app.py

import streamlit as st
import google.generativeai as genai
import zipfile
import io
import json
import os
from pathlib import Path
import time

# --- Configuration ---
GEMINI_MODEL_NAME = "gemini-2.5-pro-preview-03-25"
MAX_PROMPT_TOKENS_ESTIMATE = 800000
RESULTS_PAGE_SIZE = 25  # Number of items to show per category initially

AVAILABLE_ANALYSES = {
    # ... (keep the same)
    "generate_docs": "Generate Missing Docstrings/Comments",
    "find_bugs": "Identify Potential Bugs & Anti-patterns",
    "check_style": "Check Style Guide Compliance (General)",
    "summarize_modules": "Summarize Complex Modules/Files",
    "suggest_refactoring": "Suggest Refactoring Opportunities"
}
CODE_EXTENSIONS = {'.py', '.js', '.java', '.c', '.cpp', '.h', '.cs', '.go', '.rb', '.php', '.swift', '.kt', '.ts', '.html', '.css', '.scss', '.sql'}

# --- Session State Initialization ---
if 'mock_api_call' not in st.session_state:
    st.session_state.mock_api_call = False
if 'analysis_results' not in st.session_state:
    st.session_state.analysis_results = None  # Store results here
if 'error_message' not in st.session_state:
    st.session_state.error_message = None
if 'analysis_requested' not in st.session_state:
    st.session_state.analysis_requested = False  # Flag to know when analysis is done

# --- Gemini API Setup ---
model = None

def initialize_gemini_model():
    """Initializes the Gemini API model unless running in mock mode."""
    global model
    if model is None and not st.session_state.mock_api_call:
        try:
            if 'GEMINI_API_KEY' not in st.secrets:
                st.error("🚨 Gemini API Key not found. Add it to `.streamlit/secrets.toml`.")
                st.stop()
            genai.configure(api_key=st.secrets["GEMINI_API_KEY"])
            model = genai.GenerativeModel(GEMINI_MODEL_NAME)
            print("Gemini Model Initialized.")
            return True
        except Exception as e:
            st.error(f"🚨 Error initializing Gemini SDK: {e}")
            st.stop()
            return False
    elif st.session_state.mock_api_call:
        # Running in Mock Mode. Skipping Gemini initialization.
        return True  # Allow proceeding in mock mode
    elif model is not None:
        # Gemini Model already initialized.
        return True
    return False

# --- Helper Functions ---

def estimate_token_count(text):
    """Roughly estimate token count (assuming ~3 characters per token)."""
    return len(text) // 3

# --- OPTIMIZATION: Cache ZIP processing ---
@st.cache_data(max_entries=5)  # Cache results for recent uploads
def process_zip_file_cached(file_id, file_size, file_content_bytes):
    """Extracts code files and their content. Cached function."""
    code_files = {}
    total_chars = 0
    file_count = 0
    ignored_files = []
    status_placeholder = st.empty()  # For progress bar
    progress_bar = status_placeholder.progress(0)

    try:
        with zipfile.ZipFile(io.BytesIO(file_content_bytes), 'r') as zip_ref:
            members = zip_ref.infolist()
            total_members = len(members)
            for i, member in enumerate(members):
                # Update progress bar periodically (every 10 files)
                if i % 10 == 0:
                    progress_bar.progress(int((i / total_members) * 100))

                if member.is_dir() or any(part.startswith('.') for part in Path(member.filename).parts) or '__' in member.filename:
                    continue

                file_path = Path(member.filename)
                if file_path.suffix.lower() in CODE_EXTENSIONS:
                    try:
                        with zip_ref.open(member) as file:
                            file_bytes = file.read()
                            try:
                                content = file_bytes.decode('utf-8')
                            except UnicodeDecodeError:
                                try:
                                    content = file_bytes.decode('latin-1')
                                except Exception as decode_err:
                                    ignored_files.append(f"{member.filename} (Decode Error: {decode_err})")
                                    continue

