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rename to litellm/proxy/_experimental/out/_next/static/VkHqUpZa51X24h1lIehBx/_ssgManifest.js
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/0-w0rr3df0htc.js b/litellm/proxy/_experimental/out/_next/static/chunks/0-w0rr3df0htc.js
deleted file mode 100644
index a1f3017d4d7..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/0-w0rr3df0htc.js
+++ /dev/null
@@ -1 +0,0 @@
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index 00000000000..0a1f88f56f4
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deleted file mode 100644
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new file mode 100644
index 00000000000..8dba80201f2
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/0_8vcd9i7eo1r.js b/litellm/proxy/_experimental/out/_next/static/chunks/0_8vcd9i7eo1r.js
deleted file mode 100644
index d53e80e2dae..00000000000
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deleted file mode 100644
index e7c5d36e415..00000000000
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deleted file mode 100644
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- default_headers=${JSON.stringify(x,null,2).replace(/\n/g,"\n ")}`:"",C="azure"===f?`import openai
-
-client = openai.AzureOpenAI(
- api_key="${_||"YOUR_LITELLM_API_KEY"}",
- azure_endpoint="${b}",
- api_version="2024-02-01"${k}
-)`:`import openai
-
-client = openai.OpenAI(
- api_key="${_||"YOUR_LITELLM_API_KEY"}",
- base_url="${b}"${k}
-)`;switch(c){case t.EndpointType.CHAT:{let e=Object.keys($).length>0,t="";if(e){let e=JSON.stringify({metadata:$},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=`,
- extra_body=${e}`}let i=w.length>0?w:[{role:"user",content:j}];a=`
-import base64
-
-# Helper function to encode images to base64
-def encode_image(image_path):
- with open(image_path, "rb") as image_file:
- return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Example with text only
-response = client.chat.completions.create(
- model="${v}",
- messages=${JSON.stringify(i,null,4)}${t}
-)
-
-print(response)
-
-# Example with image or PDF (uncomment and provide file path to use)
-# base64_file = encode_image("path/to/your/file.jpg") # or .pdf
-# response_with_file = client.chat.completions.create(
-# model="${v}",
-# messages=[
-# {
-# "role": "user",
-# "content": [
-# {
-# "type": "text",
-# "text": "${N}"
-# },
-# {
-# "type": "image_url",
-# "image_url": {
-# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}
-# }
-# }
-# ]
-# }
-# ]${t}
-# )
-# print(response_with_file)
-`;break}case t.EndpointType.RESPONSES:{let e=Object.keys($).length>0,t="";if(e){let e=JSON.stringify({metadata:$},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=`,
- extra_body=${e}`}let i=w.length>0?w:[{role:"user",content:j}];a=`
-import base64
-
-# Helper function to encode images to base64
-def encode_image(image_path):
- with open(image_path, "rb") as image_file:
- return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Example with text only
-response = client.responses.create(
- model="${v}",
- input=${JSON.stringify(i,null,4)}${t}
-)
-
-print(response.output_text)
-
-# Example with image or PDF (uncomment and provide file path to use)
-# base64_file = encode_image("path/to/your/file.jpg") # or .pdf
-# response_with_file = client.responses.create(
-# model="${v}",
-# input=[
-# {
-# "role": "user",
-# "content": [
-# {"type": "input_text", "text": "${N}"},
-# {
-# "type": "input_image",
-# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}
-# },
-# ],
-# }
-# ]${t}
-# )
-# print(response_with_file.output_text)
-`;break}case t.EndpointType.IMAGE:a="azure"===f?`
-# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
-# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
-# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
-import os
-import requests
-import json
-import time
-from PIL import Image
-
-result = client.images.generate(
- model="${v}",
- prompt="${n}",
- n=1
-)
-
-json_response = json.loads(result.model_dump_json())
-
-# Set the directory for the stored image
-image_dir = os.path.join(os.curdir, 'images')
-
-# If the directory doesn't exist, create it
-if not os.path.isdir(image_dir):
- os.mkdir(image_dir)
-
-# Initialize the image path
-image_filename = f"generated_image_{int(time.time())}.png"
-image_path = os.path.join(image_dir, image_filename)
-
-try:
- # Retrieve the generated image
- if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
- image_url = json_response["data"][0]["url"]
- generated_image = requests.get(image_url).content
- with open(image_path, "wb") as image_file:
- image_file.write(generated_image)
-
- print(f"Image saved to {image_path}")
- # Display the image
- image = Image.open(image_path)
- image.show()
- else:
- print("Could not find image URL in response.")
