File size: 1,971 Bytes
d511090 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | # app.py
import streamlit as st
import google.generativeai as genai
# Configure the Gemini LLM
genai.configure(api_key="AIzaSyAKOjtXWhQKL_wDbFkSYPbfmtQYj2vUMCs")
generation_config = {
"temperature": 0.9,
"top_p": 1,
"top_k": 1,
"max_output_tokens": 2048,
}
safety_settings = [
{
"category": "HARM_CATEGORY_HARASSMENT",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_HATE_SPEECH",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
]
model = genai.GenerativeModel(model_name="gemini-1.0-pro",
generation_config=generation_config,
safety_settings=safety_settings)
def analyze_task_completion(task_description, code):
prompt_parts = [
f"Task Description: {task_description}",
f"Developed Code:\n{code}",
"Analyze the developed code and provide the following information:",
"1. Number of tasks assigned",
"2. Number of tasks completed based on the developed code",
"3. Tasks that are still incomplete or missing based on the task description"
]
response = model.generate_content(prompt_parts)
return response.text
def main():
st.title("Task Completeness Checker")
task_description = st.text_area("Enter the task description provided by the product manager:")
code = st.text_area("Enter the code you developed:")
if st.button("Analyze"):
if task_description and code:
analysis = analyze_task_completion(task_description, code)
st.success(analysis)
else:
st.warning("Please provide both the task description and the developed code.")
if __name__ == "__main__":
main() |