                            code_files[member.filename] = content
                            total_chars += len(content)
                            file_count += 1
                    except Exception as read_err:
                        ignored_files.append(f"{member.filename} (Read Error: {read_err})")
                else:
                    if not (any(part.startswith('.') for part in Path(member.filename).parts) or '__' in member.filename):
                        ignored_files.append(f"{member.filename} (Skipped Extension: {file_path.suffix})")

            progress_bar.progress(100)  # Ensure it completes
            status_placeholder.empty()  # Remove progress bar after completion

    except zipfile.BadZipFile:
        status_placeholder.empty()
        st.error("🚨 Invalid or corrupted ZIP file.")
        return None, 0, 0, []
    except Exception as e:
        status_placeholder.empty()
        st.error(f"🚨 Error processing ZIP file: {e}")
        return None, 0, 0, []

    if file_count == 0 and not ignored_files:
        st.warning("No files with recognized code extensions found in the ZIP.")
    elif file_count == 0 and ignored_files:
        st.warning("No files with recognized code extensions found. Some files were skipped.")

    print(f"Cache miss or new file: Processed ZIP {file_id}")  # Debug print
    return code_files, total_chars, file_count, ignored_files

def construct_analysis_prompt(code_files_dict, requested_analyses):
    """Constructs the prompt for Gemini, including code content and JSON structure request."""
    prompt_parts = ["Analyze the following codebase provided as a collection of file paths and their content.\n\n"]
    current_token_estimate = estimate_token_count(prompt_parts[0])
    included_files = []

    # Use join for potentially faster concatenation
    code_segments = []

    # Provide feedback for large codebases
    prompt_status = st.empty()
    if len(code_files_dict) > 50:
        prompt_status.write("Constructing prompt (processing files)...")

    for filename, content in code_files_dict.items():
        file_marker = f"--- START FILE: {filename} ---\n"
        file_content = f"{content}\n"
        file_end_marker = f"--- END FILE: {filename} ---\n\n"
        segment = file_marker + file_content + file_end_marker
        segment_token_estimate = estimate_token_count(segment)

        if current_token_estimate + segment_token_estimate <= MAX_PROMPT_TOKENS_ESTIMATE:
            code_segments.append(segment)
            current_token_estimate += segment_token_estimate
            included_files.append(filename)
        else:
            st.warning(f"⚠️ Codebase may exceed context window estimate (~{MAX_PROMPT_TOKENS_ESTIMATE} tokens). Analysis performed only on the first {len(included_files)} files ({current_token_estimate:,} tokens).")
            break

    prompt_status.empty()  # Clear status message

    if not included_files:
        st.error("🚨 No code files could be included within the estimated token limit.")
        return None, []

    concatenated_code = "".join(code_segments)
    prompt_parts.append(concatenated_code)

    # Generate the expected JSON structure description based on selected analyses
    json_structure_description = "{\n"
    structure_parts = []
    if "generate_docs" in requested_analyses:
        structure_parts.append('    "documentation_suggestions": [{"file": "path/to/file", "line": number, "suggestion": "Suggested docstring/comment"}]')
    if "find_bugs" in requested_analyses:
        structure_parts.append('    "potential_bugs": [{"file": "path/to/file", "line": number, "description": "Description of potential bug/anti-pattern", "severity": "High/Medium/Low"}]')
    if "check_style" in requested_analyses:
        structure_parts.append('    "style_issues": [{"file": "path/to/file", "line": number, "description": "Description of style deviation"}]')
    if "summarize_modules" in requested_analyses:
        structure_parts.append('    "module_summaries": [{"file": "path/to/file", "summary": "One-paragraph summary of the file purpose/functionality"}]')
    if "suggest_refactoring" in requested_analyses:
        structure_parts.append('    "refactoring_suggestions": [{"file": "path/to/file", "line": number, "area": "e.g., function name, class name", "suggestion": "Description of refactoring suggestion"}]')

    json_structure_description += ",\n".join(structure_parts)
    json_structure_description += "\n}"

    prompt_footer = f"""
**Analysis Task:**
Perform the analyses corresponding to the keys present in the JSON structure below, based *only* on the provided code files ({', '.join(included_files)}).