- print("Full response:", json_response)
-except Exception as e:
- print(f"An error occurred: {e}")
- print("Full response:", json_response)
-`:`
-import base64
-import os
-import time
-import json
-from PIL import Image
-import requests
-
-# Helper function to encode images to base64
-def encode_image(image_path):
- with open(image_path, "rb") as image_file:
- return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Helper function to create a file (simplified for this example)
-def create_file(image_path):
- # In a real implementation, this would upload the file to OpenAI
- # For this example, we'll just return a placeholder ID
- return f"file_{os.path.basename(image_path).replace('.', '_')}"
-
-# The prompt entered by the user
-prompt = "${N}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
- model="${v}",
- input=[
- {
- "role": "user",
- "content": [
- {"type": "input_text", "text": prompt},
- {
- "type": "input_image",
- "image_url": f"data:image/jpeg;base64,{base64_image1}",
- },
- {
- "type": "input_image",
- "image_url": f"data:image/jpeg;base64,{base64_image2}",
- },
- {
- "type": "input_image",
- "file_id": file_id1,
- },
- {
- "type": "input_image",
- "file_id": file_id2,
- }
- ],
- }
- ],
- tools=[{"type": "image_generation"}],
-)
-
-# Process the response
-image_generation_calls = [
- output
- for output in response.output
- if output.type == "image_generation_call"
-]
-
-image_data = [output.result for output in image_generation_calls]
-
-if image_data:
- image_base64 = image_data[0]
- image_filename = f"edited_image_{int(time.time())}.png"
- with open(image_filename, "wb") as f:
- f.write(base64.b64decode(image_base64))
- print(f"Image saved to {image_filename}")
-else:
- # If no image is generated, there might be a text response with an explanation
- text_response = [output.text for output in response.output if hasattr(output, 'text')]
- if text_response:
- print("No image generated. Model response:")
- print("\\n".join(text_response))
- else:
- print("No image data found in response.")
- print("Full response for debugging:")
- print(response)
-`;break;case t.EndpointType.IMAGE_EDITS:a="azure"===f?`
-import base64
-import os
-import time
-import json
-from PIL import Image
-import requests
-
-# Helper function to encode images to base64
-def encode_image(image_path):
- with open(image_path, "rb") as image_file:
- return base64.b64encode(image_file.read()).decode('utf-8')
-
-# The prompt entered by the user
-prompt = "${N}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
- model="${v}",
- input=[
- {
- "role": "user",
- "content": [
- {"type": "input_text", "text": prompt},
- {
- "type": "input_image",
- "image_url": f"data:image/jpeg;base64,{base64_image1}",
- },
- {
- "type": "input_image",
- "image_url": f"data:image/jpeg;base64,{base64_image2}",
- },
- {
- "type": "input_image",
- "file_id": file_id1,
- },
- {
- "type": "input_image",
- "file_id": file_id2,
- }
- ],
- }
- ],
- tools=[{"type": "image_generation"}],
-)
-
-# Process the response
-image_generation_calls = [
- output
- for output in response.output
- if output.type == "image_generation_call"
-]
-
-image_data = [output.result for output in image_generation_calls]
-
-if image_data:
- image_base64 = image_data[0]
- image_filename = f"edited_image_{int(time.time())}.png"
- with open(image_filename, "wb") as f:
- f.write(base64.b64decode(image_base64))
- print(f"Image saved to {image_filename}")
-else:
- # If no image is generated, there might be a text response with an explanation
- text_response = [output.text for output in response.output if hasattr(output, 'text')]
- if text_response:
- print("No image generated. Model response:")
- print("\\n".join(text_response))
- else:
- print("No image data found in response.")
