GITHUB: github.com/ronidas39/LLMtutor...
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1. **Streamlit App Setup**: We'll create a simple Streamlit app to accept user input related to products or items they want to order.
2. **Using LangChain and OpenAI**: We'll pass the user input through LangChain and GPT-4 to parse it into a JSON format.
3. **Centralized Repository**: In this tutorial, we'll use a CSV file as a centralized repository to maintain our inventory and prices. In a real-world scenario, a database like MongoDB could be used.
4. **Price Calculation**: We'll fetch product prices from the CSV file and update the JSON with the calculated prices based on the quantity or volume specified by the user.
5. **PDF Invoice Generation**: We'll use Python's ReportLab module to create the PDF invoice from the JSON data and display it in the Streamlit app.
**Demonstration of the Application**:
- The user inputs text like "I want 10 kg potato, 5 kg mango, and 6 packets of oatmeal."
- The app parses the input into a JSON format.
- The app fetches the prices from the centralized repository.
- The app generates and displays a PDF invoice with all the order details.
**Steps to Follow**:
- Set up the Streamlit app to accept user input.
- Use LangChain and GPT-4 to convert the input text into JSON.
- Fetch product prices from the CSV file and update the JSON.
- Generate the PDF invoice using ReportLab.
- Display the PDF invoice in the Streamlit app.
**Required Modules**:
- LangChain
- OpenAI (GPT-4)
- Streamlit
- Pandas
- ReportLab
**Installation**:
You will need to install the following Python packages:
- Streamlit
- Pandas
- LangChain
- OpenAI
- ReportLab
By the end of this tutorial, you'll have a working application that accepts user input in natural language, processes it using AI, and generates a detailed PDF invoice. Stay tuned for the next sections where we'll dive deeper into each step and walk through the coding part in detail. Don't forget to like, share, and subscribe to Total Technology Zone for more exciting tutorials!
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