFqo8fCMaOy42NDyyXCC9ls69Olk_A7zJ6Ik97AwLOyNjCryYr4W8kREmPfIOPb0xrde7137Fu3Jr5bwGKGU90T-lvI1pMT1ftz-9qy3vPMTnRDzx97C8fRWjPGbQU71d6f26ASZyPdg3Qrx-saS9FaAbu83DK732Ry-9WQ7HPPPiwTzY4gS97gc1PGXmRrzsZUS9kQwmPKDZvzw4F4a9zElPPQjmdj2Lqak9SHFXPJmPvDwRLPU7YvqHPV7OYDx7K-q8wfdIveWEXT1kYE289sfOOwH6YbuID129kMivu1uvCb35jGi9shisPNsXz7zb0xk99u_ivE68QL2ibjo8jCAmvPIz5Lyv9kU8rUIKPbCciD23RQG9P48tPfEpsLwbZkE8wtjHvHqvB72k_Au818IRO2pLHz2U2yq9M1_hvTG2FD05si275m4rvL85ojtDSCo9xWclvTOEgLrMt449xggSvFQzT72wtOw7mtgLvRe-N732MlW8bGIWvMi_m7p1XSA91Sq2PHDDxrxcMC29RPoJPVeIzTyCC448PVequz8rLz2Rsnk6yayDvVjAJjuJxqe9ivRpvVEKjTwwxoM9OKOtPUjlhT20uYO8ynh2PYLVZjwREFS8wWWIPLZGrz2oNxy7GR4gvaENaDzBxJu8ZdYGtw7B7rxG2sC7mP0QPYkKXbzbh4E7HrsqvJ64P73AL_Y8I6L-vB59jjyVE8O7jshZOw19JLwQD1A9NG3xu00JHr08iBC9WANYvBe-N73tpoS6dOZaPT1Btzy9kcI8MzNRvNH7hDuMnwK8o88MvZWU5rwDUhk9sBEPPGi_DL0BY8u8-cKPvGqIjbw90_c8y19CvVYT8bxLLxU920igvX_DgTyAzh-8q2lZOy-UmbxXnlU8or9cPJruuj3HJHy9a5-ZPUJ717w9frq88QG2vIwxQz2dIam8ET5Uu2ftYzsxRxI9nDwcPDQz5jxtrf079M5rPAVIrryU_U87fslzPSTkQrxpCK08VY1NvdsXTzuwsz48rvvavIsTY705S2G936llvTsk0bwQhEG6zoYovD0TXjoseXq9bt45PfQJqDzYp_I8z3WLvGTflLwWLui78YjDvAx4Trw2-yO9oXunvADWCj1wlQQ8NmF-PG1uszzCusW8jujMO3W637yShqy7O4loPHI4ybw4yr271thYPB9hAj0PwUQ8L0fRPLkzlD091oY88PvBOjv4wDyBMh69eUsePfOb6DyI4Zq8TxH-u0uCi702EZc8kqoQvQQkAbwz9AY841FFvebosTs2z708FpwnPUqOPb0zlQi9KNeWvBbeALwy70U91KD_PHPxSTwi8wO8aroMPYpCdb1QLDI9VgwAvL-JKz1I1Za8EshhO-EUbD2cpQW9UEeQvA8vBD0KvYq6ZJ6DvWZsVb1UdT07gHVPPUM-VLxbYZM8SoXXPFP6iDwC-AU9_5p4vIuPKD0d7x26nrBSvWaCxjn2aCY9iaLDvD8yYbzQPzo9BihlPaxUar2IeMa7zJniOsRbiD0zigS9P-TqvJMO7bsuR3u8KS_WvEL1XT3vWoG7OmGqvBsJdb3_vlE5js_2vP-wF70sbJq8XqWJPOOOM71wkJk9WJRqvA7XNzqVV-O8OjBuPW5_O71u-QK92baeOovezDw-NPS8G-dkvRx_UD1xzYe94dxoPM0_-TzqrSE8DUWhPLz6qzwr4TW9yOPIO5FmYzyM_oC9fj8gvWtdljuotQg8lwlDPPPRubw4Bxc8f6y5u_x2trxXH2Q81m2TPadef7yfNwc9AlIuvKNWXr2A2c28s6e1uspCsrzBHtk66N90PeUS-TqID109vydpu8vDVb1ZknC97JqRu18mLT1hIZg8CNmBt43v1DsezNw73krSPH_eo7yAPlA99-k0PNBamLw16jA90kSlu-pTIz0DPLs8sdQLvMP3Mz3_zCS6UOMmvX4mFr3OhtK7tjA8PT6wJL2DTy69bE17vCFNVr1LJVS8zIuouVlRzjy34jA9CV-QPD5j3DwrYu68CD4uvZAP57zhOQs8krApvOvEV707mAC7giNdPYjSbjxfdLg8gZacu3ritLz6NPI7KDTBvEEhxDzihwE8pX0vPWS4GTtZfk28YPBwPQMaFr3Jqxs8t5bsuw3moj0q2q07AuRuPDSLnbw1-zi961XVuq_qKL1ofAW8CwGBvVScoz3uDny9px98PA35cTxwaYk6jwUJPQUyZbx2mVM9SMqnPdPMejxuREA9dK0UPe4VCLrn6vg8LBTOO8c2v7yL1wW9PUWJPIqrybzN3h67_-sSvVaMHz2DKno8uDCSPN3ZiL2B5JK9-CsOPNdBrbzEIPY7gCjdvEAJCjv3LX-8NjupvTIxijw55-I7CgjIvOx1Hj33TUg81is6vLs8Gj1K7VA8nyetPXnXAbgJ1US92c7tPNAYP7zzeVi9eaVGvaT8izv9_pi92S3sPCywZD3ihPK8pJ2NPZe0BT3EYSG9ahv8uic4k7wNc-M8bX9QvTUsirzUYaC8LCVrPS5Y7jyqsnk4UYbaPLF8P70_x7C80qB_u5jBjDzQqCO9vqn8vCUcMb3Z6TY9ZLRcPYLJHz3ZPJg7OMW9O23YBr3YeZu89uKCPTdrqjuwB-O8ZL82vJ7ClbvHOq-76d4xvITNT7z2x6Q8NQAkvatTUb2PhlY8NCe0OzOLR71EliA8-W6APBeomjstTFG99YfouysDNT2R3b28y7K4vLt2-bvKf6C9bf6XvH8EBrzZMak8BNpuPLLJhz1G6gU9UouwPE-QRT1lKKA8bjaGO9JQGDwpjtQ7yi_BvLv_ATyE1Ju99_8nPRh1gr1Vyyk6Q_WzPc60fz2-XoC9nsTxvJfFDT2zS9m8kx-2u3mlxjyfqf68Tje9PcK_G7v_F467sdXOvIzZdr3LONw8ngu2vF8Rgr35KP87TzCEvQNZS7xGrkU976MhPTOLsr2woYg7OGAmvRPIzLxLtaM7ofWDPXREq7wSwsi7T7dVPEiO5zwaOtu8OudNvHa5nLxYLpC9h445OydP3rsAwMG7rV9ZvWnBfb0Gwoo8ZwPXvPa72ztudDc9l4gfPJTxsjxCioM90JIwPaFs5rxs46q8yGKQux4injyRLZ281PZivejwfLwwZGE8NTinvDfsdzwSkMk8hMtmPSTkrbyigau8L4kVvJntIrrwqTo8dNSPvctfQruCT0M8lm1Wu6mpPD2Mmpc971YEOY4sGT2Be1M9rewmPfCeS7yGn9Y8KXYaPVbF0DvtdQm9r2bhvPwGGz1jNOe8KQ0xPbEPOz38nIO8PEQaPKFsZj30PNW9Be4FvRVfzj2HUvq7aJiRPB-lNz1GIze87PZrPKefPz3g_ni8VfOPvYRFbb1j-7W8_Z0xvVLvrrz8kZQ9_2dLPbAC4zxthKa8fzwJvff_p7waPzG9FV_jvLKlDr3ED249QDy3PD1SabxxX0i9M0hAvVL1Rz0cGCG9coYuvY--bj1sKhO9d_jmPHowQL0Bm_g863DIPUM-VD0OVhQ8I5H2PLwD_rxb5zY7MJqdOy6wOrxhIS09yOU7PT74ljwRi_M9WYDLvAtKIb01Yf68WfwQPf2YsTv0-vu8bp_qPHKDrL1z1KS8Xf_bPDCQXD1oIZE9lGFjuzaN5LzWIXk8nJsvPSgdtTyqEEq9286EOyZm6rtF8DM95ugxugOlDz115YI86aumO388CT1SPRC8MtM5vB6liDwqxT06Q5agvJ-TAz0CikY9MxbBvP2_QTwxhsc8NLeDOzql9LxJxmq8MSWCPfVtkTzuo3W78-gSvenIvrxOofe8zr5APQBnCL3z4us7cKEhvQ4lWL0FTwK9cMMcPHpNZT3G1hK9XOKhvZF2nz1mRdq8dgeou_fTQTz43968VndaPNdZ_DsoxLo8n0gPvTcp0Tq17Js85EnDPWiyZ733Fsk8AFwuPGuUv7sYHcs8GGtBvZK7dju76bi9KHsavWsqEzwk0xA8",