- print("Full response for debugging:")
- print(response)
-`:`
-import base64
-import os
-import time
-
-# Helper function to encode images to base64
-def encode_image(image_path):
- with open(image_path, "rb") as image_file:
- return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Helper function to create a file (simplified for this example)
-def create_file(image_path):
- # In a real implementation, this would upload the file to OpenAI
- # For this example, we'll just return a placeholder ID
- return f"file_{os.path.basename(image_path).replace('.', '_')}"
-
-# The prompt entered by the user
-prompt = "${N}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
- model="${v}",
- input=[
- {
- "role": "user",
- "content": [
- {"type": "input_text", "text": prompt},
- {
- "type": "input_image",
- "image_url": f"data:image/jpeg;base64,{base64_image1}",
- },
- {
- "type": "input_image",
- "image_url": f"data:image/jpeg;base64,{base64_image2}",
- },
- {
- "type": "input_image",
- "file_id": file_id1,
- },
- {
- "type": "input_image",
- "file_id": file_id2,
- }
- ],
- }
- ],
- tools=[{"type": "image_generation"}],
-)
-
-# Process the response
-image_generation_calls = [
- output
- for output in response.output
- if output.type == "image_generation_call"
-]
-
-image_data = [output.result for output in image_generation_calls]
-
-if image_data:
- image_base64 = image_data[0]
- image_filename = f"edited_image_{int(time.time())}.png"
- with open(image_filename, "wb") as f:
- f.write(base64.b64decode(image_base64))
- print(f"Image saved to {image_filename}")
-else:
- # If no image is generated, there might be a text response with an explanation
- text_response = [output.text for output in response.output if hasattr(output, 'text')]
- if text_response:
- print("No image generated. Model response:")
- print("\\n".join(text_response))
- else:
- print("No image data found in response.")
- print("Full response for debugging:")
- print(response)
-`;break;case t.EndpointType.EMBEDDINGS:a=`
-response = client.embeddings.create(
- input="${n||"Your string here"}",
- model="${v}",
- encoding_format="base64" # or "float"
-)
-
-print(response.data[0].embedding)
-`;break;case t.EndpointType.TRANSCRIPTION:a=`
-# Open the audio file
-audio_file = open("path/to/your/audio/file.mp3", "rb")
-
-# Make the transcription request
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-
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/0ayyip3c-drm1.js b/litellm/proxy/_experimental/out/_next/static/chunks/0ayyip3c-drm1.js
new file mode 100644
index 00000000000..b4ec960b901
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/0ayyip3c-drm1.js
@@ -0,0 +1 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/0bks94633rs4s.js b/litellm/proxy/_experimental/out/_next/static/chunks/0bks94633rs4s.js
deleted file mode 100644
index 4b990f3a1f5..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/0bks94633rs4s.js
+++ /dev/null
@@ -1 +0,0 @@
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new file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/0eh6yvm8qswse.js b/litellm/proxy/_experimental/out/_next/static/chunks/0eh6yvm8qswse.js
deleted file mode 100644
index 33b05994e7d..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/0eh6yvm8qswse.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/0eu0cv9hcno4x.js b/litellm/proxy/_experimental/out/_next/static/chunks/0eu0cv9hcno4x.js
new file mode 100644
index 00000000000..962b0586464
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/0eu0cv9hcno4x.js
@@ -0,0 +1,421 @@
