Process no more than 10 images at a time

Fashion_MVP_v0_0_2.ipynb

@@ -38,25 +38,25 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "Os5BuiF_0kqI",
-        "outputId": "9c923a49-6f7d-4553-c142-ad2937004f65"
+        "outputId": "fd709473-ef03-45fd-965c-3a909b9baed7"
       },
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.1/17.1 MB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m91.9/91.9 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.1/17.1 MB\u001b[0m \u001b[31m19.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m91.9/91.9 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m313.4/313.4 kB\u001b[0m \u001b[31m23.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m144.8/144.8 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.7/8.7 MB\u001b[0m \u001b[31m40.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.8/60.8 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.9/129.9 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.9/71.9 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m313.6/313.6 kB\u001b[0m \u001b[31m14.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m144.8/144.8 kB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.7/8.7 MB\u001b[0m \u001b[31m42.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.8/60.8 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.9/129.9 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.9/71.9 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
           ]
         }
@@ -73,14 +73,14 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "XJ2uHTD8bE0x",
-        "outputId": "13c89046-6a87-4276-e3b4-b8ec7550e18e"
+        "outputId": "e924facd-a0dc-4ccc-9657-7b21ca5e970f"
       },
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/266.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.6/266.9 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m266.9/266.9 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/267.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m \u001b[32m194.6/267.1 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m267.1/267.1 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[?25h"
           ]
         }
@@ -99,7 +99,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "vZ6dU45cr7-a",
-        "outputId": "7eb5a466-03d2-462a-d5e4-4010a1817691"
+        "outputId": "b33ed1e3-10d3-4cd8-f3b0-4b73d770f6e4"
       },
       "execution_count": 3,
       "outputs": [
@@ -112,7 +112,7 @@
             "remote: Counting objects: 100% (34/34), done.\u001b[K\n",
             "remote: Compressing objects: 100% (26/26), done.\u001b[K\n",
             "remote: Total 34 (delta 8), reused 32 (delta 6), pack-reused 0\u001b[K\n",
-            "Receiving objects: 100% (34/34), 1.79 MiB | 6.82 MiB/s, done.\n",
+            "Receiving objects: 100% (34/34), 1.79 MiB | 5.19 MiB/s, done.\n",
             "Resolving deltas: 100% (8/8), done.\n"
           ]
         }
@@ -128,7 +128,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "T69CQRLqsBAj",
-        "outputId": "bdae9b62-3268-458e-cf9e-48041fda46d5"
+        "outputId": "a68f1758-4c49-4f2b-fe6a-1d89dfc84cee"
       },
       "execution_count": 4,
       "outputs": [
@@ -136,10 +136,10 @@
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m157.0/157.0 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.3/199.3 kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.5/61.5 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m123.2/123.2 kB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m157.0/157.0 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.3/199.3 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.5/61.5 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m123.2/123.2 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[?25h"
           ]
         }
@@ -332,7 +332,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 20,
+      "execution_count": 13,
       "metadata": {
         "id": "gX7FFN4Ocfj3"
       },
@@ -341,6 +341,9 @@
         "#|export\n",
         "def process_brand_images(files, brand_name, style_name, initial_prompt, brand_prompt):\n",
         "\n",
+        "    if len(files) > 10:\n",
+        "        return \"Please use no more than 10 images\"\n",
+        "\n",
         "    # global brand_response\n",
         "\n",
         "    # global brand_images_list\n",
@@ -371,7 +374,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 21,
+      "execution_count": 14,
       "metadata": {
         "id": "yCSpReOxgjcO"
       },
@@ -380,6 +383,9 @@
         "#|export\n",
         "def process_style_images(files, brand_name, style_name, initial_prompt, brand_prompt, brand_response, style_prompt):\n",
         "\n",
+        "    if len(files) > 10:\n",
