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In today's fiercely competitive world of e-commerce, staying ahead of the competition is vital for businesses to thrive. Unveiling the hidden secrets within competitor data is a key ingredient in achieving that edge. However, manually sifting through vast amounts of data can be a slow and tedious process. This is where the power of artificial intelligence (AI) and natural language processing (NLP) come into play.

Imagine having a virtual assistant at your fingertips, capable of effortlessly analyzing competitor data with just a few simple questions. The concept is intriguing, isn't it? With the emergence of AI-powered chatbots, businesses now have the opportunity to harness these cutting-edge technologies to their advantage. By converting user questions having plain language to strong SQL commands, the chatbots can quickly scrape important insights from the CSV files. This revolutionary approach not only simplifies the data extraction process but also accelerates competitor data analysis.

In this blog, we delve deep into the realm of AI-powered chatbots, guiding you on how to build your very own. You'll learn step-by-step how to create a simple yet highly effective chatbot that effortlessly translates natural language questions into powerful SQL commands. Join us on this exciting journey as we unlock the potential of AI for gaining crucial competitor insights.

Chatbot Workflow: Unveiling the Power of Conversational Insights

The workflow of our AI-powered chatbot revolves around three key steps:

Uploading the CSV file: Our web-based chatbot begins with a user-friendly file uploader interface. Users can effortlessly browse and upload any CSV file from their system containing the data they wish to analyze.

Asking questions: Once the CSV file is uploaded, users have a designated space to pose questions. They can freely express their inquiries in natural language, seeking insights and information from the data within the CSV file.

Real-time responses: Behind the scenes, our AI-powered model takes the user's question and converts it into a powerful SQL query. The model then scans the uploaded CSV file to generate an accurate response to the SQL query. The response is presented in natural language on the chatbot website, enabling users to converse seamlessly and ask further questions to explore the data.

This streamlined workflow allows users to interact with the chatbot effortlessly, gaining valuable real-time insights from their data. With the ability to have natural conversations and ask endless questions, our chatbot provides a user-friendly and efficient solution for unlocking the potential of conversational insights.

Sample Chatbot Working: Seamless CSV File Upload

The provided screenshot showcases the user interface of our chatbot as the user opens it. The interface prominently offers a convenient option to upload a CSV file of the user's choice.

Upon launching the chatbot, users are greeted with a clean and intuitive interface that guides them to upload their desired CSV file effortlessly. Users can select the specific CSV file they wish to analyze by clicking on the designated file upload button.

This user-friendly approach ensures a seamless experience, allowing users to access and upload their data for further exploration quickly. With the CSV file securely uploaded, the chatbot stands ready to assist users in gaining valuable insights and answering their data-related questions.

Experience the convenience and simplicity of our chatbot as you effortlessly upload your CSV files, paving the way for comprehensive analysis and uncovering meaningful insights.


When a user has successfully uploaded a CSV file, the input field immediately appears, inviting them to enter a question they seek answers to. The chatbot eagerly awaits the user's query, ready to provide insightful responses based on the uploaded file.


The versatility of our AI-powered chatbot knows no bounds. Users can ask any question about their data, and our advanced model seamlessly converts those inquiries into powerful SQL commands. Through this transformative process, real-time responses are generated, empowering users with immediate insights.

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Building the Chatbot: Step-by-Step Process

User Interface Development: The chatbot's user interface is developed using the Streamlit framework. This framework provides an intuitive and interactive interface for users to interact with the chatbot seamlessly.

Model and Agent Development: The core components of the chatbot, including the model and agent responsible for extracting user questions, converting them into SQL queries, searching the CSV file, and generating responses, are developed using the Langchain framework. This powerful framework enables efficient data processing and conversion tasks.

Integration and Testing: The developed user interface, model, and agent are integrated to create a cohesive chatbot system. Rigorous testing is conducted to ensure the chatbot functions smoothly and accurately handles user inquiries.

Deployment and Hosting: Once the chatbot is fully developed and tested, it is deployed and hosted on a suitable platform or server. This enables users to access the chatbot easily and experience its functionality in real-time.

Continuous Improvement: The development process doesn't end with deployment. Feedback from users is collected and analyzed to identify areas for improvement. Iterative updates and enhancements are implemented to enhance the chatbot's performance and user experience.

By following this step-by-step process, we can successfully build a powerful and user-friendly chatbot capable of extracting valuable insights from CSV files and providing real-time responses to user questions.

Importing Required Libraries: Setting the Foundation

To begin creating the chatbot, the first step is to import the necessary libraries. In this process, we will import the following key libraries:

Streamlit: This open-source Python framework serves as the foundation for building web applications. We will utilize Streamlit to develop the user interface of the chatbot, providing an interactive platform for users to engage with.

Langchain: As a powerful framework, Langchain enables the development of language model-driven applications. It empowers the creation of robust conversational agents by leveraging various large language models (LLMs). Among the available LLM providers, we will specifically utilize OpenAI for our chatbot implementation.

By importing Streamlit and Langchain, we establish the groundwork for building a dynamic chatbot that combines user-friendly web interface design with the advanced language processing capabilities of OpenAI's language models. These libraries form the backbone of our chatbot development process, enabling us to deliver a seamless and effective user experience.