+(()=>{"use strict";(globalThis.TURBOPACK||(globalThis.TURBOPACK=[])).push(["object"==typeof document?document.currentScript:void 0,434626,e=>{var t=e.i(271645);let a=t.forwardRef(function(e,a){return t.createElement("svg",Object.assign({xmlns:"http://www.w3.org/2000/svg",fill:"none",viewBox:"0 0 24 24",strokeWidth:2,stroke:"currentColor","aria-hidden":"true",ref:a},e),t.createElement("path",{strokeLinecap:"round",strokeLinejoin:"round",d:"M10 6H6a2 2 0 00-2 2v10a2 2 0 002 2h10a2 2 0 002-2v-4M14 4h6m0 0v6m0-6L10 14"}))});e.s(["f",0,a],434626)},541071,373488,e=>{let t=(0,e.i(475254).f)("ellipsis",[["circle",{cx:"12",cy:"12",r:"1",key:"41hilf"}],["circle",{cx:"19",cy:"12",r:"1",key:"1wjl8i"}],["circle",{cx:"5",cy:"12",r:"1",key:"1pcz8c"}]]);e.s(["f",0,t],373488),e.s(["f",0,t],541071)},546467,e=>{let t=(0,e.i(475254).f)("external-link",[["path",{d:"M15 3h6v6",key:"1q9fwt"}],["path",{d:"M10 14 21 3",key:"gplh6r"}],["path",{d:"M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6",key:"a6xqqp"}]]);e.s(["f",0,t])},778917,e=>{var t=e.i(546467);e.s(["f",()=>t.f])},332102,e=>{let t=(0,e.i(475254).f)("inbox",[["polyline",{points:"22 12 16 12 14 15 10 15 8 12 2 12",key:"o97t9d"}],["path",{d:"M5.45 5.11 2 12v6a2 2 0 0 0 2 2h16a2 2 0 0 0 2-2v-6l-3.45-6.89A2 2 0 0 0 16.76 4H7.24a2 2 0 0 0-1.79 1.11z",key:"oot6mr"}]]);e.s(["f",0,t],332102)},306228,e=>{let t=(0,e.i(475254).f)("link-2",[["path",{d:"M9 17H7A5 5 0 0 1 7 7h2",key:"8i5ue5"}],["path",{d:"M15 7h2a5 5 0 1 1 0 10h-2",key:"1b9ql8"}],["line",{x1:"8",x2:"16",y1:"12",y2:"12",key:"1jonct"}]]);e.s(["f",0,t],306228)},560280,e=>{var t=e.i(843476),a=e.i(271645),r=e.i(618566),i=e.i(976883);function s(){let e=(0,r.useSearchParams)().get("key"),[s,n]=(0,a.useState)(null);return(0,a.useEffect)(()=>{e&&n(e)},[e]),(0,t.jsx)(i.f,{accessToken:s})}e.s(["default",0,function(){return(0,t.jsx)(a.Suspense,{fallback:(0,t.jsx)("div",{className:"flex items-center justify-center min-h-screen",children:"Loading..."}),children:(0,t.jsx)(s,{})})}])},909947,e=>{var t=e.i(865361);e.s(["f",0,e=>{let a,{apiKeySource:r,accessToken:i,apiKey:s,inputMessage:n,chatHistory:o,selectedTags:l,selectedVectorStores:p,selectedGuardrails:d,selectedPolicies:m,selectedVoice:u,endpointType:c,selectedModel:g,selectedSdk:f,proxySettings:h,customHeaders:x}=e,_="session"===r?i:s,b=window.location.origin,y=h?.LITELLM_UI_API_DOC_BASE_URL;y&&y.trim()?b=y:h?.PROXY_BASE_URL&&(b=h.PROXY_BASE_URL);let j=n||"Your prompt here",w=j.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),v=o.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),N={};l.length>0&&(N.tags=l),p.length>0&&(N.vector_stores=p),d.length>0&&(N.guardrails=d),m.length>0&&(N.policies=m);let $=g||"your-model-name",k=x&&Object.keys(x).length>0?`,
+ default_headers=${JSON.stringify(x,null,2).replace(/\n/g,"\n ")}`:"",I="azure"===f?`import openai
+
+client = openai.AzureOpenAI(
+ api_key="${_||"YOUR_LITELLM_API_KEY"}",
+ azure_endpoint="${b}",
+ api_version="2024-02-01"${k}
+)`:`import openai
+
+client = openai.OpenAI(
+ api_key="${_||"YOUR_LITELLM_API_KEY"}",
+ base_url="${b}"${k}
+)`;switch(c){case t.Q.CHAT:{let e=Object.keys(N).length>0,t="";if(e){let e=JSON.stringify({metadata:N},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=`,
+ extra_body=${e}`}let r=v.length>0?v:[{role:"user",content:j}];a=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+ with open(image_path, "rb") as image_file:
+ return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.chat.completions.create(
+ model="${$}",
+ messages=${JSON.stringify(r,null,4)}${t}
+)
+
+print(response)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg") # or .pdf
+# response_with_file = client.chat.completions.create(
+# model="${$}",
+# messages=[
+# {
+# "role": "user",
+# "content": [
+# {
+# "type": "text",
+# "text": "${w}"
+# },
+# {
+# "type": "image_url",
+# "image_url": {
+# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}
+# }
+# }
+# ]
+# }
+# ]${t}
+# )
