From 2a274b8817d8c49afca36e6711a7a4f89bc89443 Mon Sep 17 00:00:00 2001
From: juston <96567547+justonf@users.noreply.github.com>
Date: Mon, 13 May 2024 13:28:44 -0400
Subject: [PATCH] Update existing cookbooks to use gpt-4o (#1197)
---
authors.yaml | 4 +
..._with_vision_for_video_understanding.ipynb | 37 +-
...ine_GPT4o_with_RAG_Outfit_Assistant.ipynb} | 164 ++--
examples/gpt4o/data/keynote_recap.mp4 | Bin 0 -> 8521774 bytes
examples/gpt4o/data/triangle.png | Bin 0 -> 8037 bytes
examples/gpt4o/introduction_to_gpt4o.ipynb | 819 ++++++++++++++++++
registry.yaml | 14 +-
7 files changed, 940 insertions(+), 98 deletions(-)
rename examples/{How_to_combine_GPT4v_with_RAG_Outfit_Assistant.ipynb => How_to_combine_GPT4o_with_RAG_Outfit_Assistant.ipynb} (55%)
create mode 100644 examples/gpt4o/data/keynote_recap.mp4
create mode 100644 examples/gpt4o/data/triangle.png
create mode 100644 examples/gpt4o/introduction_to_gpt4o.ipynb
diff --git a/authors.yaml b/authors.yaml
index 54c948a..655d849 100644
--- a/authors.yaml
+++ b/authors.yaml
@@ -93,3 +93,7 @@ royziv11:
website: "https://www.linkedin.com/in/roy-ziv-a46001149/"
avatar: "https://media.licdn.com/dms/image/D5603AQHkaEOOGZWtbA/profile-displayphoto-shrink_400_400/0/1699500606122?e=1716422400&v=beta&t=wKEIx-vTEqm9wnqoC7-xr1WqJjghvcjjlMt034hXY_4"
+justonf:
+ name: "Juston Forte"
+ website: "www.linkedin.com/in/justonforte"
+ avatar: "https://avatars.githubusercontent.com/u/96567547?s=400&u=08b9757200906ab12e3989b561cff6c4b95a12cb&v=4"
diff --git a/examples/GPT_with_vision_for_video_understanding.ipynb b/examples/GPT_with_vision_for_video_understanding.ipynb
index b0b7cb9..6f1fe38 100644
--- a/examples/GPT_with_vision_for_video_understanding.ipynb
+++ b/examples/GPT_with_vision_for_video_understanding.ipynb
@@ -4,12 +4,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Processing and narrating a video with GPT's visual capabilities and the TTS API\n",
+ "# Processing and narrating a video with GPT-4o's visual capabilities and the TTS API\n",
"\n",
- "This notebook demonstrates how to use GPT's visual capabilities with a video. GPT-4 doesn't take videos as input directly, but we can use vision and the new 128K context window to describe the static frames of a whole video at once. We'll walk through two examples:\n",
+ "This notebook demonstrates how to use GPT's visual capabilities with a video. GPT-4o doesn't take videos as input directly, but we can use vision and the 128K context window to describe the static frames of a whole video at once. We'll walk through two examples:\n",
"\n",
- "1. Using GPT-4 to get a description of a video\n",
- "2. Generating a voiceover for a video with GPT-4 and the TTS API\n"
+ "1. Using GPT-4o to get a description of a video\n",
+ "2. Generating a voiceover for a video with GPT-o and the TTS API\n"
]
},
{
@@ -118,9 +118,10 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "\"🐺 Survival of the Fittest: An Epic Tale in the Snow ❄️ - Witness the intense drama of nature as a pack of wolves face off against mighty bison in a harsh winter landscape. This raw footage captures the essence of the wild where every creature fights for survival. With each frame, experience the tension, the strategy, and the sheer force exerted in this life-or-death struggle. See nature's true colors in this gripping encounter on the snowy plains. 🦬\"\n",
+ "Title: \"Epic Wildlife Showdown: Wolves vs. Bison in the Snow\"\n",
"\n",
- "Remember to respect wildlife and nature. This video may contain scenes that some viewers might find intense or distressing, but they depict natural animal behaviors important for ecological studies and understanding the reality of life in the wilderness.\n"
+ "Description: \n",
+ "Witness the raw power and strategy of nature in this intense and breathtaking video! A pack of wolves face off against a herd of bison in a dramatic battle for survival set against a stunning snowy backdrop. See how the wolves employ their cunning tactics while the bison demonstrate their strength and solidarity. This rare and unforgettable footage captures the essence of the wild like never before. Who will prevail in this ultimate test of endurance and skill? Watch to find out and experience the thrill of the wilderness! 🌨️🦊🐂 #Wildlife #NatureDocumentary #AnimalKingdom #SurvivalOfTheFittest #NatureLovers\n"
