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CoderzColumn
Приєднався 23 січ 2019
Python | AI | Machine Learning | Deep Learning | Data Science | Data Visualization
CoderzColumn Is Designed For Sharing Knowledge On the Topics Like:
* Python
* Artificial Intelligence (Deep Learning)
* Data Science
* Machine Learning
* Data Visualization
We focus on explaining various concepts related to these topics in simple terms. Our tutorials are short and simple to understand. Our main aim is to get someone started with technologies faster.
You can support us by donating through BuyMeACoffee (Link Below).
www.buymeacoffee.com/coderzcolumn
CoderzColumn: Developed for Developers by Developers for the Betterment of Development.
CoderzColumn Is Designed For Sharing Knowledge On the Topics Like:
* Python
* Artificial Intelligence (Deep Learning)
* Data Science
* Machine Learning
* Data Visualization
We focus on explaining various concepts related to these topics in simple terms. Our tutorials are short and simple to understand. Our main aim is to get someone started with technologies faster.
You can support us by donating through BuyMeACoffee (Link Below).
www.buymeacoffee.com/coderzcolumn
CoderzColumn: Developed for Developers by Developers for the Betterment of Development.
Step-by-Step Guide to Build ReAct Agent using Pure Python | Llama 3.1
Hi, My name is Sunny Solanki, and in this video, I provide a step-by-step guide to building ReAct Agent in Pure Python using Open Source LLM (LlaMa-3.1-70B). A ReAct agent is a framework that combines the reasoning capabilities of large language models (LLMs) with the ability to take action (call appropriate tool). ReAct stands for Reasoning and Action.
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CODE - github.com/sunny2309/react_agent_python
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SUPPORT US - buymeacoffee.com/coderzcolumn
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NEWSLETTER - eepurl.com/gRW2u9
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WEBSITE - coderzcolumn.com
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Important Chapters:
0:00 - Structured Data Extraction using LlaMa-3 Intro
1:49 - Code Start
3:08 - Load LLM
4:40 - Define Tools
8:00 - ReAct Prompt
10:37 - ReAct Agent
18:14 - Write Loop to Automate Thought-Action-Observation Loop
#python #datascience #datasciencetutorial #python #pythonprogramming #pythoncode #pythontutorial #llama3.1 #groqapi #open-source-llms #langchain-data-extraction #llama-3 #reactagent
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CODE - github.com/sunny2309/react_agent_python
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SUPPORT US - buymeacoffee.com/coderzcolumn
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NEWSLETTER - eepurl.com/gRW2u9
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WEBSITE - coderzcolumn.com
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Important Chapters:
0:00 - Structured Data Extraction using LlaMa-3 Intro
1:49 - Code Start
3:08 - Load LLM
4:40 - Define Tools
8:00 - ReAct Prompt
10:37 - ReAct Agent
18:14 - Write Loop to Automate Thought-Action-Observation Loop
#python #datascience #datasciencetutorial #python #pythonprogramming #pythoncode #pythontutorial #llama3.1 #groqapi #open-source-llms #langchain-data-extraction #llama-3 #reactagent
Переглядів: 75
Відео
LangChain RAG over Youtube Videos | LlaMa-3
Переглядів 295Місяць тому
Hi, My name is Sunny Solanki, and in this video, I provide a step-by-step guide to building a RAG app that answers questions from UA-cam videos. We use the famous LLM Apps building framework LangChain for coding. We access LLM (LlaMa-3-70B) through Groq API. You can easily extend this app and add streamlit front end. CODE - github.com/sunny2309/langchain_tutorials/blob/main/RAG over Video.ipynb...
Generate Pydantic & JSON Objects from Text using LlaMa-3 | LlamaIndex | Groq API
Переглядів 384Місяць тому
Hi, My name is Sunny Solanki, and in this video, I provide a step-by-step guide to extracting structured data with Open Source LLMs (LlaMa-3). We use the famous LLM Apps building framework LlamaIndex for coding. We access LLMs through Groq API. Structured data extraction from unstructured data is one of the useful applications of LLMs. CODE - github.com/sunny2309/llamaindex_tutorials/blob/main/...