+# print(response_with_file)
+`;break}case t.Q.RESPONSES:{let e=Object.keys(N).length>0,t="";if(e){let e=JSON.stringify({metadata:N},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=`,
+ extra_body=${e}`}let r=v.length>0?v:[{role:"user",content:j}];a=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+ with open(image_path, "rb") as image_file:
+ return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.responses.create(
+ model="${$}",
+ input=${JSON.stringify(r,null,4)}${t}
+)
+
+print(response.output_text)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg") # or .pdf
+# response_with_file = client.responses.create(
+# model="${$}",
+# input=[
+# {
+# "role": "user",
+# "content": [
+# {"type": "input_text", "text": "${w}"},
+# {
+# "type": "input_image",
+# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}
+# },
+# ],
+# }
+# ]${t}
+# )
+# print(response_with_file.output_text)
+`;break}case t.Q.IMAGE:a="azure"===f?`
+# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
+# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
+# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
+import os
+import requests
+import json
+import time
+from PIL import Image
+
+result = client.images.generate(
+ model="${$}",
+ prompt="${n}",
+ n=1
+)
+
+json_response = json.loads(result.model_dump_json())
+
+# Set the directory for the stored image
+image_dir = os.path.join(os.curdir, 'images')
+
+# If the directory doesn't exist, create it
+if not os.path.isdir(image_dir):
+ os.mkdir(image_dir)
+
+# Initialize the image path
+image_filename = f"generated_image_{int(time.time())}.png"
+image_path = os.path.join(image_dir, image_filename)
+
+try:
+ # Retrieve the generated image
+ if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
+ image_url = json_response["data"][0]["url"]
+ generated_image = requests.get(image_url).content
+ with open(image_path, "wb") as image_file:
+ image_file.write(generated_image)
+
+ print(f"Image saved to {image_path}")
+ # Display the image
+ image = Image.open(image_path)
+ image.show()
+ else:
+ print("Could not find image URL in response.")
+ print("Full response:", json_response)
+except Exception as e:
+ print(f"An error occurred: {e}")
+ print("Full response:", json_response)
+`:`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+ with open(image_path, "rb") as image_file:
+ return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+ # In a real implementation, this would upload the file to OpenAI
+ # For this example, we'll just return a placeholder ID
+ return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${w}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+ model="${$}",
+ input=[
+ {
+ "role": "user",
+ "content": [
+ {"type": "input_text", "text": prompt},
+ {
+ "type": "input_image",
+ "image_url": f"data:image/jpeg;base64,{base64_image1}",
+ },
+ {
+ "type": "input_image",
+ "image_url": f"data:image/jpeg;base64,{base64_image2}",
+ },
+ {
+ "type": "input_image",
+ "file_id": file_id1,
+ },
+ {
+ "type": "input_image",
+ "file_id": file_id2,
+ }
+ ],
+ }
+ ],
+ tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+ output
+ for output in response.output
+ if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+ image_base64 = image_data[0]
+ image_filename = f"edited_image_{int(time.time())}.png"
+ with open(image_filename, "wb") as f:
+ f.write(base64.b64decode(image_base64))
+ print(f"Image saved to {image_filename}")
+else:
+ # If no image is generated, there might be a text response with an explanation
+ text_response = [output.text for output in response.output if hasattr(output, 'text')]
+ if text_response:
+ print("No image generated. Model response:")
+ print("\\n".join(text_response))
+ else:
+ print("No image data found in response.")