]
}
],
@@ -135,7 +136,7 @@
" },\n",
"]\n",
"params = {\n",
- " \"model\": \"gpt-4-vision-preview\",\n",
+ " \"model\": \"gpt-4o\",\n",
" \"messages\": PROMPT_MESSAGES,\n",
" \"max_tokens\": 200,\n",
"}\n",
@@ -167,15 +168,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "In the vast, white expanse of the northern wilderness, a drama as old as time unfolds. Here, amidst the silence of the snow, the wolf pack circles, their breaths visible as they cautiously approach their formidable quarry, the bison. These wolves are practiced hunters, moving with strategic precision, yet the bison, a titan of strength, stands resolute, a force to be reckoned with.\n",
+ "In the frozen expanses of the North American wilderness, a battle unfolds—a testament to the harsh realities of survival.\n",
"\n",
- "As tension crackles in the frozen air, the wolves close in, their eyes locked on their target. The bison, wary of every movement, prepares to defend its life. It's a perilous dance between predator and prey, where each step could be the difference between life and death.\n",
+ "The pack of wolves, relentless and coordinated, closes in on the mighty bison. Exhausted and surrounded, the bison relies on its immense strength and bulk to fend off the predators.\n",
"\n",
- "In an instant, the quiet of the icy landscape is shattered. The bison charges, a desperate bid for survival as the pack swarms. The wolves are relentless, each one aware that their success depends on the strength of the collective. The bison, though powerful, is outnumbered, its massive form stirring up clouds of snow as it struggles.\n",
+ "But the wolves are cunning strategists. They work together, each member playing a role in the hunt, nipping at the bison's legs, forcing it into a corner.\n",
"\n",
- "It's an epic battle, a testament to the harsh realities of nature. In these moments, there is no room for error, for either side. The wolves, agile and tenacious, work in unison, their bites a chorus aiming to bring down the great beast. The bison, its every heaving breath a testament to its will to survive, fights fiercely, but the odds are not in its favor.\n",
+ "The alpha female leads the charge, her pack following her cues. They encircle their prey, tightening the noose with every passing second.\n",
"\n",
- "With the setting sun casting long shadows over the snow, the outcome is inevitable. Nature, in all its raw beauty and brutality, does not show favor. The wolves, now victors, gather around their prize, their survival in this harsh climate secured for a moment longer. It's a poignant reminder of the circle of life that rules this pristine wilderness, a reminder that every creature plays its part in the enduring saga of the natural world.\n"
+ "The bison makes a desperate attempt to escape, but the wolves latch onto their target, wearing it down through sheer persistence and teamwork.\n",
+ "\n",
+ "In these moments, nature's brutal elegance is laid bare—a primal dance where only the strongest and the most cunning can thrive.\n",
+ "\n",
+ "The bison, now overpowered and exhausted, faces its inevitable fate. The wolves have triumphed, securing a meal that will sustain their pack for days to come.\n",
+ "\n",
+ "And so, the cycle of life continues, as it has for millennia, in this unforgiving land where the struggle for survival is an unending battle.\n"
]
}
],
@@ -190,7 +197,7 @@
" },\n",
"]\n",
"params = {\n",
- " \"model\": \"gpt-4-vision-preview\",\n",
+ " \"model\": \"gpt-4o\",\n",
" \"messages\": PROMPT_MESSAGES,\n",
" \"max_tokens\": 500,\n",
"}\n",
@@ -216,7 +223,7 @@
"text/html": [
"\n",
" \n",
" "
@@ -266,7 +273,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.16"
+ "version": "3.11.8"
}
},
"nbformat": 4,
diff --git a/examples/How_to_combine_GPT4v_with_RAG_Outfit_Assistant.ipynb b/examples/How_to_combine_GPT4o_with_RAG_Outfit_Assistant.ipynb
similarity index 55%
rename from examples/How_to_combine_GPT4v_with_RAG_Outfit_Assistant.ipynb
rename to examples/How_to_combine_GPT4o_with_RAG_Outfit_Assistant.ipynb
