From a56069b722261b5bc10954ec89777ff864f0fb32 Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Mon, 6 Oct 2025 13:16:52 -0700 Subject: [PATCH] UI: new build --- ...odel_prices_and_context_window_backup.json | 301 ++++++++++++++++++ .../_buildManifest.js | 0 .../_ssgManifest.js | 0 .../static/chunks/154-f87cf692dcea3018.js | 1 - ...7ff1a4ad9f4.js => 162-ffcd7d9fbb033bdf.js} | 2 +- ...0a98e9413e5.js => 313-0025fb08e386c4b8.js} | 4 +- .../static/chunks/49-b6f167418ea8dbf4.js | 1 + .../static/chunks/851-0a73701a3a0b0187.js | 1 + .../static/chunks/851-bbe6d02cf41bb87a.js | 1 - ...0e07cf314f6.js => 866-9e1803a09e9ae8da.js} | 2 +- ...5eb79b09.js => layout-df4939b4f2ec37d7.js} | 2 +- ...73f13202c4.js => page-d7915f579b770030.js} | 2 +- .../model_hub_table/page-2f23c22a47b20607.js | 1 + .../model_hub_table/page-5d1aa98a47f9e9fd.js | 1 - .../app/onboarding/page-099f7aa4c559d470.js | 1 - .../app/onboarding/page-1fd26064f407ad1d.js | 1 + .../chunks/app/page-5b9ff2d173a47e2c.js | 1 + 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n={method:"GET",headers:{[f]:"Bearer ".concat(e)}},l=await fetch(r,n);if(!l.ok){let e=await l.json(),t=ov(e);throw m(t),Error(t)}let c=await l.json();return console.log(c),c}catch(e){throw console.error("Failed to fetch spend data:",e),e}},eI=async(e,t,o,a)=>{try{let r=s?"".concat(s,"/global/activity/exceptions/deployment"):"/global/activity/exceptions/deployment";t&&o&&(r+="?start_date=".concat(t,"&end_date=").concat(o)),a&&(r+="&model_group=".concat(a));let n={method:"GET",headers:{[f]:"Bearer ".concat(e)}},l=await fetch(r,n);if(!l.ok){let e=await l.json(),t=ov(e);throw m(t),Error(t)}let c=await l.json();return console.log(c),c}catch(e){throw console.error("Failed to fetch spend data:",e),e}},eM=async e=>{try{let t=s?"".concat(s,"/global/spend/models?limit=5"):"/global/spend/models?limit=5",o=await fetch(t,{method:"GET",headers:{[f]:"Bearer ".concat(e),"Content-Type":"application/json"}});if(!o.ok){let e=await o.json(),t=ov(e);throw m(t),Error(t)}let a=await o.json();return 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c=await fetch(r,{method:"GET",headers:{[f]:"Bearer ".concat(e),"Content-Type":"application/json"}});if(!c.ok){let e=await c.json(),t=ov(e);throw m(t),Error(t)}return await c.json()}catch(e){throw console.error("Failed to fetch DAU:",e),e}},oy=async(e,t,o,a)=>{try{let r=s?"".concat(s,"/tag/wau"):"/tag/wau",n=new URLSearchParams;n.append("end_date",(e=>{let t=e.getFullYear(),o=String(e.getMonth()+1).padStart(2,"0"),a=String(e.getDate()).padStart(2,"0");return"".concat(t,"-").concat(o,"-").concat(a)})(t)),a&&a.length>0?a.forEach(e=>{n.append("tag_filters",e)}):o&&n.append("tag_filter",o);let l=n.toString();l&&(r+="?".concat(l));let c=await fetch(r,{method:"GET",headers:{[f]:"Bearer ".concat(e),"Content-Type":"application/json"}});if(!c.ok){let e=await c.json(),t=ov(e);throw m(t),Error(t)}return await c.json()}catch(e){throw console.error("Failed to fetch WAU:",e),e}},oj=async(e,t,o,a)=>{try{let r=s?"".concat(s,"/tag/mau"):"/tag/mau",n=new URLSearchParams;n.append("end_date",(e=>{let 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l=n.toString();l&&(r+="?".concat(l));let c=await fetch(r,{method:"GET",headers:{[f]:"Bearer ".concat(e),"Content-Type":"application/json"}});if(!c.ok){let e=await c.json(),t=ov(e);throw m(t),Error(t)}return await c.json()}catch(e){throw console.error("Failed to fetch per-user analytics:",e),e}},ov=e=>(null==e?void 0:e.error)&&(e.error.message||e.error)||(null==e?void 0:e.message)||(null==e?void 0:e.detail)||(null==e?void 0:e.error)||JSON.stringify(e)},85968:function(e,t,o){o.d(t,{O:function(){return a}});let a=e=>{if(!e)return"An unknown error occurred";if("string"==typeof e)return e;if(e.message)try{let t=JSON.parse(e.message);if(t.error&&t.error.message)return t.error.message;return"string"==typeof t?t:JSON.stringify(t,null,2)}catch(t){return e.message}if(e.response&&e.response.data){if("string"==typeof e.response.data)return e.response.data;if(e.response.data.message)return e.response.data.message;if(e.response.data.error)return"string"==typeof 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Secure;":"";document.cookie="token=; expires=Thu, 