+ print("Full response for debugging:")
+ print(response)
+`;break;case t.Q.IMAGE_EDITS:a="azure"===f?`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+ with open(image_path, "rb") as image_file:
+ return base64.b64encode(image_file.read()).decode('utf-8')
+
+# The prompt entered by the user
+prompt = "${w}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+ model="${$}",
+ input=[
+ {
+ "role": "user",
+ "content": [
+ {"type": "input_text", "text": prompt},
+ {
+ "type": "input_image",
+ "image_url": f"data:image/jpeg;base64,{base64_image1}",
+ },
+ {
+ "type": "input_image",
+ "image_url": f"data:image/jpeg;base64,{base64_image2}",
+ },
+ {
+ "type": "input_image",
+ "file_id": file_id1,
+ },
+ {
+ "type": "input_image",
+ "file_id": file_id2,
+ }
+ ],
+ }
+ ],
+ tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+ output
+ for output in response.output
+ if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+ image_base64 = image_data[0]
+ image_filename = f"edited_image_{int(time.time())}.png"
+ with open(image_filename, "wb") as f:
+ f.write(base64.b64decode(image_base64))
+ print(f"Image saved to {image_filename}")
+else:
+ # If no image is generated, there might be a text response with an explanation
+ text_response = [output.text for output in response.output if hasattr(output, 'text')]
+ if text_response:
+ print("No image generated. Model response:")
+ print("\\n".join(text_response))
+ else:
+ print("No image data found in response.")
+ print("Full response for debugging:")
+ print(response)
+`:`
+import base64
+import os
+import time
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+ with open(image_path, "rb") as image_file:
+ return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+ # In a real implementation, this would upload the file to OpenAI
+ # For this example, we'll just return a placeholder ID
+ return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${w}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+ model="${$}",
+ input=[
+ {
+ "role": "user",
+ "content": [
+ {"type": "input_text", "text": prompt},
+ {
+ "type": "input_image",
+ "image_url": f"data:image/jpeg;base64,{base64_image1}",
+ },
+ {
+ "type": "input_image",
+ "image_url": f"data:image/jpeg;base64,{base64_image2}",
+ },
+ {
+ "type": "input_image",
+ "file_id": file_id1,
+ },
+ {
+ "type": "input_image",
+ "file_id": file_id2,
+ }
+ ],
+ }
+ ],
+ tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+ output
+ for output in response.output
+ if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+ image_base64 = image_data[0]
+ image_filename = f"edited_image_{int(time.time())}.png"
+ with open(image_filename, "wb") as f:
+ f.write(base64.b64decode(image_base64))
+ print(f"Image saved to {image_filename}")
+else:
+ # If no image is generated, there might be a text response with an explanation
+ text_response = [output.text for output in response.output if hasattr(output, 'text')]
+ if text_response:
+ print("No image generated. Model response:")
+ print("\\n".join(text_response))
+ else:
+ print("No image data found in response.")