index 80659a2..38d3561 100644
--- a/examples/How_to_combine_GPT4v_with_RAG_Outfit_Assistant.ipynb
+++ b/examples/How_to_combine_GPT4o_with_RAG_Outfit_Assistant.ipynb
@@ -4,21 +4,21 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# How to combine GPT-4V with RAG - Create a Clothing Matchmaker App\n",
+ "# How to combine GPT-4o with RAG - Create a Clothing Matchmaker App\n",
"\n",
- "Welcome to the Clothing Matchmaker App Jupyter Notebook! This project demonstrates the power of the GPT-4V model in analyzing images of clothing items and extracting key features such as color, style, and type. The core of our app relies on this advanced image analysis model developed by OpenAI, which enables us to accurately identify the characteristics of the input clothing item.\n",
+ "Welcome to the Clothing Matchmaker App Jupyter Notebook! This project demonstrates the power of the GPT-4o model in analyzing images of clothing items and extracting key features such as color, style, and type. The core of our app relies on this advanced image analysis model developed by OpenAI, which enables us to accurately identify the characteristics of the input clothing item.\n",
"\n",
- "GPT-4V is a model that combines natural language processing with image recognition, allowing it to understand and generate responses based on both text and visual inputs.\n",
+ "GPT-4o is a model that combines natural language processing with image recognition, allowing it to understand and generate responses based on both text and visual inputs.\n",
"\n",
- "Building on the capabilities of the GPT-4V model, we employ a custom matching algorithm and the RAG technique to search our knowledge base for items that complement the identified features. This algorithm takes into account factors like color compatibility and style coherence to provide users with suitable recommendations. Through this notebook, we aim to showcase the practical application of these technologies in creating a clothing recommendation system.\n",
+ "Building on the capabilities of the GPT-4o model, we employ a custom matching algorithm and the RAG technique to search our knowledge base for items that complement the identified features. This algorithm takes into account factors like color compatibility and style coherence to provide users with suitable recommendations. Through this notebook, we aim to showcase the practical application of these technologies in creating a clothing recommendation system.\n",
"\n",
- "Using the combination of GPT-4 Vision + RAG (Retrieval-Augmented Generation) offers several advantages:\n",
+ "Using the combination of GPT-4o + RAG (Retrieval-Augmented Generation) offers several advantages:\n",
"\n",
- "1. **Contextual Understanding**: GPT-4 Vision can analyze input images and understand the context, such as the objects, scenes, and activities depicted. This allows for more accurate and relevant suggestions or information across various domains, whether it's interior design, cooking, or education.\n",
+ "1. **Contextual Understanding**: GPT-4o can analyze input images and understand the context, such as the objects, scenes, and activities depicted. This allows for more accurate and relevant suggestions or information across various domains, whether it's interior design, cooking, or education.\n",
"2. **Rich Knowledge Base**: RAG combines the generative capabilities of GPT-4 with a retrieval component that accesses a large corpus of information across different fields. This means the system can provide suggestions or insights based on a wide range of knowledge, from historical facts to scientific concepts.\n",
"3. **Customization**: The approach allows for easy customization to cater to specific user needs or preferences in various applications. Whether it's tailoring suggestions to a user's taste in art or providing educational content based on a student's learning level, the system can be adapted to deliver personalized experiences.\n",
"\n",
- "Overall, the GPT-4 Vision + RAG approach offers a powerful and flexible solution for various fashion-related applications, leveraging the strengths of both generative and retrieval-based AI techniques."
