app.py

import streamlit as st
import difflib
import re
import hashlib
import requests
import os

# --- Blackbox AI Agent Key (read from environment variable) ---
BLACKBOX_API_KEY = os.environ.get("BLACKBOX_API_KEY")
if not BLACKBOX_API_KEY:
    st.error("❌ Please set your BLACKBOX_API_KEY environment variable.")
    st.stop()
BLACKBOX_API_URL = "https://api.blackbox.ai/v1/chat/completions"

def blackbox_api_call(prompt):
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {BLACKBOX_API_KEY}",
    }
    data = {
        "model": "gpt-4",
        "messages": [
            {"role": "user", "content": prompt}
        ],
        "max_tokens": 2048,
        "temperature": 0.7
    }
    response = requests.post(BLACKBOX_API_URL, headers=headers, json=data)
    if response.status_code == 200:
        result = response.json()
        return result["choices"][0]["message"]["content"]
    else:
        return f"[Blackbox API Error {response.status_code}]: {response.text}"

# --- Cache for embeddings ---
embedding_cache = {}

def get_embedding(text):
    key = hashlib.sha256(text.encode()).hexdigest()
    if key in embedding_cache:
        return embedding_cache[key]
    embedding = [ord(c) % 100 / 100 for c in text[:512]]
    embedding_cache[key] = embedding
    return embedding

def cosine_similarity(vec1, vec2):
    dot = sum(a*b for a,b in zip(vec1, vec2))
    norm1 = sum(a*a for a in vec1) ** 0.5
    norm2 = sum(b*b for b in vec2) ** 0.5
    return dot / (norm1 * norm2 + 1e-8)

def split_code_into_chunks(code, lang):
    if lang.lower() == "python":
        pattern = r'(def\s+\w+\(.*?\):|class\s+\w+\(?.*?\)?:)'
        splits = re.split(pattern, code)
        chunks = []
        for i in range(1, len(splits), 2):
            header = splits[i]
            body = splits[i+1] if (i+1) < len(splits) else ""
            chunks.append(header + body)
        return chunks if chunks else [code]
    else:
        return [code]

def semantic_search_improved(code, question, lang, skill, role, explain_lang):
    chunks = split_code_into_chunks(code, lang)
    question_emb = get_embedding(question)
    scored_chunks = []
    for chunk in chunks:
        emb = get_embedding(chunk)
        score = cosine_similarity(question_emb, emb)
        scored_chunks.append((score, chunk))
    scored_chunks.sort(key=lambda x: x[0], reverse=True)
    top_chunks = [c for _, c in scored_chunks[:3]]
    combined_code = "\n\n".join(top_chunks)
    prompt = (
        f"You are a friendly and insightful {lang} expert helping a {skill} {role}.\n"
        f"Based on these relevant code snippets:\n{combined_code}\n"
        f"Answer this question in {explain_lang}:\n{question}\n"
        f"Explain which parts handle the question and how to modify them if needed."
    )
    answer = blackbox_api_call(prompt)
    add_to_blackbox_history(prompt, answer, "semantic_search")
    return answer

def error_detection_and_fixes(refactored_code, lang, skill, role, explain_lang):
    prompt = (
        f"You are a senior {lang} developer. Analyze this code for bugs, security flaws, "
        f"and performance issues. Suggest fixes with explanations in {explain_lang}:\n\n{refactored_code}"
    )
    answer = blackbox_api_call(prompt)
    add_to_blackbox_history(prompt, answer, "workflow")
    return answer

def agentic_workflow(code, skill_level, programming_language, explanation_language, user_role):
    timeline = []
    suggestions = []

    # Explanation
    explain_prompt = (
        f"You are a friendly and insightful {programming_language} expert helping a {skill_level} {user_role}. "
        f"Explain this code in {explanation_language} with clear examples, analogies, and why each part matters:\n\n{code}"
    )
    explanation = blackbox_api_call(explain_prompt)
    timeline.append({"step": "Explain", "description": "Detailed explanation", "output": explanation, "code": code})
    suggestions.append("Consider refactoring your code to improve readability and performance.")
    add_to_blackbox_history(explain_prompt, explanation, "workflow")

    # Refactor
    refactor_prompt = (
        f"Refactor this {programming_language} code. Explain the changes like a mentor helping a {skill_level} {user_role}. "
        f"Include best practices and improvements:\n\n{code}"
    )
    refactor_response = blackbox_api_call(refactor_prompt)
    add_to_blackbox_history(refactor_prompt, refactor_response, "workflow")
    if "```" in refactor_response:
        parts = refactor_response.split("```")
        refactored_code = ""
        for part in parts:
            if part.strip().startswith(programming_language.lower()):
                refactored_code = part.strip().split('\n', 1)[1] if '\n' in part else ""
                break
        if not refactored_code:
            refactored_code = refactor_response
    else:
        refactored_code = refactor_response
    timeline.append({"step": "Refactor", "description": "Refactored code with improvements", "output": refactored_code, "code": refactored_code})
    suggestions.append("Review the refactored code and adapt it to your style or project needs.")

    # Review
    review_prompt = (
        f"As a senior {programming_language} developer, review the refactored code. "
        f"Give constructive feedback on strengths, weaknesses, performance, security, and improvements in {explanation_language}:\n\n{refactored_code}"
    )
    review = blackbox_api_call(review_prompt)
    add_to_blackbox_history(review_prompt, review, "workflow")
    timeline.append({"step": "Review", "description": "Code review and suggestions", "output": review, "code": refactored_code})
    suggestions.append("Incorporate review feedback for cleaner, robust code.")

    # Error detection & fixes
    errors = error_detection_and_fixes(refactored_code, programming_language, skill_level, user_role, explanation_language)
    timeline.append({"step": "Error Detection", "description": "Bugs, security, performance suggestions", "output": errors, "code": refactored_code})
    suggestions.append("Apply fixes to improve code safety and performance.")

    # Test generation
    test_prompt = (
        f"Write clear, effective unit tests for this {programming_language} code. "
        f"Explain what each test does in {explanation_language}, for a {skill_level} {user_role}:\n\n{refactored_code}"
    )
    tests = blackbox_api_call(test_prompt)
    add_to_blackbox_history(test_prompt, tests, "workflow")
    timeline.append({"step": "Test Generation", "description": "Generated unit tests", "output": tests, "code": tests})
    suggestions.append("Run generated tests locally to validate changes.")

    return timeline, suggestions

def get_inline_diff_html(original, modified):
    differ = difflib.HtmlDiff(tabsize=4, wrapcolumn=80)
    html = differ.make_table(
        original.splitlines(), modified.splitlines(),
        "Original", "Refactored", context=True, numlines=2
    )
    return f'<div style="overflow-x:auto; max-height:400px;">{html}</div>'

def detect_code_type(code, programming_language):
    backend_keywords = [
        'flask', 'django', 'express', 'fastapi', 'spring', 'controller', 'api', 'server', 'database', 'sql', 'mongoose'
    ]
    frontend_keywords = [
        'react', 'vue', 'angular', 'component', 'html', 'css', 'document.getelementbyid', 'window.', 'render', 'jsx',
        '<html', '<body', '<script', '<div', 'getelementbyid', 'queryselector', 'addeventlistener', 'innerhtml'
    ]
    data_science_keywords = [
        'pandas', 'numpy', 'sklearn', 'matplotlib', 'seaborn', 'plt', 'train_test_split', 'randomforestclassifier', 'classification_report'
    ]
    code_lower = code.lower()
    if any(word in code_lower for word in data_science_keywords):
        return 'data_science'
    if any(word in code_lower for word in frontend_keywords):
        return 'frontend'
    if programming_language.lower() in ['python', 'java', 'c#']:
        if any(word in code_lower for word in backend_keywords):
            return 'backend'
    if programming_language.lower() in ['javascript', 'typescript', 'java', 'c#']:
        if any(word in code_lower for word in frontend_keywords):
            return 'frontend'
    if programming_language.lower() in ['python', 'java', 'c#']:
        return 'backend'
    if programming_language.lower() in ['javascript', 'typescript']:
        return 'frontend'
    return 'unknown'