Build AI Finance Agent using LangChain | LlaMa-3 | Groq API
Переглядів 865Місяць тому
Hi, My name is Sunny Solanki, and in this video, I provide a step-by-step guide to building a finance agent using the famous LLM app-building framework "LangChain". The agent uses the list of finance tools (company profile tool, stock dividend history tool, earnings history tool, top institutional & mutual fund holders tool, split history tool, stock news tool, etc ) to retrieve the latest deta...
LangChain Structured Data (Pydantic & JSON) Extraction | LlaMa-3 | Groq API
Переглядів 6912 місяці тому
Hi, My name is Sunny Solanki, and in this video, I provide a step-by-step guide to structured data extraction with Open Source LLMs (LlaMa-3). We access LLMs through Groq API. Structured data extraction from unstructured data is one of the useful applications of LLMs. CODE - github.com/sunny2309/langchain_tutorials/blob/main/Structured Output from LLMs.ipynb SUPPORT US - buymeacoffee.com/coderz...
LlamaIndex Function Calling Agent with Local LLMs | Llama-3 | Mistral | Ollama
Переглядів 3762 місяці тому
Hi, My name is Sunny Solanki, and in this video, I provide a complete guide to using function calling features with Local LLMs (Llama-3 & Mistral) accessed through Ollama. These native functions can be used by LLMs to answer user queries. CODE - github.com/sunny2309/llamaindex_tutorials/blob/main/Function Calling using Local LLMs (LlamaIndex).ipynb SUPPORT US - Buy Me Coffee - buymeacoffee.com/...
LangChain Function Calling with Open Source LLMs | LlaMa-3 | Groq API
Переглядів 5092 місяці тому
Hi, My name is Sunny Solanki, and in this video, I provide a step-by-step guide to using function calling features with Open Source LLMs (LlaMa-3). We access LLMs through Groq API. These native functions can be used by LLMs to answer user queries. CODE - github.com/sunny2309/langchain_tutorials/blob/main/Function Calling with Open Source LLMs | LlaMa-3.ipynb SUPPORT US - Buy Me Coffee - buymeac...
Llama-CPP-Python: Step-by-step Guide to Run LLMs on Local Machine | Llama-2 | Mistral
Переглядів 4,5 тис.4 місяці тому
Hi, My name is Sunny Solanki, and in this video, I provide a step-by-step guide to running Local LLMs using Python library "llama-cpp-python". I have explained how to use llama-2 and mistral models on local machine. CODE - github.com/sunny2309/llama_cpp_python_tutorial SUPPORT US - Buy Me Coffee - buymeacoffee.com/coderzcolumn NEWSLETTER - eepurl.com/gRW2u9 WEBSITE - coderzcolumn.com Important ...
Complete Guide to Build RAG App using Ollama Python Lib | Local LLM RAG
Переглядів 1,4 тис.5 місяців тому
Hi, My name is Sunny Solanki, and in this video, I provide a step-by-step guide to creating a RAG LLM App using the Python library "Ollama". It is a wrapper around the "Ollama" tool that lets us access Open-Source LLM on the local machine for free. To build our LLM app, we have used open-source LLM "llama-2" with 7B parameters. For storing & searching embeddings of external docs, we have used t...
Chainlit: Guide to Build Chatbot using Python Faster | Ollama | llama2
Переглядів 1,1 тис.5 місяців тому
Hi, My name is Sunny Solanki, and in this video, I provide a step-by-step guide to building a chatbot using "Chainlit" and Ollama. I use "llama2" model with 7B parameters for conversation. The LLM runs on a local machine and is accessed through the Ollama Python Library. The response is displayed in a streaming fashion (typewriter) on a UI like ChatGPT. Feel free to extend the chatbot and add m...