+ "Overall, the GPT-4o + RAG approach offers a powerful and flexible solution for various fashion-related applications, leveraging the strengths of both generative and retrieval-based AI techniques."
]
},
{
@@ -37,25 +37,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Note: you may need to restart the kernel to use updated packages.\n",
- "Note: you may need to restart the kernel to use updated packages.\n",
- "Note: you may need to restart the kernel to use updated packages.\n",
- "Note: you may need to restart the kernel to use updated packages.\n",
- "Note: you may need to restart the kernel to use updated packages.\n",
- "Note: you may need to restart the kernel to use updated packages.\n",
- "\u001b[31mERROR: Could not find a version that satisfies the requirement concurrent (from versions: none)\u001b[0m\u001b[31m\n",
- "\u001b[0m\u001b[31mERROR: No matching distribution found for concurrent\u001b[0m\u001b[31m\n",
- "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%pip install openai --quiet\n",
"%pip install tenacity --quiet\n",
@@ -86,7 +70,7 @@
"\n",
"client = OpenAI()\n",
"\n",
- "GPT_MODEL = \"gpt-4-vision-preview\"\n",
+ "GPT_MODEL = \"gpt-4o\"\n",
"EMBEDDING_MODEL = \"text-embedding-3-large\"\n",
"EMBEDDING_COST_PER_1K_TOKENS = 0.00013"
]
@@ -241,7 +225,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "1024it [00:02, 438.09it/s] \n"
+ "1024it [00:01, 724.12it/s] \n"
]
},
{
@@ -285,11 +269,11 @@
"4 2012.0 Ethnic Shree Women Multi Colored Patiala \n",
"\n",
" embeddings \n",
- "0 [0.006932149175554514, 0.00035877348273061216,... \n",
- "1 [-0.04372308403253555, -0.008888939395546913, ... \n",
- "2 [-0.02798476628959179, 0.058831773698329926, -... \n",
- "3 [-0.004207939375191927, 0.02941293641924858, -... \n",
- "4 [-0.05236775428056717, 0.015963038429617882, -... \n",
+ "0 [0.006903026718646288, 0.0004031236458104104, ... \n",
+ "1 [-0.04371623694896698, -0.008869604207575321, ... \n",
+ "2 [-0.027989011257886887, 0.05884069576859474, -... \n",
+ "3 [-0.004197604488581419, 0.029409468173980713, ... \n",
+ "4 [-0.05241541564464569, 0.015912825241684914, -... \n",
"Opened dataset successfully. Dataset has 1000 items of clothing along with their embeddings.\n"
]
}
@@ -387,7 +371,7 @@
"source": [
"### Analysis Module\n",
"\n",
- "In this module, we leverage `gpt-4-vision-preview` to analyze input images and extract important features like detailed descriptions, styles, and types. The analysis is performed through a straightforward API call, where we provide the URL of the image for analysis and request the model to identify relevant features.\n",
+ "In this module, we leverage `gpt-4o` to analyze input images and extract important features like detailed descriptions, styles, and types. The analysis is performed through a straightforward API call, where we provide the URL of the image for analysis and request the model to identify relevant features.\n",
"\n",
"To ensure the model returns accurate results, we use specific techniques in our prompt:\n",
"\n",
@@ -402,7 +386,7 @@
"3. **One Shot Example**: \n",
" - To further clarify the expected output, we provide the model with an example input description and a corresponding example output. Although this may increase the number of tokens used (and thus the cost of the call), it helps to guide the model and results in better overall performance.\n",
"\n",
- "By following this structured approach, we aim to obtain precise and useful information from the `gpt-4-vision-preview` model for further analysis and integration into our database."
+ "By following this structured approach, we aim to obtain precise and useful information from the `gpt-4o` model for further analysis and integration into our database."