+        "Fire": "VsgiPPjckjuCP-q8fs25PC1DT7z-1li7_bAkvPkCrDx4W9E7V9sYvf3_D72Oyau8vXGFPVCSFD00qhO9-mSEPGQ8kT1_pN67qvXxvDwZDb1nLnO8TbvSO8Gn0j3OdUc9dNaqPP2wJL170AQ9CX-0PJu1jTycyBW9JAVvvAtWPjyF1pe8o4awO12vBrvkJ0O8EtsBPUszvbzduJO8zghmPeIBezyBaKE8STaIPBUnkDq7D1q8swJCPUR1kLy9qmU7itcaPZ2flr2fOlg7gcpKvQh__DzNAEM8xqSJPVNTb7s7aMA7cJ3sPK8ujDx4W1G8-RVhvcra-jxRuD-9RTkAPdr0dDx9a7Q7Y1q9ux2WLT0QBJO8DWkHPUE8pby9Ioi8kFydPJX0Xbzwpme8YeVuvL0iiD0q2Ti9oE1OPGXPlL0qswS805g7vWb1pD1ivPi8oOskvAm7BT323P685LIju0N1WL0WYLq80sEWPShRx7zTSVC83qLkvF-_1bwZ1RG9833EvIRlVr0jo2m8xxmpvGXirjyLTDo9hRa-PKAnGj2tVwK9aGogvay5xjxvZB69MoSnPHSHmzxcc1k79hhQvfIufbyH7Zo9E2NNvRq_0Dz0o_i8Tx2iPJb0lTypgPY8g90cPV7VljuyyZc7gXvfPLjW97x5W6097CGdvJhT9Lk7yum8AJsbPP90izw3uj49qUeEvQE277xqQaE70UybPIw2-Tq1O2y8yD9dPD0_wbxZY6485v7DvN3LP7vsg8Y8OZE_PehzGzwNj1-86kouPW2Ni70FM9w7wDIzvHUibzwqO_Q7c4djvc2eh73TIwq9icSkvA7x0rzb9Ky9Lpp4vaSs0rwUdkM6WLK9PFhQOL2vZ348TUYPveAqVrw3HOi87JbyPPGmXj06foG6E1CPPMgD-rx7gT29kgpMPau5fLxaTe271DaSvUjUpr0v6a28hLRBvS-tbjznpHy8rbm0uic-UTwCwZm8UOEavCZnmrz9Ek69cU65vcUGabznc0g8oZwDPWVX8ry3YVi9MPwRPZqilzu_H-E8m7WNOs-INLy-5pI85ZzivLewQ7wlVLY9WnaAPOMBMz1j-JM8MF47Pfe2gjuxjWq5LWEqPRohejwumni84ow3vBoOvLzHyqu8Z7mvvB1t9jzE83I83LglvQRv4zxAx448KRUuvI3UGT0Ojxe9IJM-O-fCzjwKVv29DMtUPX6R6LwDIwQ9gwNRPaOG1LzCSsA8f20AvbUoLj1SyzU9o3MNPJd8cz0mBQO8A0kvPVhQFDx-axC9Ts7IPEtkxDyxjeq6O7fPPI1Jbz3VDS49UQcrOsPg_LwpUSM8VwFxve7P5rwOQCw61uTbu1WMdbxV22A8IM-PPKsxHz1U8SG9cmGvvD1Kgjx3cRI9WMVXvPigwbxR9JA9zoj8vJJGlDzWlXC9N7q-vFGlAT0Ywps6XoYrvD946zy3_668ZZNnu4OhSz3WqHi9dl4cuE1ZKb3fj4I82s4KvILKJr3hjFu8w35TPZDker20d2E9DS22PRBmmLxmaug8D_EKPRUnEDsbcB2911n7u0PEwzyl-yu9lWl9vFFWljzP_Xc7i5slvU5_yzzJtGo80RBKvcKUFLxQkgs8jXImvDUyRLxjWr07RxCcPfPfybw_eGu8HZaJu9VvPDzCIwK8XjecPC9LRT56bkc9tWSRvFD0Rr2QgtE77x7SvPbUkzyikR48vVv6PPSQujuS96i9G10Du50wKj0UdsO5NIFcvGxnjbtLRnu9ZvVIvDI1vLyDjo28URppPCegVj1oLk-9u5oEPWzcrLspApQ9W4mavJEzHrufOti7RHWQPF9warw1vZI8vaplugiojzxjHuw8LSXrPH0v9bwwmoM9Dt6CPPIufTwHu_G8XK8qve6psrrvHtI60l8Rvdwt6byZj7M8VVMDPI3nxboqd7M84gFXu4-rI71DE688wEXxPIxfsLycKq07rWrJvAowJb1mG_28M-YRvYUWtTxq1Da95v7DvJHRKj0VJxC7QU_sPEdyxbyw8hY9xUIWPaYhPDtVtay7faepvBg3Tb1Ai2E8Kuz2vNOrebygnF09X13QvEN1xjy4dM67XE0cvE67Lr0yhCe9rbmiPZvuf7yT2YW8_E5DvR8TXr1QVt46iLGuPJqXTb3Z7DY9LunjPNkw2Lw3uj499ZCEPbLJl7vefAw9TWznvJ8nPr0OLZI8Ff7Yva0IoLsEmIg9x__juwaoe7vmmgY97vgdPUIAuTwOQCw79-_0O_K5ubxN96M8GkqNPY2YbL0bcJ08y3gtPK0IoDucAfY8uLAfPbDceb0c0iK9ysdgOQgKFbtkCwq-NqfIvGD7Aj3cuEk8z8QFO7CjKzwNyzA9aslsPdRv8ryQq4i8e7LEPLaKD7v7KI-9fJSzPO6WmLy27Dg8TNGTvOSyIz0arLa9zRNdPW7G_TxUUzm958JOPbrWCz3L2kS9tU50PZ3ut7yenwS9rQggvH_1-LxCTxK8OeAqPSvsUr3Ijja9Tn9LPYixrjuD3Qq9HzyVPGgbNbzyV7S8UQerOj5l9bymITw8H4BIu3g1Hb3KePU8F9VQvZvIp7yN50W8poNlvB5alL3M7cy8IUSLO0HaH70NLba8PI70vGq2QLwRjMy8YF0aPZmPIbzVvqe9MHF5PcjdobuWuM08rQggvaMRiL1Tokg97-JcvMgsjb0-8A09bNysPbrpJTxQQyC9e-NUvF8OZTz_Xve85CdDPEXB-DyPb9s8XdVePVRAjbx3SFu8lM4pvHTWhjwgk746a2dDvF2vBjw00Gs9akEhPE3kCT2PmJK8D2bgPIS0Qb2xRY-8-jdbPQuloLvyzFO9uRKKPHaEdD12Xhy8Je0Uve-TcT1lMRo9PlI3PafSCLhy1nK8qLzrPA0E_zzkJ0M8eUiTPf1hubxkqYS9IhvUPPeNyzz7O006wmvdOxX-2L0EDcy8zcRxvTp-gT0f4nE7u3HfPB3lBj2Pq6w8FHZDvZlANj0OQKw7z3WavIVlMj1B2qi7jP0GvMBF8btDOWM8w-B8PX4cpb2tkP08YqnwPHLW8jzPJgs9-mSEvbf_rrzN7QS845-tvP3_j72M_Ya7ZkQ0vczajryGKTS9RMSNvS-Hnz3aQ_I8IIAkPbV3vTx8Mgq9z4i0vP_pTr1UZmU8lDDTOwl_ND1zdKU8G3AdPUlJIr0aIfo77x7Su1Zmi7wmK7e90oVFvSMuSj0WwuO85WvJPKa_ErwarD88mFN0OkJPpLrB0Am8hviavMhl_7xIIxK98sxTPKZwJzz8YW89lGwAvFIaIbx2QLi8sY3qvMTzcj37O006BPoxvVrFjzwPU6K81tFBPGzcrLqmg-W87ZY8vObYj7wEvjy9cZ0ku48Nsjtmpl09Z71gvcOnCrzPxAW9e24RPWUeAL3WIAm9YHDYPCTflrrcuEk8Q2KaPS-tyrze3hG936JAvGhqoDy_gWa9v1uyOw6PFz2QglG86DfuPEX9yTzazhw9sFSuPCEI3ru4_wq9QrHNPDwZDTz5UTK9DS02vYLw2jy_qh09v1syPNP6Ur0HMwu9HVpcvVgU57wdNJY8uvy_PO6AezxBPK68vHG7vEHahLvnr7S9DAcCvdWCerz9Es67lB25vO-T8bsUFJq84oyTvV8O5TsCIzG7gcrKPH5rkLvQrsQ8KaCOvCXJ1bsmBQO9XP4nPesle71QMJi8qm0Uu8YZTb3nwjw9C2lzvOSyI7xXPcI8sHp0PZZDk7xvUQS9-cZRPZCC0TvoERY9mrVVPOvS-Twez_u72jA0vSo74rwOQCy7nrIMvYcA4juuzKq8C0MJPXRLbj2yjU88FMUuvSv_bD3_1pC9yfAXva_yOj2tCKC769JePRF5DjzIixi9gBl-Pe-8KD0G5Ci9Cc7DPKxEJz1grJc9La4KvaERWb1nppU7rd_oPDdFn7yatag8HSnDvIxfDD0VTcQ83_EHPeBmJ709LCe99qOMvApDP72cAfa8YCHbPGpBITpXsuG8qfiGvKNzBD2QXAI9o5luvXGKijzaQ_K8R5h5PVJpjLug_mI9ZVfyPC_WAT0AI3m9",