def code_complexity(code):
    lines = code.count('\n') + 1
    functions = code.count('def ')
    classes = code.count('class ')
    comments = code.count('#')
    return f"Lines: {lines}, Functions: {functions}, Classes: {classes}, Comments: {comments}"

def code_matches_language(code: str, language: str) -> bool:
    code_lower = code.strip().lower()
    language = language.lower()
    patterns = {
        "python": [
            "def ", "class ", "import ", "from ", "try:", "except", "raise", "lambda",
            "with ", "yield", "async ", "await ", "print(", "self.", "__init__", "__name__",
            "if __name__ == '__main__':", "#!",
        ],
        "c++": [
            "#include", "int main(", "std::", "::", "cout <<", "cin >>", "new ", "delete ",
            "try {", "catch(", "template<", "using namespace", "class ", "struct ", "#define",
        ],
        "java": [
            "package ", "import java.", "public class", "private ", "protected ", "public static void main",
            "System.out.println", "try {", "catch(", "throw new ", "implements ", "extends ",
            "@Override", "interface ", "enum ", "synchronized ", "final ",
        ],
        "c#": [
            "using System", "namespace ", "class ", "interface ", "public static void Main",
            "Console.WriteLine", "try {", "catch(", "throw ", "async ", "await ", "get;", "set;",
            "List<", "Dictionary<", "[Serializable]", "[Obsolete]",
        ],
        "javascript": [
            "function ", "const ", "let ", "var ", "document.", "window.", "console.log",
            "if(", "for(", "while(", "switch(", "try {", "catch(", "export ", "import ", "async ",
            "await ", "=>", "this.", "class ", "prototype", "new ", "$(",
        ],
        "typescript": [
            "function ", "const ", "let ", "interface ", "type ", ": string", ": number", ": boolean",
            "implements ", "extends ", "enum ", "public ", "private ", "protected ", "readonly ",
            "import ", "export ", "console.log", "async ", "await ", "=>", "this.",
        ],
        "html": [
            "<!doctype html", "<html", "<head>", "<body>", "<script", "<style", "<meta ", "<link ",
            "<title>", "<div", "<span", "<p>", "<h1>", "<ul>", "<li>", "<form", "<input", "<button",
            "<table", "<footer", "<header", "<section", "<article", "<nav", "<img", "<a ", "</html>",
        ],
    }
    match_patterns = patterns.get(language, [])
    match_count = sum(1 for pattern in match_patterns if pattern in code_lower)
    return match_count >= 1

# --- Chat History ---
if 'blackbox_chat_history' not in st.session_state:
    st.session_state['blackbox_chat_history'] = []

def add_to_blackbox_history(prompt, response, mode):
    st.session_state['blackbox_chat_history'].append({
        'mode': mode,  # 'workflow' or 'semantic_search'
        'prompt': prompt,
        'response': response
    })

def show_blackbox_history():
    st.sidebar.markdown('---')
    st.sidebar.subheader('🕑 Blackbox Agent Chat History')
    if not st.session_state['blackbox_chat_history']:
        st.sidebar.info('No chat history this session.')
    else:
        for i, entry in enumerate(reversed(st.session_state['blackbox_chat_history'])):
            with st.sidebar.expander(f"{entry['mode'].replace('_', ' ').title()} #{len(st.session_state['blackbox_chat_history'])-i}"):
                st.markdown(f"**Prompt:**\n{entry['prompt']}")
                st.markdown(f"**Response:**\n{entry['response']}")

# --- Page config ---
st.set_page_config(page_title="🚀 AI Assistant with Workflow + Semantic Search", layout="wide")

# --- Sidebar ---
st.sidebar.title("🔧 Configuration")
lang = st.sidebar.selectbox("Programming Language", ["Python", "JavaScript", "C++", "Java", "C#", "TypeScript"])
skill = st.sidebar.selectbox("Skill Level", ["Beginner", "Intermediate", "Expert"])
role = st.sidebar.selectbox("Your Role", ["Student", "Frontend Developer", "Backend Developer", "Data Scientist"])
explain_lang = st.sidebar.selectbox("Explanation Language", ["English", "Spanish", "Chinese", "Urdu"])
st.sidebar.markdown("---")
st.sidebar.markdown("<span style='color:#fff;'>Powered by <b>BLACKBOX.AI</b></span>", unsafe_allow_html=True)