+ print("Full response for debugging:")
+ print(response)
+`;break;case t.Q.EMBEDDINGS:a=`
+response = client.embeddings.create(
+ input="${n||"Your string here"}",
+ model="${$}",
+ encoding_format="base64" # or "float"
+)
+
+print(response.data[0].embedding)
+`;break;case t.Q.TRANSCRIPTION:a=`
+# Open the audio file
+audio_file = open("path/to/your/audio/file.mp3", "rb")
+
+# Make the transcription request
+response = client.audio.transcriptions.create(
+ model="${$}",
+ file=audio_file${n?`,
+ prompt="${n.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""}
+)
+
+print(response.text)
+`;break;case t.Q.SPEECH:a=`
+# Make the text-to-speech request
+response = client.audio.speech.create(
+ model="${$}",
+ input="${n||"Your text to convert to speech here"}",
+ voice="${u}" # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer
+)
+
+# Save the audio to a file
+output_filename = "output_speech.mp3"
+response.stream_to_file(output_filename)
+print(f"Audio saved to {output_filename}")
+
+# Optional: Customize response format and speed
+# response = client.audio.speech.create(
+# model="${$}",
+# input="${n||"Your text to convert to speech here"}",
+# voice="alloy",
+# response_format="mp3", # Options: mp3, opus, aac, flac, wav, pcm
+# speed=1.0 # Range: 0.25 to 4.0
+# )
+# response.stream_to_file("output_speech.mp3")
+`;break;default:a="\n# Code generation for this endpoint is not implemented yet."}return`${I}
+${a}`}])},652272,209261,e=>{var t=e.i(843476),a=e.i(271645),r=e.i(871689),i=e.i(643531),s=e.i(174886),n=e.i(306228),o=e.i(196631);let l=/^[a-zA-Z0-9][a-zA-Z0-9._-]*(\/[a-zA-Z0-9][a-zA-Z0-9._-]*)*$/,p=e=>e.trim().replace(/\/+$/,""),d=/\.(md|markdown|txt|json|ya?ml|toml)$/i,m=/\.zip$/i,u=/^[0-9a-fA-F]{64}$/,c=/^\d{1,3}(\.\d{1,3}){3}$/,g=/^[A-Za-z0-9-]+$/,f=/^[A-Za-z0-9._-]+$/,h=/^https?:\/\//i,x="ssh://",_=/^([a-z0-9._-]+)@([^:/@]+):(?!\/)(.+)$/i,b=e=>e.pathname.split("/").filter(e=>""!==e),y=e=>{try{return new URL(e)}catch{return null}},j=e=>e.hostname.includes(".")&&!e.hostname.startsWith("[")&&!c.test(e.hostname),w=e=>{let t=e.split("/").filter(e=>""!==e);return t[t.length-1]??""},v=e=>e.toLowerCase().replace(/[^a-z0-9-]+/g,"-").replace(/-+/g,"-").replace(/^-+|-+$/g,""),N=(e,t,a,r)=>{let i=p(r??"");return""!==i?l.test(i)?{parsed:{source:"git-subdir",url:t,path:i},label:`${e} subdir — ${t} @ ${i}`,suggestedName:v(w(i))}:null:{parsed:{source:"url",url:t},label:`${e} repo — 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deleted file mode 100644
index fc2db2d7541..00000000000
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new file mode 100644
index 00000000000..077e03d5d72
--- /dev/null
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+curl -X POST --location '/end_user/new' \\
+
+-H 'Authorization: Bearer ' \\
+
+-H 'Content-Type: application/json' \\
+
+-d '{"user_id": "my-customer-id', "budget_id": ""}' # 👈 KEY CHANGE
+
+`,ef=`
+curl -X POST --location '/chat/completions' \\
+
+-H 'Authorization: Bearer ' \\
+
+-H 'Content-Type: application/json' \\
+
+-d '{
+ "model": "gpt-3.5-turbo',
+ "messages":[{"role": "user", "content": "Hey, how's it going?"}],
+ "user": "my-customer-id"
+}' # 👈 KEY CHANGE
+
+`,eb=`from openai import OpenAI
+client = OpenAI(
+ base_url="",
+ api_key=""
+)
+
+completion = client.chat.completions.create(
+ model="gpt-3.5-turbo",
+ messages=[
+ {"role": "system", "content": "You are a helpful assistant."},
+ {"role": "user", "content": "Hello!"}
+ ],
+ user="my-customer-id"
+)
+
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\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/0kleyyiv0t-se.js b/litellm/proxy/_experimental/out/_next/static/chunks/0kleyyiv0t-se.js
new file mode 100644
index 00000000000..c6be8d1ec2f
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/0kleyyiv0t-se.js
@@ -0,0 +1 @@
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--- a/litellm/proxy/_experimental/out/_next/static/chunks/0lwia0t_dwgb-.js
+++ /dev/null
@@ -1 +0,0 @@
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new file mode 100644
index 00000000000..718e88f0126
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+++ b/litellm/proxy/_experimental/out/_next/static/chunks/0mss80dklcn0m.js
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