Step-by-Step Guide to Build RAG App using LlamaIndex | Ollama | Llama-2 | Python
Переглядів 1,5 тис.5 місяців тому
Hi, My name is Sunny Solanki, and in this video, I provide a step-by-step guide to creating a RAG LLM App using the Python framework "llamaindex". To build our LLM app, we have used open-source LLM "llama-2" with 7B parameters. To access LLMs, we use a tool named "Ollama". The tutorial is a good starting point for someone new to LlamaIndex and who wants to learn how to create LLM Apps. It's a g...
Chatbot using Gradio & Ollama | llama2 | Local LLM | Python
Переглядів 2,1 тис.5 місяців тому
Hi, My name is Sunny Solanki, and in this video, I provide a step-by-step guide to building a chatbot using Gradio and Ollama. I use "llama2" model with 7B parameters for conversation. The LLM runs on a local machine and is accessed through the Ollama Library. The response is displayed in a streaming fashion (typewriter) on a UI like ChatGPT. Feel free to extend the chatbot and add more functio...
Step-by-Step Guide to Build RAG App using LangChain | Ollama | Llama-2 | Python
Переглядів 2 тис.5 місяців тому
Hi, My name is Sunny Solanki, and in this video, I provide a step-by-step guide to creating a RAG LLM App using the Python framework "langchain". To build our LLM app, we have used open-source LLM "llama-2" with 7B parameters. To access LLMs, we use a tool named "Ollama". The tutorial is a good starting point for someone new to LangChain and wants to learn how to create LLM Apps. It's a good in...
Let's Build Chatbot using Python Libraries Panel & Ollama | No OpenAI API
Переглядів 8685 місяців тому
Hi, My name is Sunny Solanki, and in this video, I provide a step-by-step guide to building a chatbot using Python Libraries "Panel" and "Ollama". I use "llama2" model with 7B parameters for conversation. The LLM is accessed through the Ollama Library. Feel free to extend the chatbot and add more functionalities to it. CODE - github.com/sunny2309/panel_chatbot SUPPORT US - Buy Me Coffee - buyme...
Embedding Bokeh Charts into Your Django App: Step-by-Step Guide
Переглядів 3276 місяців тому
Hi, My name is Sunny Solanki, and in this video, I provide a step-by-step guide on integrating Bokeh Charts into Django Web App. I create a small stock analysis dashboard that displays candlestick chart of historical stock prices to explain integration. CODE - github.com/sunny2309/bokeh_django_integration SUPPORT US - Buy Me Coffee - buymeacoffee.com/coderzcolumn NEWSLETTER - eepurl.com/gRW2u9 ...
Build Chatbot using Streamlit & Ollama | llama2 | No ChatGPT API
Переглядів 4,7 тис.6 місяців тому
Build Chatbot using Streamlit & Ollama | llama2 | No ChatGPT API
Deploy Dash-Plotly Python Dashboard App to Google Cloud (Free): Step-by-step Guide
Переглядів 1,4 тис.6 місяців тому
Deploy Dash-Plotly Python Dashboard App to Google Cloud (Free): Step-by-step Guide
Ollama Python Library: Use LLMs on your Local Computer | llama2 | mistral
Переглядів 1,2 тис.6 місяців тому
Ollama Python Library: Use LLMs on your Local Computer | llama2 | mistral
OpenAI Sora Capabilities, Research, Limitations & Safety | Text-to-video AI
Переглядів 1866 місяців тому
OpenAI Sora Capabilities, Research, Limitations & Safety | Text-to-video AI
Ollama: Run LLMs on your Local Machine for Free | Open Source LLMs | llama2 | codellama | llava
Переглядів 7896 місяців тому
Ollama: Run LLMs on your Local Machine for Free | Open Source LLMs | llama2 | codellama | llava
Let's Talk about AI Buzzword: RAG | Retrieval Augmented Generation Explained
Переглядів 1296 місяців тому
Let's Talk about AI Buzzword: RAG | Retrieval Augmented Generation Explained
Building a Dynamic Stock Analysis Dashboard with Django: A Step-By-Step Guide | AmCharts | Bootstrap
Переглядів 5796 місяців тому
Building a Dynamic Stock Analysis Dashboard with Django: A Step-By-Step Guide | AmCharts | Bootstrap
Embedding Plotly Charts into Django Web Apps: Step by Step Guide | Python
Переглядів 1,3 тис.7 місяців тому
Embedding Plotly Charts into Django Web Apps: Step by Step Guide | Python
Building Multi-Tab Dashboards with Dash-Plotly: A Comprehensive Guide | Sunny Solanki
Переглядів 3,1 тис.7 місяців тому
Building Multi-Tab Dashboards with Dash-Plotly: A Comprehensive Guide | Sunny Solanki
Building Interactive Dash-Plotly Dashboard with Navbar: A Step-by-Step Guide | Python
Переглядів 1,6 тис.8 місяців тому
Building Interactive Dash-Plotly Dashboard with Navbar: A Step-by-Step Guide | Python
Modal Magic: Add Pop-up Windows to Your Plotly-Dash Dashboard
