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## 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.
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