**Output Format:**
Respond ONLY with a single, valid JSON object adhering strictly to the following structure. If no issues/suggestions are found for a category, provide an empty list `[]`. Do not include explanations outside the JSON structure.

{json_structure_description}

**JSON Output Only:**
"""
    prompt_parts.append(prompt_footer)
    full_prompt = "".join(prompt_parts)
    return full_prompt, included_files

def call_gemini_api(prompt):
    """Calls the Gemini API or returns mock data based on session state."""
    if not prompt:
        return None, "Prompt generation failed."

    # MOCK MODE LOGIC
    if st.session_state.mock_api_call:
        st.info("MOCK MODE: Simulating API call...")
        st.write("...")  # Minimal feedback in mock mode
        time.sleep(1)  # Shorter mock delay

        mock_json_response = json.dumps({
            "documentation_suggestions": [{"file": "mock/core.py", "line": 15, "suggestion": "def process_data(data):\n    \"\"\"Processes the input data using mock logic.\"\"\""}],
            "potential_bugs": [{"file": "mock/utils.py", "line": 22, "description": "Potential division by zero if denominator is not checked.", "severity": "Medium"}],
            "style_issues": [],
            "module_summaries": [],
            "refactoring_suggestions": []
        })
        st.success("Mock response generated.")
        return json.loads(mock_json_response), None

    # REAL API CALL LOGIC
    else:
        if not initialize_gemini_model():
            return None, "Gemini Model Initialization Failed."
        if model is None:
            return None, "Gemini model not available."

        try:
            api_status = st.empty()
            token_estimate = estimate_token_count(prompt)
            api_status.info(f"📡 Sending request to {GEMINI_MODEL_NAME} (Estimated prompt tokens: {token_estimate:,})... This can take several minutes depending on code size and model load.")
            start_time = time.time()
            response = model.generate_content(
                prompt,
                generation_config=genai.types.GenerationConfig(temperature=0.2),
                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"},
                ]
            )
            end_time = time.time()
            api_status.success(f"✅ Response received from AI in {end_time - start_time:.2f} seconds.")
            time.sleep(1)
            api_status.empty()

            try:
                json_response_text = response.text.strip()
                if json_response_text.startswith("```json"):
                    json_response_text = json_response_text[7:]
                if json_response_text.startswith("```"):
                    json_response_text = json_response_text[3:]
                if json_response_text.endswith("```"):
                    json_response_text = json_response_text[:-3]
                json_start = json_response_text.find('{')
                json_end = json_response_text.rfind('}') + 1
                if json_start != -1 and json_end != -1 and json_end > json_start:
                    final_json_text = json_response_text[json_start:json_end]
                    insights = json.loads(final_json_text)
                    return insights, None
                else:
                    st.warning("⚠️ Could not find valid JSON object boundaries ({...}) in response.")
                    return {"raw_response": response.text}, "AI response did not contain clear JSON object, showing raw text."
            except json.JSONDecodeError as json_err:
                st.error(f"🚨 Error parsing JSON response from AI: {json_err}")
                st.code(response.text, language='text')
                return None, f"AI response was not valid JSON: {json_err}"
            except AttributeError:
                st.error("🚨 Unexpected API response structure (AttributeError).")
                st.code(f"Response object: {response}", language='text')
                try:
                    block_reason = response.prompt_feedback.block_reason
                    if block_reason:
                        return None, f"Content blocked by API. Reason: {block_reason}"
                except Exception:
                    pass
                return None, "Unexpected response structure from API (AttributeError)."
            except Exception as e:
                st.error(f"🚨 Unexpected issue processing AI response: {e}")
                try:
                    st.code(f"Response object: {response}", language='text')
                except Exception:
                    pass
                return None, f"Unexpected response structure: {e}"
        except Exception as e:
            api_status.empty()
            st.error(f"🚨 An error occurred during API call: {e}")
            error_msg = f"API call failed: {e}"
            if hasattr(e, 'message'):
                if "429" in e.message:
                    error_msg = "API Quota Exceeded or Rate Limit hit."
                elif "API key not valid" in e.message:
                    error_msg = "Invalid Gemini API Key."
                elif "blocked" in e.message.lower():
                    error_msg = "Content blocked due to safety settings."
            elif "block_reason: SAFETY" in str(e):
                error_msg = "Content blocked due to safety settings."
            return None, error_msg