]
},
{
@@ -518,7 +502,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "{'items': [\"Men's Light Blue Jeans\", \"Men's Black Leather Belt\", \"Men's Brown Loafers\"], 'category': 'Shirts', 'gender': 'Men'}\n"
+ "{'items': [\"Slim Fit Blue Men's Jeans\", \"White Men's Sneakers\", \"Men's Silver Watch\"], 'category': 'Shirts', 'gender': 'Men'}\n"
]
}
],
@@ -562,13 +546,13 @@
"output_type": "stream",
"text": [
"513 Remaining Items\n",
- "[\"Men's Light Blue Jeans\", \"Men's Black Leather Belt\", \"Men's Brown Loafers\"]\n"
+ "[\"Slim Fit Blue Men's Jeans\", \"White Men's Sneakers\", \"Men's Silver Watch\"]\n"
]
},
{
"data": {
"text/html": [
- ""
+ ""
],
"text/plain": [
""
@@ -616,7 +600,7 @@
"source": [
"### Guardrails\n",
"\n",
- "In the context of using Large Language Models (LLMs) like GPT-4V, \"guardrails\" refer to mechanisms or checks put in place to ensure that the model's output remains within desired parameters or boundaries. These guardrails are crucial for maintaining the quality and relevance of the model's responses, especially when dealing with complex or nuanced tasks.\n",
+ "In the context of using Large Language Models (LLMs) like GPT-4o, \"guardrails\" refer to mechanisms or checks put in place to ensure that the model's output remains within desired parameters or boundaries. These guardrails are crucial for maintaining the quality and relevance of the model's responses, especially when dealing with complex or nuanced tasks.\n",
"\n",
"Guardrails are useful for several reasons:\n",
"\n",
@@ -625,7 +609,7 @@
"3. **Safety**: They prevent the model from generating harmful, offensive, or inappropriate content.\n",
"4. **Contextual Relevance**: They ensure that the model's output is contextually relevant to the specific task or domain it is being used for.\n",
"\n",
- "In our case, we are using GPT-4V to analyze fashion images and suggest items that would complement an original outfit. To implement guardrails, we can **refine results**: After obtaining initial suggestions from GPT-4V, we can send the original image and the suggested items back to the model. We can then ask GPT-4V to evaluate whether each suggested item would indeed be a good fit for the original outfit.\n",
+ "In our case, we are using GPT-4o to analyze fashion images and suggest items that would complement an original outfit. To implement guardrails, we can **refine results**: After obtaining initial suggestions from GPT-4o, we can send the original image and the suggested items back to the model. We can then ask GPT-4o to evaluate whether each suggested item would indeed be a good fit for the original outfit.\n",
"\n",
"This gives the model the ability to self-correct and adjust its own output based on feedback or additional information. By implementing these guardrails and enabling self-correction, we can enhance the reliability and usefulness of the model's output in the context of fashion analysis and recommendation.\n",
"\n",
@@ -691,6 +675,60 @@
"execution_count": 15,
"metadata": {},
"outputs": [
+ {
+ "data": {
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+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The items match!\n",
+ "The black shirt and blue jeans create a classic and casual outfit that works well together.\n"
+ ]
+ },
+ {
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HAHA/P1lbfrLrgVJ2ofAfye5/vibuyJ4RE/6/p/x/ZP8AzljIk54iOIjiJTGT4Th2bQ2V2Y4rjeV143K9kHJaKpv4bHL48uZFt4ZmMXw1Pw1Pxx/EyCRnBxu4PzHfB+8t9C64de62U2bXaK03Amtcj2y4WrsFiymvT8ObIBQNejk/kvnzxGT5y7FdU1lRGbDqq+FWRGoiNjV0SPAA1E+E+kMQTGt8J+PCcTE76IifhPn+v5Vf7qv54ic8RHERxEcRHERxEcRHERxEcRHERxEcRHERxEcRHET/2Q==",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The items match!\n",
+ "The black shirt and the white sneakers with red and beige accents can work well together. Black is a versatile color that pairs well with many shoe options, and the white sneakers can add a sporty and casual touch to the outfit.\n"
+ ]
+ },
+ {
+ "data": {
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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The items match!\n",
+ "The black button-up shirt is casual and versatile, making it compatible with the white and red athletic shoes for a relaxed and sporty look.\n"
+ ]
+ },
{
"data": {
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@@ -706,12 +744,12 @@
"output_type": "stream",
"text": [
"The items match!\n",
