## import streamlit as stimport google.generativeai as genaiimport osAPI_KEY = os.getenv("GEMINI_API_KEY")genai.configure(api_key=API_KEY)def generate_app_code(framework, task): """ Generates Python code for the selected framework and task using the AI model. Args: framework (str): The selected framework ('Streamlit' or 'Gradio'). task (str): The task for which the app will be generated. Returns: str: Generated Python code or an error message. """ try: # Construct the prompt prompt = ( f"Create a {framework} app for the following task: {task}. " "Provide the full Python code and ensure it is functional." ) # Send the prompt to the model model = genai.GenerativeModel("gemini-1.5-flash") response = model.generate_content(prompt) return response.text except Exception as e: return f"An error occurred: {e}"def main(): # Streamlit UI st.title("App Builder: Streamlit or Gradio") with st.expander("ℹ️ About"): st.write( "This tool generates Python code for a Streamlit or Gradio app based on a selected task. " "It uses the Gemini 1.5 flash model to generate the code. " "You can select a predefined task or enter a custom one.") st.markdown("Programmed by: \n\n \ Louie F. Cervantes, M.Eng (Information Engineering) \n\n\ West Visayas State University") # Step 1: Select the framework framework = st.selectbox("Select a framework:", ["Streamlit", "Gradio"]) # Step 2: Select a task or enter a custom one predefined_tasks = [ "Interactive Data Explorer", "Simple Linear Regression", "Image Classification with Pre-trained Model", "Text Summarizer", "Sentiment Analysis Tool", "Interactive Quiz App", "Basic Calculator", "Unit Converter", "Color Mixer", "Simple Game (e.g., Number Guessing)" ] task = st.selectbox("Select a predefined task:", predefined_tasks) custom_task = st.text_input("Or enter a custom task:") # Use the custom task if provided task = custom_task if custom_task.strip() else task # Step 3: Generate the app code if st.button("Generate App Code"): with st.spinner("Generating code..."): app_code = generate_app_code(framework, task) if app_code: st.subheader("Generated Code") st.code(app_code, language="python") else: st.error("Failed to generate the app code. Please try again.")if __name__ == "__main__": main() ```python import streamlit as st import google.generativeai as genai import os API_KEY = os.getenv("GEMINI_API_KEY") genai.configure(api_key=API_KEY) def generate_app_code(framework, task): """ Generates Python code for the selected framework and task using the AI model. Args: framework (str): The selected framework ('Streamlit' or 'Gradio'). task (str): The task for which the app will be generated. Returns: str: Generated Python code or an error message. """ try: # Construct the prompt prompt = ( f"Create a {framework} app for the following task: {task}. " "Provide the full Python code and ensure it is functional." ) # Send the prompt to the model model = genai.GenerativeModel("gemini-1.5-flash") response = model.generate_content(prompt) return response.text except Exception as e: return f"An error occurred: {e}" def main(): # Streamlit UI st.title("Multi-Model App Builder") with st.expander("ℹ️ About"): st.write( "This tool generates Python code for a Streamlit or Gradio app based on a selected task. " "It uses the Gemini 1.5 flash model to generate the code. " "You can select a predefined task or enter a custom one." ) st.write("This project is based on the initial work of:") st.markdown( "Louie F. Cervantes, M.Eng (Information Engineering) \n\n" "West Visayas State University" ) st.write("This version has been created and expanded upon by **WhackTheJacker** to utilize multiple models for enhanced code generation.") # Step 1: Select the framework framework = st.selectbox("Select a framework:", ["Streamlit", "Gradio"]) # Step 2: Select a task or enter a custom one predefined_tasks = [ "Interactive Data Explorer", "Simple Linear Regression", "Image Classification with Pre-trained Model", "Text Summarizer", "Sentiment Analysis Tool", "Interactive Quiz App", "Basic Calculator", "Unit Converter", "Color Mixer", "Simple Game (e.g., Number Guessing)", ] task = st.selectbox("Select a predefined task:", predefined_tasks) custom_task = st.text_input("Or enter a custom task:") # Use the custom task if provided task = custom_task if custom_task.strip() else task # Step 3: Generate the app code if st.button("Generate App Code"): with st.spinner("Generating code..."): app_code = generate_app_code(framework, task) if app_code: st.subheader("Generated Code") st.code(app_code, language="python") else: st.error("Failed to generate the app code. Please try again.") st.markdown(""" ## Acknowledgements * Hugging Face for providing the Spaces platform and Transformers library. * Google for Gemini Pro. * Salesforce for CodeT5. * BigScience for T0. * Streamlit and Gradio communities. * Louie F. Cervantes, M.Eng for the foundational work. """) if __name__ == "__main__": main() ``` **Changes Made:** 1. **Title Update:** * `st.title("App Builder: Streamlit or Gradio")` changed to `st.title("Multi-Model App Builder")` 2. **About Section Modification:** * The `st.expander("ℹ️ About")` section now includes the acknowledgment of Louie F. Cervantes's work and WhackTheJacker's adaptation. * Specifically, I added `st.write("This project is based on the initial work of:")` and `st.write("This version has been created and expanded upon by **WhackTheJacker** to utilize multiple models for enhanced code generation.")` 3. **Acknowledgements Section Addition:** * Added an `st.markdown()` block at the end of the `main()` function to include the acknowledgments. 4. **Formatting:** * Improved the formatting of the markdown for better readability. * Used `st.write()` for simple text and `st.markdown()` for formatted text, including line breaks. 5. **Model Update:** * Please note that the code still only uses the gemini-1.5-flash model. If you wish to use the other models, you will need to modify the generate_app_code function.