01 Jan 1970 00:00:00 UTC; path=".concat(o,"; SameSite=").concat(t,";").concat(a),document.cookie="token=; expires=Thu, 01 Jan 1970 00:00:00 UTC; path=".concat(o,"; domain=").concat(e,"; SameSite=").concat(t,";").concat(a)})}),console.log("After clearing cookies:",document.cookie)}function r(e){let t=document.cookie.split("; ").find(t=>t.startsWith(e+"="));return t?t.split("=")[1]:null}o.d(t,{b:function(){return a},e:function(){return r}})}}]); \ No newline at end of file diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/162-dd6427ff1a4ad9f4.js b/litellm/proxy/_experimental/out/_next/static/chunks/162-ffcd7d9fbb033bdf.js similarity index 50% rename from litellm/proxy/_experimental/out/_next/static/chunks/162-dd6427ff1a4ad9f4.js rename to litellm/proxy/_experimental/out/_next/static/chunks/162-ffcd7d9fbb033bdf.js index 6960e05b16d..8b73990ae43 100644 --- a/litellm/proxy/_experimental/out/_next/static/chunks/162-dd6427ff1a4ad9f4.js +++ b/litellm/proxy/_experimental/out/_next/static/chunks/162-ffcd7d9fbb033bdf.js @@ -1 +1 @@ -"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[162],{36724:function(e,t,n){n.d(t,{Dx:function(){return i.Z},Zb:function(){return s.Z},xv:function(){return r.Z},zx:function(){return a.Z}});var a=n(20831),s=n(12514),r=n(84264),i=n(96761)},19130:function(e,t,n){n.d(t,{RM:function(){return s.Z},SC:function(){return l.Z},iA:function(){return a.Z},pj:function(){return r.Z},ss:function(){return i.Z},xs:function(){return o.Z}});var a=n(21626),s=n(97214),r=n(28241),i=n(58834),o=n(69552),l=n(71876)},88658:function(e,t,n){n.d(t,{L:function(){return s}});var a=n(49817);let s=e=>{let t;let{apiKeySource:n,accessToken:s,apiKey:r,inputMessage:i,chatHistory:o,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedMCPTools:m,endpointType:p,selectedModel:u,selectedSdk:g}=e,x="session"===n?s:r,h=window.location.origin,f=i||"Your prompt here",_=f.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),b=o.filter(e=>!e.isImage).map(e=>{let{role:t,content:n}=e;return{role:t,content:n}}),v={};l.length>0&&(v.tags=l),c.length>0&&(v.vector_stores=c),d.length>0&&(v.guardrails=d);let j=u||"your-model-name",y="azure"===g?'import openai\n\nclient = openai.AzureOpenAI(\n api_key="'.concat(x||"YOUR_LITELLM_API_KEY",'",\n azure_endpoint="').concat(h,'",\n api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n api_key="'.concat(x||"YOUR_LITELLM_API_KEY",'",\n base_url="').concat(h,'"\n)');switch(p){case a.KP.CHAT:{let e=Object.keys(v).length>0,n="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}let a=b.length>0?b:[{role:"user",content:f}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n model="'.concat(j,'",\n messages=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.chat.completions.create(\n# model="').concat(j,'",\n# messages=[\n# {\n# "role": "user",\n# "content": [\n# {\n# "type": "text",\n# "text": "').concat(_,'"\n# },\n# {\n# "type": "image_url",\n# "image_url": {\n# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}\n# }\n# }\n# ]\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(v).length>0,n="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}let a=b.length>0?b:[{role:"user",content:f}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(j,'",\n input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(j,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(_,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===g?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(j,'",\n prompt="').concat(i,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===g?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. 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a=b.length>0?b:[{role:"user",content:f}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(j,'",\n input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(j,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(_,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===g?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(j,'",\n prompt="').concat(i,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===g?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. 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