+    },       
     "Portraits": {
         "Snoop": "Snoop Dogg.jpg",
         "Snoop Prompt": "Snoop Dogg",
@@ -279,24 +276,32 @@ tabbed_examples = {
         "Donkey": "Donkey.jpg",
         "Donkey Prompt": "Donkey, from Shrek",
     },
-    "NFT's": {
-        "SohoJoe": "SohoJoeEth.jpeg",
-        "SohoJoe Prompt": "SohoJoe.Eth",
-        "Mirai": "Mirai.jpg",
-        "Mirai Prompt": "Mirai from White Rabbit, @shibuyaxyz",
-        "OnChainMonkey": "OnChainMonkey-2278.jpg",
-        "OCM Prompt": "On Chain Monkey",
-        "Wassie": "Wassie 4498.jpeg",
-        "Wassie Prompt": "Wassie by Wassies",
+    "Transforms": {
+        "ColorWheel001": "ColorWheel001.jpg",
+        "ColorWheel001 BW": "ColorWheel001 BW.jpg",
+        "ColorWheel002": "ColorWheel002.jpg",
+        "ColorWheel002 BW": "ColorWheel002 BW.jpg",
     },
-    "Pups": {
-        "Pup1": "pup1.jpg",
-        "Prompt": "Teacup Yorkies",
-        "Pup2": "pup2.jpg",
-        "Pup3": "pup3.jpg",
-        "Pup4": "pup4.jpeg",
-        "Pup5": "pup5.jpg",
+    "CoCo": {
+        "452650": "452650.jpeg",
+        "Prompt 1": "a college dorm with a desk and bunk beds",
+        "371739": "371739.jpeg",
+        "Prompt 2": "a large banana is placed before a stuffed monkey.",
+        "557922": "557922.jpeg",
+        "Prompt 3": "a person sitting on a bench using a cell phone",
+        "540554": "540554.jpeg",
+        "Prompt 4": "two trains are coming down the tracks, a steam engine and a modern train.",
     },
+    # "NFT's": {
+    #     "SohoJoe": "SohoJoeEth.jpeg",
+    #     "SohoJoe Prompt": "SohoJoe.Eth",
+    #     "Mirai": "Mirai.jpg",
+    #     "Mirai Prompt": "Mirai from White Rabbit, @shibuyaxyz",
+    #     "OnChainMonkey": "OnChainMonkey-2278.jpg",
