import streamlit as st from dotenv import load_dotenv import streamlit.components.v1 as components from analyzer import CodeAnalyzer from typing import Any, Iterable, List import os import html # Force reload environment variables load_dotenv(override=True) st.set_page_config( page_title="Matrix Code Analyzer", page_icon="🧠", layout="wide" ) # Debug sidebar removed for cleaner UI CUSTOM_CSS = """ """ st.markdown(CUSTOM_CSS, unsafe_allow_html=True) st.markdown( """
""", unsafe_allow_html=True, ) @st.cache_resource def get_analyzer(): # Use /tmp for cache in Docker/HF Spaces (always writable) # Use .analyzer_cache for local development cache_dir = "/tmp/.analyzer_cache" if os.path.exists("/app") else ".analyzer_cache" return CodeAnalyzer(cache_dir=cache_dir) analyzer = get_analyzer() AVAILABLE_MODELS = analyzer.available_models if not AVAILABLE_MODELS: st.error("No AI models configured. Add API keys to your .env file and restart the app.") st.stop() LANGUAGE_DISPLAY = { "auto": "Auto Detect", "python": "Python", "javascript": "JavaScript", "typescript": "TypeScript", "html": "HTML", "css": "CSS", "java": "Java", "cpp": "C++", "c": "C", "csharp": "C#", "go": "Go", "rust": "Rust", "php": "PHP", "ruby": "Ruby", "swift": "Swift", "kotlin": "Kotlin", } LANGUAGE_OPTIONS = list(LANGUAGE_DISPLAY.keys()) def ensure_list(items: Any) -> List[str]: if not items: return [] if isinstance(items, str): clean = items.strip() return [clean] if clean else [] if isinstance(items, dict): return [f"{key}: {value}" for key, value in items.items() if str(value).strip()] if isinstance(items, Iterable): values = [] for entry in items: if entry is None: continue text = str(entry).strip() if text: values.append(text) return values return [str(items)] def parse_score(raw: Any) -> float: try: return float(raw) except (TypeError, ValueError): return 0.0 def score_badge(score: float) -> tuple[str, str]: if score >= 80: return "Excellent", "#00fba4" if score >= 60: return "Review Suggested", "#ffd76a" return "Needs Attention", "#ff6b6b" def render_list_section(title: str, icon: str, content: Any, fallback: str | None = None) -> None: entries = ensure_list(content) if entries: items_html = "".join(f"
  • {html.escape(entry)}
    • " for entry in entries[:6]) st.markdown( f"""

    {icon} {title}

    """, unsafe_allow_html=True, ) elif fallback: st.markdown( f"""

    {icon} {title}

    {html.escape(fallback)}

    """, unsafe_allow_html=True, ) def render_code_result(result: dict[str, Any], model_label: str) -> None: if result.get("error"): st.error(f"Analysis failed: {result['error']}") return score = parse_score(result.get("quality_score", 0)) status_label, status_color = score_badge(score) language = (result.get("language") or "auto").upper() line_count = result.get("line_count", "-") exec_time = parse_score(result.get("execution_time", 0.0)) cached_text = "Hit" if result.get("cached") else "Fresh" stats = [ {"label": "Quality Score", "value": f"{int(round(score))}/100", "sub": status_label, "color": status_color}, {"label": "Language", "value": language, "sub": "Detected" if language != "AUTO" else "Auto"}, {"label": "Lines", "value": line_count, "sub": "Analyzed"}, {"label": "Latency", "value": f"{exec_time:.1f}s", "sub": "Runtime"}, {"label": "Cache", "value": cached_text, "sub": "Result Store"}, ] # Render stats using native Streamlit components to avoid raw HTML showing st.markdown( f"""
    🤖 {model_label}
    """, unsafe_allow_html=True, ) cols = st.columns(len(stats)) for idx, stat in enumerate(stats): with cols[idx]: st.metric(label=stat["label"], value=stat["value"], delta=stat["sub"]) summary = result.get("summary") if summary: st.markdown( f"""

    📋 Overview

    {html.escape(summary)}

    """, unsafe_allow_html=True, ) render_list_section("Highlights", "✨", result.get("strengths")) render_list_section("Bug Detection", "🐞", result.get("bugs") or result.get("issues"), "No critical bugs were flagged.") render_list_section("Security", "🔒", result.get("security_vulnerabilities") or result.get("security_concerns"), "No security vulnerabilities detected.") render_list_section("Code Quality", "🧩", result.get("quality_issues"), "Structure looks solid and maintainable.") render_list_section("Quick Fixes", "⚡", result.get("quick_fixes"), "No urgent fixes suggested.") render_list_section("Suggestions", "💡", result.get("suggestions")) raw = result.get("raw_response") if raw: with st.expander("View full model response", expanded=False): st.code(raw, language="text") def render_repo_result(result: dict[str, Any], model_label: str) -> None: if result.get("error"): st.error(f"Repository analysis failed: {result['error']}") return info = result.get("repository_info", {}) repo_name = info.get("name", "Repository") repo_desc = info.get("description") or "No description provided." repo_url = result.get("repo_url") or st.session_state.get("repo_analysis_url") repo_stats = [ {"label": "Primary Language", "value": info.get("language", "Unknown"), "sub": "Detected"}, {"label": "Stars", "value": info.get("stars", 0), "sub": "Community"}, {"label": "Forks", "value": info.get("forks", 0), "sub": "Collaboration"}, {"label": "Size", "value": f"{info.get('size', 0)} KB", "sub": "Repo Size"}, {"label": "Latency", "value": f"{parse_score(result.get('execution_time', 0.0)):.1f}s", "sub": "Runtime"}, ] link_html = f'