Переглядів 1,2 тис.8 місяців тому
Modal Magic: Add Pop-up Windows to Your Plotly-Dash Dashboard
Step-by-Step Guide to Create Multi-Page Dashboard using Panel | Hvplot | Python
Переглядів 4,4 тис.9 місяців тому
Step-by-Step Guide to Create Multi-Page Dashboard using Panel | Hvplot | Python
Making Sense of Text Classifier Predictions | Interpretation & Explanation
Переглядів 2689 місяців тому
Making Sense of Text Classifier Predictions | Interpretation & Explanation
Optimizing User Experience: Plotly-Dash Dashboard Design with Sidebar | Sunny Solanki
Переглядів 1,8 тис.9 місяців тому
Optimizing User Experience: Plotly-Dash Dashboard Design with Sidebar | Sunny Solanki
Data Insights at a Glance: Using SweetViz for Quick Analysis | Python Tutorial
Переглядів 1,1 тис.Рік тому
Data Insights at a Glance: Using SweetViz for Quick Analysis | Python Tutorial
Does this support Dashboard that deals with MySQL database? Because application needs to CRUD with MySQL database. If so, how to set up? Thank you very much
Gonzalez Ruth Hernandez Brenda Williams Jessica
This concept is excellent! However, here’s how it could be refined: Think should be a tool, allowing the agent to self-query for a plan, methodology, or the next step in a sequence. Observe should return the tool call. Action should represent the selected tool or action to be executed. By treating each component as a tool, we can create any methodology or chain of thoughts. For example: Plan Creation: This could be a tool, functioning as a prompt that queries itself with a task and returns a plan. The prompt manages this tool within the chain. Code Refinement: Similarly, this could also be a tool, where the model queries itself with code for refinement. The only change would be in the prompt, whether it's a tool, chain, or graph. This allows for specialized refiners with strict requirements for correcting or industrializing code. When designing processes for the agent, we could have template plans in the planner. For example, if the user wants to repair some code, the planner could offer tool choices and distribute a boilerplate from a set of pre-defined templates. These templates could be stored as docstrings and selected by the model, with tools accessing the required files without going out of bounds. Open code execution and system command execution should always require human intervention due to potential system risks, hence the use of tools with defined boundaries. A planner could also have access to tool collections, which can be loaded into the agent to perform tasks-essentially passing a toolbox to the model. The central controller bot would access all these tools. Given a menu of tasks, the model could return tool collections, plans, and start nodes, allowing the agent to pass a custom state to a chain and return its output. For example, if tasked to create an app, it would query the general planner bot, which would direct it to the correct planner. This planner would provide the agent with tools, requirements, and the start node, enabling the agent to execute the correct graph (tool) with a state and expected outcome. Initially, I built this process similarly to yours, but after reflecting on the above, I realized an even higher perspective: Our front-end should resemble Dialogflow, adding a personality layer. While it may not be intelligent, it provides the necessary constraints on top of the low-level processes we're currently working on. We should recognize that there are many layers beyond this, not just a UI. The react process (or thought process and selected methodology for a task) is great for long and in-depth tasks with a graph-based structure, but it's not ideal for general chat. Hence, we need a simple front-end with keyword detection and response, a dialog manager. Even RAG (Retrieval-Augmented Generation) should be plugged in at this higher level, as it’s not required unless dealing with in-depth tasks. Basic chat history can keep the model personalized and up-to-date. RAG serves as short/long-term memory, which after optimization, can be fine-tuned into the main LLM. However, it should still be a tool, only used when necessary. A state is enough to perform a task, and chunking and embedding for similarity should be reserved for in-depth queries, not for downloading and summarizing data. Summarization only requires a prompt chain, making it essentially a self-query tool. Sub-agents are tools that self-query the model, with clean chat history solely for tool execution. Only the exchange between the agent and tool is