- "The solid color of the first item tends to be versatile and can pair well with the second item, which appears to be a casual style of pants, creating a cohesive and suitable outfit for casual occasions.\n"
+ "The black shirt pairs well with the light blue jeans, creating a classic and balanced color combination that is casual and stylish.\n"
]
},
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loVqPcNIhTp0L4czmSS2p4txkrWgEk9oKdnXnxxqpzp+EeB+fKyOAcD//2Q==",
"text/plain": [
""
]
@@ -724,43 +762,7 @@
"output_type": "stream",
"text": [
"The items match!\n",
- "The solid color of the shirt provides a versatile base that can easily be matched with different styles and colors of footwear. The shoes appear to be dressy casual loafers, which can complement the shirt for a smart casual look.\n"
- ]
- },
- {
- "data": {
- "image/jpeg": 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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The items match!\n",
- "The dark-colored casual shirt can easily be paired with a variety of shoe styles, and the shoes presented have a casual yet slightly polished design that would complement the shirt for a casual to smart-casual look.\n"
- ]
- },
- {
- "data": {
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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The items match!\n",
- "A dark-colored button-up shirt typically pairs well with dark jeans for a casual yet put-together look.\n"
+ "Both the black shirt and the black watch have a sleek and coordinated look, making them suitable to be worn together as part of an outfit.\n"
]
}
],
@@ -795,7 +797,7 @@
"source": [
"### Conclusion\n",
"\n",
- "In this Jupyter Notebook, we explored the application of GPT-4 with Vision and other machine learning techniques to the domain of fashion. We demonstrated how to analyze images of clothing items, extract relevant features, and use this information to find matching items that complement an original outfit. Through the implementation of guardrails and self-correction mechanisms, we refined the model's suggestions to ensure they are accurate and contextually relevant.\n",
+ "In this Jupyter Notebook, we explored the application of GPT-4o and other machine learning techniques to the domain of fashion. We demonstrated how to analyze images of clothing items, extract relevant features, and use this information to find matching items that complement an original outfit. Through the implementation of guardrails and self-correction mechanisms, we refined the model's suggestions to ensure they are accurate and contextually relevant.\n",
"\n",
"This approach has several practical uses in the real world, including:\n",
"\n",
@@ -803,7 +805,7 @@
"2. **Virtual Wardrobe Applications**: Users can upload images of their own clothing items to create a virtual wardrobe and receive suggestions for new items that match their existing pieces.\n",
"3. **Fashion Design and Styling**: Fashion designers and stylists can use this tool to experiment with different combinations and styles, streamlining the creative process.\n",
"\n",
- "However, one of the considerations to keep in mind is **cost**. The use of LLMs and image analysis models can incur costs, especially if used extensively. It's important to consider the cost-effectiveness of implementing these technologies. `gpt-4-vision-preview` is priced at `$0.01` per 1000 tokens. This adds up to `$0.00255` for one 256px x 256px image.\n",
+ "However, one of the considerations to keep in mind is **cost**. The use of LLMs and image analysis models can incur costs, especially if used extensively. It's important to consider the cost-effectiveness of implementing these technologies.\n",
"\n",
"Overall, this notebook serves as a foundation for further exploration and development in the intersection of fashion and AI, opening doors to more personalized and intelligent fashion recommendation systems."
]
@@ -825,7 +827,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.1"
+ "version": "3.11.8"
}
},
"nbformat": 4,
diff --git a/examples/gpt4o/data/keynote_recap.mp4 b/examples/gpt4o/data/keynote_recap.mp4
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![](\"data/sample_clothes/sample_images/44582.jpg\")
![](\"data/sample_clothes/sample_images/31330.jpg\")
![](\"data/sample_clothes/sample_images/19353.jpg\")
"
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![](\"data/sample_clothes/sample_images/44775.jpg\")
![](\"data/sample_clothes/sample_images/42015.jpg\")
![](\"data/sample_clothes/sample_images/44968.jpg\")
"
],
"text/plain": [
"