    View repository ↗

    ' if repo_url else "" # Header card st.markdown( f"""
    🤖 {html.escape(model_label)}

    {html.escape(repo_name)}

    {html.escape(repo_desc)}

    {link_html}
    """, unsafe_allow_html=True, ) # Metrics row cols = st.columns(len(repo_stats)) for idx, stat in enumerate(repo_stats): with cols[idx]: st.metric(label=stat["label"], value=stat["value"], delta=stat["sub"]) overview = result.get("project_overview") if overview: st.markdown( f"""

    📋 Project Overview

    {html.escape(overview)}

    """, unsafe_allow_html=True, ) render_list_section("Architecture Quality", "🏗️", result.get("architecture_quality"), "Project structure looks well organized.") render_list_section("Critical Issues", "🚨", result.get("critical_issues"), "No critical issues were highlighted.") render_list_section("Priority Improvements", "🎯", result.get("improvement_priorities"), "No immediate improvements suggested.") render_list_section("Onboarding Guide", "🚀", result.get("onboarding_guide"), "No specific onboarding steps identified.") render_list_section("Tech Stack", "🛠️", result.get("tech_stack_rationale"), "Tech stack details were not identified.") render_list_section("API Endpoints", "🔌", result.get("api_endpoint_summary"), "No API endpoints were identified.") raw = result.get("raw_response") if raw: with st.expander("View full model response", expanded=False): st.code(raw, language="text") if "code_input" not in st.session_state: st.session_state.code_input = "" if "code_file_meta" not in st.session_state: st.session_state.code_file_meta = None if "code_analysis_result" not in st.session_state: st.session_state.code_analysis_result = None if "code_analysis_model" not in st.session_state: st.session_state.code_analysis_model = "" if "repo_analysis_result" not in st.session_state: st.session_state.repo_analysis_result = None if "repo_analysis_model" not in st.session_state: st.session_state.repo_analysis_model = "" if "repo_analysis_url" not in st.session_state: st.session_state.repo_analysis_url = "" st.markdown( """
    CODE ANALYZER

    AI Code Analyzer

    Inspect bugs, surface security gaps, and review repositories with instant feedback.

    🧠 Multi-model 🔍 Bug & Security Scan ⚡ Instant Results 📦 GitHub Ready
    """, unsafe_allow_html=True, ) code_tab, repo_tab = st.tabs(["Code Analysis", "Repository Insights"]) with code_tab: model_keys = list(AVAILABLE_MODELS.keys()) selected_model_code = st.selectbox( "AI Model", options=model_keys, format_func=lambda key: AVAILABLE_MODELS[key], key="code_model_select", ) selected_language = st.selectbox( "Language", LANGUAGE_OPTIONS, format_func=lambda code: LANGUAGE_DISPLAY[code], key="language_select", ) uploaded_file = st.file_uploader( "Upload a code file", type=["py", "js", "java", "cpp", "c", "cs", "go", "rs", "php", "rb", "swift", "kt", "txt"], key="code_file_uploader", ) if uploaded_file is not None: raw_bytes = uploaded_file.read() try: decoded = raw_bytes.decode("utf-8") except UnicodeDecodeError: st.error("Only UTF-8 encoded files are supported.") else: st.session_state.code_input = decoded st.session_state.code_file_meta = { "name": uploaded_file.name, "size": len(raw_bytes), "lines": len(decoded.splitlines()), } st.text_area( "Or paste code below", key="code_input", height=320, placeholder="Paste any code snippet to inspect bugs, security gaps, and quality issues.", ) meta = st.session_state.get("code_file_meta") if meta: st.markdown( f"""

    Active File

    {meta['name']} {meta['lines']} lines {meta['size']} bytes
    """, unsafe_allow_html=True, ) analyze_code_clicked = st.button("Run Code Analysis", key="code_analyze_button", use_container_width=True) if analyze_code_clicked: snippet = st.session_state.get("code_input", "").strip() if not snippet: st.error("Please upload a file or paste some code to analyze.") else: with st.spinner("Analyzing code..."): language_arg = None if selected_language == "auto" else selected_language result = analyzer.analyze_code(snippet, selected_model_code, language_arg) st.session_state.code_analysis_result = result st.session_state.code_analysis_model = AVAILABLE_MODELS[selected_model_code] if st.session_state.get("code_analysis_result"): render_code_result(st.session_state.code_analysis_result, st.session_state.get("code_analysis_model", "")) else: st.info("Upload a file or paste code to generate an analysis.") with repo_tab: model_keys = list(AVAILABLE_MODELS.keys()) selected_model_repo = st.selectbox( "AI Model", options=model_keys, format_func=lambda key: AVAILABLE_MODELS[key], key="repo_model_select", ) st.text_input( "GitHub repository URL", placeholder="https://github.com/owner/repo", key="repo_analysis_url", ) analyze_repo_clicked = st.button("Analyze Repository", key="repo_analyze_button", use_container_width=True) if analyze_repo_clicked: repo_url = st.session_state.get("repo_analysis_url", "").strip() if not repo_url: st.error("Enter a GitHub repository URL.") else: with st.spinner("Inspecting repository..."): result = analyzer.analyze_github_repo(repo_url, selected_model_repo) st.session_state.repo_analysis_result = result st.session_state.repo_analysis_model = AVAILABLE_MODELS[selected_model_repo] if st.session_state.get("repo_analysis_result"): render_repo_result( st.session_state.repo_analysis_result, st.session_state.get("repo_analysis_model", ""), ) else: st.info("Provide a public GitHub repository URL to review its structure, issues, and improvements.")