+        "        return \"Please use no more than 10 images\"\n",
+        "\n",
         "    # global style_response\n",
         "\n",
         "    # global style_images_list\n",
@@ -417,7 +423,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 22,
+      "execution_count": 10,
       "metadata": {
         "id": "Xoh8vf36PhOd"
       },
@@ -530,7 +536,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 23,
+      "execution_count": 11,
       "metadata": {
         "id": "9wmX3cjy0eIt"
       },
@@ -591,60 +597,55 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 15,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 802
+          "height": 660
         },
         "id": "5SMuBifpbxmv",
-        "outputId": "6677bde0-7eb3-4a45-c1c4-7751658f25ed"
+        "outputId": "7813219d-276d-40b4-81e9-0ce468faaf45"
       },
       "outputs": [
         {
-          "metadata": {
-            "tags": null
-          },
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
             "\n",
             "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
-            "Running on public URL: https://b204be3ae78dd427f9.gradio.live\n",
+            "Running on public URL: https://0347ce40842cc0d81b.gradio.live\n",
             "\n",
             "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
           ]
         },
         {
+          "output_type": "display_data",
           "data": {
-            "text/html": [
-              "<div><iframe src=\"https://b204be3ae78dd427f9.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
-            ],
             "text/plain": [
               "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<div><iframe src=\"https://0347ce40842cc0d81b.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
             ]
           },
-          "metadata": {},
-          "output_type": "display_data"
+          "metadata": {}
         },
         {
-          "metadata": {
-            "tags": null
-          },
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
-            "namespace dalle {\n",
-            "\n",
-            "type text2im = ({\n",
-            "  size: \"1024x1792\",\n",
-            "  prompt: \"Imagine a model in a minimalist, modern pose, embodying the BLCV brand's aesthetic. The model is wearing a tailored denim maxi skirt, featuring a high waist and a front slit, paired with a crisp, white, fitted t-shirt tucked in. The outfit is completed with simple, leather ankle boots. The model's look is accessorized with minimal jewelry, emphasizing a clean and sophisticated style. The background is a simple gradient of white shades, focusing all attention on the outfit and the model's pose. The overall vibe is chic, with a nod to classic denim fashion, updated for a contemporary audience.\",\n",
-            "}) => any;\n",
-            "\n",
-            "}\n",
-            "Imagine a model in a minimalist, modern pose, embodying the BLCV brand's aesthetic. The model is wearing a tailored denim maxi skirt, featuring a high waist and a front slit, paired with a crisp, white, fitted t-shirt tucked in. The outfit is completed with simple, leather ankle boots. The model's look is accessorized with minimal jewelry, emphasizing a clean and sophisticated style. The background is a simple gradient of white shades, focusing all attention on the outfit and the model's pose. The overall vibe is chic, with a nod to classic denim fashion, updated for a contemporary audience.\n"
+            "Keyboard interruption in main thread... closing server.\n",
+            "Killing tunnel 127.0.0.1:7860 <> https://0347ce40842cc0d81b.gradio.live\n"
           ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": []
+          },
+          "metadata": {},
+          "execution_count": 15
         }
       ],
       "source": [
@@ -657,7 +658,11 @@
   ],
   "metadata": {
     "colab": {
-      "provenance": []
+      "provenance": [],
+      "collapsed_sections": [
+        "JIg5wmXwfgM4",
+        "umRePYg3cUSy"
+      ]
     },
     "kernelspec": {
       "display_name": "Python 3",

app.py

@@ -150,6 +150,9 @@ namespace dalle {
 # %% Fashion_MVP_v0_0_2.ipynb 12
 def process_brand_images(files, brand_name, style_name, initial_prompt, brand_prompt):
 
+    if len(files) > 10:
+        return "Please use no more than 10 images"
+
     # global brand_response
 
     # global brand_images_list
@@ -180,6 +183,9 @@ def process_brand_images(files, brand_name, style_name, initial_prompt, brand_pr
 # %% Fashion_MVP_v0_0_2.ipynb 13
 def process_style_images(files, brand_name, style_name, initial_prompt, brand_prompt, brand_response, style_prompt):
 
+    if len(files) > 10:
+        return "Please use no more than 10 images"
+
     # global style_response
 
     # global style_images_list