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| import os | |
| import pandas as pd | |
| import streamlit as st | |
| from datetime import datetime | |
| from groq import Groq | |
| from logic import LLMClient, CodeProcessor | |
| from batch_code_logic_csv import csv_read_batch_code | |
| import zipfile | |
| import io | |
| import markdown2 | |
| import pdfkit | |
| client = Groq(api_key=os.getenv("GROQ_API_KEY")) | |
| llm_obj = LLMClient(client) | |
| processor = CodeProcessor(llm_obj) | |
| st.title("Code Analysis with LLMs") | |
| st.sidebar.title("Input Options") | |
| code_input_method = st.sidebar.radio("How would you like to provide your code?", | |
| ("Upload CSV file", "Upload Code File")) | |
| code_dict = {} | |
| if code_input_method == "Upload CSV file": | |
| uploaded_file = st.sidebar.file_uploader("Upload your CSV/Excel file", type=["csv", "xlsx"]) | |
| if uploaded_file is not None: | |
| dataframe = pd.read_csv(uploaded_file) | |
| code_dict = csv_read_batch_code(dataframe) | |
| elif code_input_method == "Upload Code File": | |
| uploaded_file = st.sidebar.file_uploader("Upload your code file", type=["py", "txt"]) | |
| if uploaded_file is not None: | |
| code_text = uploaded_file.read().decode("utf-8") | |
| code_dict = {"single_code": code_text} | |
| model_choice = st.sidebar.selectbox("Select LLM Model", | |
| ["llama-3.2-90b-text-preview", "llama-3.2-90b-text-preview", "llama3-8b-8192"]) | |
| if code_dict: | |
| unique_key = st.sidebar.selectbox("Select a Key for Analysis", list(code_dict.keys())) | |
| if st.sidebar.button("Analyze Code") and unique_key: | |
| code_text = code_dict[unique_key] | |
| markdown_output = processor.process_code(code_text, model_choice) | |
| with st.expander(f"Analysis for {unique_key}"): | |
| st.markdown(markdown_output) | |
| timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") | |
| st.download_button( | |
| label=f"Download {unique_key} Result as Markdown", | |
| data=markdown_output, | |
| file_name=f"code_analysis_{unique_key}_{timestamp}.md", | |
| mime="text/markdown" | |
| ) | |
| html_output = markdown2.markdown(markdown_output) | |
| pdf_file_path = f"code_analysis_{unique_key}_{timestamp}.pdf" | |
| pdfkit.from_string(html_output, pdf_file_path) | |
| with open(pdf_file_path, "rb") as pdf_file: | |
| pdf_data = pdf_file.read() | |
| st.download_button( | |
| label=f"Download {unique_key} Result as PDF", | |
| data=pdf_data, | |
| file_name=pdf_file_path, | |
| mime="application/pdf" | |
| ) | |
| if st.sidebar.button("Batch Predict"): | |
| timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") | |
| all_markdowns = {} | |
| for key, code_text in code_dict.items(): | |
| markdown_output = processor.process_code(code_text, model_choice) | |
| all_markdowns[key] = markdown_output | |
| with st.expander(f"Analysis for {key}"): | |
| st.markdown(markdown_output) | |
| st.download_button( | |
| label=f"Download {key} Result as Markdown", | |
| data=markdown_output, | |
| file_name=f"code_analysis_{key}_{timestamp}.md", | |
| mime="text/markdown" | |
| ) | |
| html_output = markdown2.markdown(markdown_output) | |
| pdf_file_path = f"code_analysis_{key}_{timestamp}.pdf" | |
| pdfkit.from_string(html_output, pdf_file_path) | |
| with open(pdf_file_path, "rb") as pdf_file: | |
| pdf_data = pdf_file.read() | |
| st.download_button( | |
| label=f"Download {key} Result as PDF", | |
| data=pdf_data, | |
| file_name=pdf_file_path, | |
| mime="application/pdf" | |
| ) | |
| zip_buffer = io.BytesIO() | |
| with zipfile.ZipFile(zip_buffer, "w") as zip_file: | |
| for key, markdown_output in all_markdowns.items(): | |
| zip_file.writestr(f"code_analysis_{key}_{timestamp}.md", markdown_output) | |
| st.download_button( | |
| label="Download All as Zip", | |
| data=zip_buffer.getvalue(), | |
| file_name=f"code_analysis_batch_{timestamp}.zip", | |
| mime="application/zip" | |
| ) | |
| else: | |
| st.write("Please upload your file to analyze.") | |