saved, and internal processing within the tool is not returned to the agent-only the final output. This can be shared using verbose mode, where two responses are created: a verbose one with extensive logging and custom tripwire exceptions, or a simple output to preserve minimum token exchange between agents (i.e., only query, state, and output). Tools can also function as conditional nodes, allowing input-based branching within the tool. For example, a refiner tool could call a coder tool to return the refined output to the calling agent. The coder node always returns data to the refiner tool unless explicitly told to return the output without refinement. This prevents nodes that cannot return direct output, enabling many nodes in a graph to be executed as tools. I hope this enhancement to your process resonates with you and perhaps guides your next steps. You may find this approach less memory-intensive, as tools unload after execution, and the agent can be a very small model, with the tools providing the intelligence. A moderate-sized model (7-14B parameters) would be ideal, especially since the same model might be sub-called multiple times (e.g., in the case of a refiner or planner that may call a sub-planner). The latest models have been trained on function calling, tool use, and planning.
Thank you for sharing the step by step guide. Really appreciate your efforts.
Glad it was helpful! Thanks for taking time to comment !!!
you don't even try to explain it like we know this topic already
Very thanks. from South Korea.
Really appreciate your feedback !!! Thank You !!!
@@CoderzColumn I did the same, only the OHLC Chart doesn't show. bokeh library doesn't work in django. No error message. Could you possibly tell me why?
If you have properly followed tutorial then the chart should appear without any issue. Also, I hope you included script tag in HEAD of html page with proper bokeh version. It should be same as bokeh library installed on your PC. <script src="cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js" crossorigin="anonymous"></script> Other than this, I hope you included chart tags this way. <div class="card-body"> {{ scripts | safe }} {{ hist_chart_div | safe }} </div> I would suggest first creating simple django app to understand this and then try it in your app.
@@CoderzColumn Thank you very much for your reply. I'll check out the bokeh version in my venv.
I solved that problem. The cause of the problem was the difference in bokeh version. Thank you very much my teacher.
how to modify the grid distance to diplay all dates in mplfiance?
Incredibly clear and well-structured, making the complex information easy to understand, Thank you SUNNY SOLANKI.
So it just run on our local enviroment, no need internet? But what if we share it on streamlit cloud, can it run as we know streamlit cloud doesn't have high resource or even GPU
Yes. It runs on our local computer and does not need internet for it, Now for sharing it on streamlit cloud is not possible with ollama because streamlit does not let u run ollama there and yes it does not have high resource or GPU. You can deploy Ollama on some cloud instance (Gcloud or AWS) and then access it through API. You can then replace code to access ollama through that cloud instance based URL (base_url parameter). That's one solution. Another solution would be to use OpenAI rest API but it is not free. Recently, I have started using Groq REST API which is free. They provide latest Open source models like Llama-3.1, though there is request limit (30 requests per minute). Also, there models are full and not quantized one like Ollama hence more accurate.
@@CoderzColumn aahh i see, so we can build and host our model in server that have high resource, then we use it as rest API for streamlit web app that we share, thx for explain.
Yea. You can use it other platforms than Streamlit because just like openai API, your models are available through API. You can even create models for custom uses. But i feel the cloud instance are bit pricey. You'll need good instance with 16/32/64 GB RAM and one with high GPU RAM. The cost increases fastly with specs. You know if you have some spare system with GPU lying around then you can use it as well.You'll have to keep it on all the time and initially, you'll have to do few firewall settings to make it accessible to outside world on internet. This one can be cheaper hopefully but you'll still have to pay for electricity for keeping this system on (which has GPU hence more electricity usage).