# Show chat history in sidebar
show_blackbox_history()

# Download chat history
if st.session_state['blackbox_chat_history']:
    chat_history_text = ""
    for entry in st.session_state['blackbox_chat_history']:
        chat_history_text += f"Mode: {entry['mode']}\nPrompt: {entry['prompt']}\nResponse: {entry['response']}\n\n"
    st.sidebar.download_button(
        label="Download Chat History",
        data=chat_history_text,
        file_name="blackbox_chat_history.txt",
        mime="text/plain"
    )

tabs = st.tabs(["🧠 Full AI Workflow", "🔍 Semantic Search"])

# --- Tab 1: Full AI Workflow ---
with tabs[0]:
    st.title("🧠 Full AI Workflow")
    file_types = {
        "Python": ["py"],
        "JavaScript": ["js"],
        "C++": ["cpp", "h", "hpp"],
        "Java": ["java"],
        "C#": ["cs"],
        "TypeScript": ["ts"],
    }

    uploaded_file = st.file_uploader(
        f"Upload {', '.join(file_types.get(lang, []))} file(s)", 
        type=file_types.get(lang, None)
    )
    if uploaded_file:
        code_input = uploaded_file.read().decode("utf-8")
    else:
        code_input = st.text_area("Your Code", height=300, placeholder="Paste your code here...")

    if code_input:
        st.markdown(f"<b>Complexity:</b> {code_complexity(code_input)}", unsafe_allow_html=True)

    if st.button("Run AI Workflow"):
        if not code_input.strip():
            st.warning("Please paste or upload your code.")
        elif not code_matches_language(code_input, lang):
            st.error(f"The pasted code doesn’t look like valid {lang} code. Please check your code or select the correct language.")
        else:  
            code_type = detect_code_type(code_input, lang)
            if code_type == "data_science" and role != "Data Scientist":
                st.error("Data science code detected. Please select 'Data Scientist' role.")
            elif code_type == "frontend" and role != "Frontend Developer":
                st.error("Frontend code detected. Please select 'Frontend Developer' role.")
            elif code_type == "backend" and role != "Backend Developer":
                st.error("Backend code detected. Please select 'Backend Developer' role.")
            else:
                with st.spinner("Running agentic workflow..."):
                    timeline, suggestions = agentic_workflow(code_input, skill, lang, explain_lang, role)
                
                # Show each step in an expander
                for step in timeline:
                    with st.expander(f"✅ {step['step']} - {step['description']}"):
                        if step['step'] == "Refactor":
                            diff_html = get_inline_diff_html(code_input, step['code'])
                            st.markdown(diff_html, unsafe_allow_html=True)
                            st.code(step['output'], language=lang.lower())
                        else:
                            st.markdown(step['output'])

                st.markdown("#### Agent Suggestions")
                for s in suggestions:
                    st.markdown(f"- {s}")

                # Download buttons after suggestions
                st.markdown("---")
                st.markdown("### 📥 Download Results")

                report_text = ""
                for step in timeline:
                    report_text += f"## {step['step']}\n{step['description']}\n\n{step['output']}\n\n"

                st.download_button(
                    label="📄 Download Full Workflow Report",
                    data=report_text,
                    file_name="ai_workflow_report.txt",
                    mime="text/plain",
                )

# --- Tab 2: Semantic Search ---
with tabs[1]:
    st.title("🔍 Semantic Search")
    sem_code = st.text_area("Your Code", height=300, placeholder="Paste your code...")
    sem_q = st.text_input("Your Question", placeholder="E.g., What does this function do?")
    if st.button("Run Semantic Search"):
        if not sem_code.strip() or not sem_q.strip():
            st.warning("Code and question required.")
        else:
            with st.spinner("Running semantic search..."):
                answer = semantic_search_improved(sem_code, sem_q, lang, skill, role, explain_lang)
                st.markdown("### 📌 Answer")
                st.markdown(answer)

st.markdown("---")