+    #     "OCM Prompt": "On Chain Monkey",
+    #     "Wassie": "Wassie 4498.jpeg",
+    #     "Wassie Prompt": "Wassie by Wassies",
+    # },
 }
 
 
@@ -311,25 +316,26 @@ with gr.Blocks() as demo:
 
 A tool for exploring CLIP embedding space.
 
-Try uploading a few images and/or add some text prompts and click generate images.
+Try uploading a few images and/or add some text prompts and search the embedding space.
 """)
         with gr.Column(scale=2, min_width=(tile_size+20)*3):
             with gr.Row():
                 with gr.Column(scale=1, min_width=tile_size):
-                    gr.Markdown("## Input 1")
+                    gr.Markdown("## Pup in cup:")
                 with gr.Column(scale=1, min_width=tile_size):
-                    gr.Markdown("## Input 2")
+                    gr.Markdown("## - 'Teacup'")
                 with gr.Column(scale=1, min_width=tile_size):
-                    gr.Markdown("## Generates:")
+                    gr.Markdown("## = Pup")
             for example in examples:
                 with gr.Row():
                     for example in example:
                         with gr.Column(scale=1, min_width=tile_size):
-                            local_path = os.path.join(image_folder, example)
-                            gr.Image(
-                                value = local_path, shape=(tile_size,tile_size), 
-                                show_label=False, interactive=False) \
-                                .style(height=tile_size, width=tile_size)
+                            if len(example):
+                                local_path = os.path.join(image_folder, example)
+                                gr.Image(
+                                    value = local_path, shape=(tile_size,tile_size), 
+                                    show_label=False, interactive=False) \
+                                    .style(height=tile_size, width=tile_size)
 
     with gr.Row():
         for i in range(max_tabs):