@@CoderzColumn yep you right, even if we make own server to host Rest API, we can use it for any use case. thx for response
Hi CoderzColumn, you are so generous to share and explain coding clearly. Hope you are okay and keep pursuiting your career successfully in nearby future. <3. Thanks for your free tutorials
Thanks for your feedback. Really Appreciate it.
SO GLAD YOU POSTED THIS THANK YOU SM!!!
Thanks for your feedback. Really Appreciate it.
Hi sir. I can see that you can combine matplotlib with widgets . Can widget combine with plotly? Thanks for this videos.
Yes, definitely you can use it with Plotly. It'll work fine. Also, I think that with plotly, if you use FigureWidget by wrapping figure object inside it then it'll be better. Check out this article. plotly.com/python/getting-started/#jupyter-notebook-support
@@CoderzColumn You are nice! Thanks you for your help. greetings from Perú
No probs. Hope my answer help you solve the problem.
Great video! Do you know if there's a way to upload the resulting code from a Streamlit app using Ollama to streamlit.share and make it work?
Wonderful content, I had learnt a lot.
Glad you enjoyed it!
Superb video. How to deploy it in production?
Nice video... Just what I'm looking for. This Dash-plotly I used when I need a real time update, at maximum 5 to 5 min refresh. Do you know how this will cost monthly? just to have some idea
If you have set max_instance in config file to 1 then it won't cost you anything. But if you increase count of instances then it'll charge based on usage of instance. I have included info about instance charge pages in video. Please feel free to check them to know more about charges. But with just one instance running, it comes under free tier hence no charges.
Thanks teacher. 😍😍😍
Thanks for comment.
Nice video, thanks for this
Thanks for the feedback. Appreciate it.
Is there a way for the answer to show a timestamp that corresponds to a summarized point?
I think that when you load transcripts, you can load it in chunks and then create vector store using it. That way you can retrieve timestamps from retrieved relevant documents. Start time of chunk is available as metadata. It'll require bit of coding changes. It won't be accurate but near to answer.
Excellent video, very well explained and easy to follow. I take a look at to your content and is really good and useful for me. Thanks a lot Mr Solanki
Thanks for taking time to comment !! Really appreciate it !!!
Very Nice video
Thanks for taking time to comment !! Really appreciate it !!!
Nice video
Thanks for taking time to comment !! Really appreciate it !!!
hi how yu are running application without main function define?
Great video karthik
Thank you 🙂
Nice tutorial. May I know what configuration of computer required to run in local machine . With 8gb Ram above tutorial possible.
Thanks for feedback. Appreciate it. In this tutorial, I have used Groq API for accessing LlaMa-3 hence you should be able to run it even with 2 GB RAM. Because it'll hit Groq API for LLM. You'll need to create api-key on groq though. But if you want to use Local LLMs like the one available from Ollama then you'll need around 8 GB of RAM. It'll be better with more than 8GB but 7-8 B parameters model works with 8 GB as well. Good to have i3 or or above processor. But want to inform you that results were not that good with Llama-3 accessed through Ollama. I have tried it. Maybe because it's quantized version. I would suggest you using Groq API.
@@CoderzColumn Thank you .RAG vs fine tuning can you explain the difference please in which situations we use them.
why not use LangGraph?
We can use LangGraph as well. In future, I'll try langgraph as well.
Thanks for share! 🇧🇷
Thanks for watching!
I LOVE YOUR WORK
Thanks for your feedback. Appreciate it.
