typicaleoxx/code-explainer-ai

1# this file defines the Streamlit user interface2# it allows a user to paste code and receive an AI generated analysis3 4 5import streamlit as st6 7from prompt_template import build_prompt8from ai_client import generate_explanation9from parser import parse_analysis10from static_analyzer import analyze_code_structure11 12 13# configure page settings14st.set_page_config(page_title="Code Intelligence AI", layout="wide")15 16 17# main title displayed in the browser18st.title("Code Intelligence AI")19 20 21# short explanation of what the tool does22st.markdown(23    """24Paste a code snippet and receive a structured engineering analysis.25 26The AI will analyze:27 28• Code purpose  29• Logic breakdown  30• Execution walkthrough  31• Code quality issues  32• Performance analysis  33• Security concerns  34• Refactored version35"""36)37 38 39# create a large input box for the user to paste code40code_input = st.text_area("Paste your code here", height=300)41 42 43# analyze button triggers the AI analysis44if st.button("Analyze Code"):45 46    # ensure the user entered code47    if code_input.strip() == "":48        st.warning("Please paste some code before analyzing.")49 50    else:51 52        # show the submitted code with syntax highlighting53        st.subheader("Submitted Code")54        st.code(code_input, language="python")55 56        # run static AST analysis on the submitted code57        structure_analysis = analyze_code_structure(code_input)58 59        # display static analysis results to the user60        st.subheader("Static Code Analysis")61 62        if "error" in structure_analysis:63            st.error(structure_analysis["error"])64            st.stop()65 66        else:67            st.json(structure_analysis)68 69        # build the prompt sent to the AI model including AST signals70        prompt = build_prompt(code_input, structure_analysis)71 72        # show loading spinner while the AI processes the request73        with st.spinner("Analyzing code..."):74 75            # send prompt to the AI model76            result = generate_explanation(prompt)77 78        # parse the AI response into structured sections79        analysis = parse_analysis(result)80 81        # render the parsed sections in the UI82 83        st.subheader("Code Summary")84        st.write(analysis["Code Summary"])85 86        st.subheader("Logic Explanation")87        st.write(analysis["Logic Explanation"])88 89        st.subheader("Execution Walkthrough")90        st.write(analysis["Execution Walkthrough"])91 92        st.subheader("Code Quality Issues")93        st.write(analysis["Code Quality Issues"])94 95        st.subheader("Performance Analysis")96        st.write(analysis["Performance Analysis"])97 98        st.subheader("Security Concerns")99        st.write(analysis["Security Concerns"])100 101        st.subheader("Improved Version")102        st.code(analysis["Improved Version"], language="python")103