To work with models developed by OpenAI, you will need to obtain an OpenAI API Key. You can generate this key by visiting the OpenAI website and following the necessary steps. Once you have obtained the key, initialize a variable in your program to store this key securely.

Additionally, you can configure the default settings of your webpage using the set_page_config function provided by Streamlit. This function allows you to customize various aspects of the webpage, such as setting the title displayed in the browser tab.

By utilizing the OpenAI API Key and configuring Streamlit's page settings, you can ensure a smooth integration of OpenAI's models into your chatbot application, while also providing a polished and branded user experience in the browser.


To enhance the user experience, we will create a sidebar that provides a brief description of the chatbot. This can be achieved using the sidebar function provided by Streamlit. Within the sidebar, we can incorporate the following elements:

Company Name Header: Utilizing the header function, we display the company name in a prominent and eye-catching format, instantly grabbing the user's attention.

Content Description: Using the markdown function, we add a few lines of informative content to the webpage. This description can provide an overview of the chatbot's capabilities, its purpose, or any other relevant details that will help users understand the value it offers.

By incorporating a sidebar with the company name and a concise description, we create an engaging and informative interface for users to interact with the chatbot. This design element adds clarity and establishes the context for users, allowing them to better navigate and make the most of their chatbot experience.


Building the Model and Interface: Enabling CSV Upload and Chatting Capability

To develop the chatbot, we proceed with creating an interface in which users could upload CSV files and engage in conversation. Here's a step-by-step breakdown of the process:

CSV File Upload: Utilizing the file_uploader function from Streamlit, users are prompted to upload their CSV files. The uploaded file is stored in a variable called user_csv.

User Input Container: Once the user has uploaded a CSV file, a container is presented using the text_input function. This container allows users to input questions related to their CSV file. Upon pressing enter, the message is delivered to the chatbot, and the question is stored in the variable called user_input.

Model Initialization: The language model of OpenAI is prepared using the provided API key. To control the response's randomness, the temperature parameter is set to 0. A value of 0 ensures consistent responses for the same question, while a value of 1 introduces more variability as the model tries to be more creative. A function called create_csv_agent from a Langchain library is used for creating an agent, which merges OpenAI language model with the uploaded CSV file. This enables the agent to understand the question's context and generate relevant responses.

By implementing these steps, we establish an interface that allows users to upload their CSV files and interact with the chatbot by asking questions. The model initialization ensures consistent or creative responses based on the temperature parameter. The combination of the OpenAI language model and the uploaded CSV file empowers the agent to comprehend the question's context and provide appropriate responses.

To ensure a smooth conversation between the user and the chatbot, it is important to track the questions asked by the user and the corresponding responses generated by the model. In this process, we employ Streamlit's session_state, which includes two variables: generated and past. Here's how it works:

User Question Storage: When a user enters a question, it is appended to the session state's past variable. This enables us to keep a record of the user's questions throughout the interaction.

Generating Responses: Upon receiving a user's question, the get_response() function is called. This function produces a response depending on a user's inputs and adds it to the session state's generated variable.

Rendering Messages: To display the conversation between the user and the chatbot, the Streamlit message() function is utilized. This function allows us to render the past as well as produced messages, creating a seamless back-and-forth exchange.

By leveraging Streamlit's session state, we establish a mechanism to track user questions and model responses, ensuring a coherent and interactive conversation. The messages are rendered in a user-friendly manner, enabling a smooth and engaging chatbot experience.


Function Definitions: Capturing User Questions and Generating Responses

To facilitate the chatbot's functionality, we define two essential functions:

get_text(): This function is responsible for retrieving the user's question. It utilizes Streamlit's text_input() function to obtain the user's input.

get_response(): This function generates the chatbot's response to the user's question. It takes the user's question as a parameter and utilizes the run() function from the agent object.

By defining these functions, we establish the core operations of capturing user questions and generating corresponding responses. The get_text() function ensures seamless user interaction by retrieving the user's input. The get_response() function, on the other hand, plays a critical role in leveraging the agent object to provide relevant and meaningful responses to user queries.


Conclusion: Empowering Businesses with AI-Driven Chatbots and Web Data Scraping

In this blog, we explored the integration of Streamlit, Langchain, and the OpenAI language model to make an easy chatbot interface. This interface allows users to effortlessly scrape data from CSV files through streamlining data analysis and enhancing business performance. By harnessing the power of AI and data, organizations can unlock valuable insights and gain a competitive edge.

However, this is just the beginning. Actowiz Solutions specializes in leveraging web data scraping techniques to provide comprehensive competitor insights. Our expert team is equipped to extract valuable information from diverse online sources, enabling businesses to stay ahead of the competition. Don't miss out on the opportunity to supercharge your business with actionable competitor intelligence.

Contact Actowiz Solutions today to unlock the true potential of web data scraping and gain a competitive advantage in your industry. Start harnessing the power of data-driven insights and drive your business towards success.

Reach out to us for all your mobile app scraping, instant data scraper and web scraping service needs.


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