def display_results(results_json, requested_analyses):
    """Renders the analysis results with pagination."""
    st.header("📊 Analysis Report")
    if not isinstance(results_json, dict):
        st.error("Invalid results format received.")
        st.json(results_json)
        return
    if "raw_response" in results_json:
        st.subheader("Raw AI Response (JSON Parsing Failed)")
        st.code(results_json["raw_response"], language='text')
        return

    display_config = {
        "generate_docs": {"key": "documentation_suggestions", "title": AVAILABLE_ANALYSES["generate_docs"], "fields": {"file": "File", "line": "Line"}},
        "find_bugs": {"key": "potential_bugs", "title": AVAILABLE_ANALYSES["find_bugs"], "fields": {"file": "File", "line": "Line", "severity": "Severity"}},
        "check_style": {"key": "style_issues", "title": AVAILABLE_ANALYSES["check_style"], "fields": {"file": "File", "line": "Line"}},
        "summarize_modules": {"key": "module_summaries", "title": AVAILABLE_ANALYSES["summarize_modules"], "fields": {"file": "File"}},
        "suggest_refactoring": {"key": "refactoring_suggestions", "title": AVAILABLE_ANALYSES["suggest_refactoring"], "fields": {"file": "File", "line": "Line", "area": "Area"}}
    }

    any_results_found = False
    for analysis_key in requested_analyses:
        if analysis_key in display_config:
            config = display_config[analysis_key]
            items = results_json.get(config["key"], [])
            total_items = len(items)

            st.subheader(f"{config['title']} ({total_items} found)")

            if items:
                any_results_found = True
                state_key = f"visible_{analysis_key}"
                if state_key not in st.session_state:
                    st.session_state[state_key] = RESULTS_PAGE_SIZE

                visible_count = st.session_state[state_key]
                items_to_display = items[:visible_count]

                for item in items_to_display:
                    details = []
                    for field_key, field_label in config["fields"].items():
                        value = item.get(field_key, 'N/A')
                        if value != 'N/A':
                            details.append(f"**{field_label}:** `{value}`" if field_key == 'file' else f"**{field_label}:** {value}")
                    st.markdown("- " + " - ".join(details))
                    if 'suggestion' in item:
                        st.code(item['suggestion'], language='text')
                    elif 'description' in item:
                        st.markdown(f"  > {item['description']}")
                    elif 'summary' in item:
                        st.markdown(f"  > {item['summary']}")

                if total_items > visible_count:
                    if st.button(f"Show more ({total_items - visible_count} remaining)", key=f"more_{analysis_key}"):
                        st.session_state[state_key] += RESULTS_PAGE_SIZE
                        st.rerun()
            else:
                st.markdown("_No items found for this category._")
            st.divider()

    if not any_results_found:
        st.info("No specific findings were identified in the analysis based on your selections.")

    st.download_button(
        label="Download Full Report (JSON)",
        data=json.dumps(results_json, indent=4),
        file_name="code_audit_report.json",
        mime="application/json"
    )