'Exception occurred: Error code: 400 - {'error': {'message': "'messages.1' : for 'role:user' the following must be satisfied[('messages.1.content' : one of the following must be satisfied[('messages.1.content' : value must be a string) OR ('messages.1.content.0' : one of the following must be satisfied[('messages.1.content.0.type' : value is not one of the allowed values ['text']) OR ('messages.1.content.0.type' : value is not one of the allowed values ['image_url'])])])]", 'type': 'invalid_request_error'}}' i am not able to fix this error
Can you share which line of code is failing? Is it something failing from Notebook of tutorial or you are trying something new which is failing? I am not able to understand error from this trace only.
perect
Thanks for the feedback.
Very well explained... Thanks 😊
Thanks for your comment. Really appreciate it.
i think there is too many bells
Thanks for feedback. I really needed to know this. I'll try to include less bells in intro. I feel i included many this time when things pop up on screen.
But groq AI isn't Llama. It's no longer open source and host locally/ on premise
Groq provides access to 4 open source LLM (Llama-3 8B & 70B, Gemma 7B & Mistral 8x7B)for free. Groq specializes in hardware creation that speed up token generation for LLMs. These LLMs that they are providing through their API are not theirs but they are open source LLMs which they have deployed on their high performing hardware and provided everyone limited access through API (30 requests per minute). You can login to console.groq.com/settings/limits and check limit for free account. I just now noticed that they added two new models (gemma2 8B and Whisper Large v3). Yes, It's not locally hosted.
@@CoderzColumn Yes, free isn't opensource. I am trying to achieve something locally as I don't want to share my functions with any third party.
Very clean code & easy to follow! 👏👏👏
Thanks for feedback. Appreciate it.
Hi Sir, please do more tutorials as you said on this
This tutorial is gold. Dude your channel is seriously underrated
Great tutorial! Just a suggestion, post the complete code in the comments.
Thanks for feedback. I have code present in one my blog. Here's link * coderzcolumn.com/tutorials/data-science/timeline-using-matplotlib I included it in description as well. This is one of my earlier videos where I forgot to include code. Hope this helps.
Nice explanation of this topic, thanks!
Glad it was helpful!
Thank you Sunny. This has been a great intro and for me, it worked perfectly. I really appreciate your time and effort - you are a good teacher and I have learned much here! Bokeh + Django is a great combination. Regards from Sydney - David
Thanks for the comment. Ideally, if you want to include chart in your Django website, I would recommend to use some Javascript library like Amcharts, d3.js, etc. I have found that it works better with django. But it'll require you to learn javascript though charts are pretty looking. Bokeh is good solution if you want to code in Python for charts and want to include them in django website. At the end of the day, it is javascript based as well. Happy to hear that my tutorial helped you.
@@CoderzColumn Thank you for your advice. I have been considering learning D3, but using D3Blocks, it appears only to render a full HTML page. I have done a fair bit of searching and can't find a way for it to just render the SVG + js without parsing the file myself. Are you aware of a way to just embed the D3 chart and not have a full html page returned? Once again - many thanks and cheers - David
I have worked with one client as freelancer sometime back. I created few sites for him using Django + Amcharts. Amcharts is bit simple. You need to pass data in proper JSON format to create charts. Here is one website (Django + Amcharts): www.cyclicalstrength.com/
can this be done for multiple products without date label, only hover and display.
great work man
Thanks for sparing time to comment! Appreciate it.
There is a lot of content around on this topic but this is one of the useful ones.
Thanks for the feedback. Really appreciate it.
amazing thanks
Thank you too!
You are so underrated and I still don't get it
I have never taken my eyes off your content since I set my eyes on it.
thanks so muchhh
Welcome. Hope my content helped you.
Hi Dude, your channel is highly UNDERRATED!
Glad you think so! Really appreciate that you took time to comment !!! I am planning to upload regularly to get more views. Hope my content helps people around globe.
Which notable models work with function calling?
GPT-3.5, GPT-4, LlaMa-3, Phi-3, Mistral 0.3, Gemma, and few others. Most latest releases are now adding support for function calling by including it in fine-tuning step.
how can we do it for pcb defect detection?
Nice video. Have you made the sso based authentication video ?
Thanks for feedback. No, I haven't made video on authentication for chatbots yet. Its on my radar and planning to make some videos on adding authentication to Chatbots. But as of now, there are none.