# --- Streamlit App Main Interface ---
st.set_page_config(page_title="Codebase Audit Assistant", layout="wide")
st.title("🤖 Codebase Audit Assistant")
st.markdown(f"Upload codebase (`.zip`) for analysis via **{GEMINI_MODEL_NAME}**.")

with st.sidebar:
    st.header("⚙️ Analysis Controls")
    st.session_state.mock_api_call = st.toggle("🧪 Enable Mock API Mode", value=st.session_state.mock_api_call, help="Use fake data instead of calling Gemini API.")
    st.info("Mock API Mode ACTIVE" if st.session_state.mock_api_call else "Using REAL Gemini API")
    st.divider()
    st.header("🔎 Select Analyses")
    selected_analyses = [key for key, name in AVAILABLE_ANALYSES.items() if st.checkbox(name, value=True, key=f"cb_{key}")]
    st.divider()
    st.header("📄 How To Use")
    st.info("1. Set API Key (if not in Mock Mode).\n2. Toggle Mock Mode if needed.\n3. Select analyses.\n4. Create & Upload a **ZIP** of your code.\n5. Click 'Analyze Codebase'.\n6. Review the report.")
    st.info(f"Note: Only common code extensions are supported. Analysis is limited by token estimates (~{MAX_PROMPT_TOKENS_ESTIMATE:,} estimated tokens).")
    st.divider()
    st.warning("⚠️ **Privacy:** Code is sent to the Google API if Mock Mode is OFF.")

uploaded_file = st.file_uploader("📁 Upload Codebase ZIP File", type=['zip'], key="file_uploader",
                                 on_change=lambda: st.session_state.update(analysis_results=None, error_message=None, analysis_requested=False))

analysis_button_placeholder = st.empty()  # Placeholder for the button
results_placeholder = st.container()        # Container for results display

if uploaded_file:
    st.success(f"✅ File '{uploaded_file.name}' uploaded.")

    uploaded_file_bytes = uploaded_file.getvalue()
    file_id = f"{uploaded_file.name}-{uploaded_file.size}"

    code_files, total_chars, file_count, ignored_files = process_zip_file_cached(file_id, uploaded_file.size, uploaded_file_bytes)

    if code_files is not None:
        st.info(f"Found **{file_count}** relevant code files ({total_chars:,} characters). Est. tokens: ~{estimate_token_count(total_chars):,}")
        if ignored_files:
            with st.expander(f"View {len(ignored_files)} Skipped/Ignored Files"):
                st.code("\n".join(ignored_files), language='text')

        analyze_button_disabled = (not selected_analyses or file_count == 0)
        analyze_button_label = "Analyze Codebase" if not analyze_button_disabled else "Select Analyses or Upload Valid Code"
        if analysis_button_placeholder.button(analyze_button_label, type="primary", disabled=analyze_button_disabled):
            st.session_state.analysis_requested = True
            st.session_state.analysis_results = None
            st.session_state.error_message = None

            if not selected_analyses:
                st.warning("Please select analysis types.")
            elif file_count == 0:
                st.warning("No relevant code files found.")
            else:
                with results_placeholder:
                    with st.spinner(f"🚀 Preparing prompt & contacting AI ({'Mock Mode' if st.session_state.mock_api_call else GEMINI_MODEL_NAME})... Please wait."):
                        analysis_prompt, included_files_in_prompt = construct_analysis_prompt(code_files, selected_analyses)
                        if analysis_prompt and included_files_in_prompt:
                            results_json, error_msg = call_gemini_api(analysis_prompt)
                            st.session_state.analysis_results = results_json
                            st.session_state.error_message = error_msg
                        elif not included_files_in_prompt:
                            st.session_state.error_message = "Could not proceed: No files included (check token limits/errors)."
                        else:
                            st.session_state.error_message = "Failed to generate analysis prompt."
                st.rerun()

if st.session_state.analysis_requested:
    with results_placeholder:
        st.divider()
        if st.session_state.error_message:
            st.error(f"Analysis Failed: {st.session_state.error_message}")
            if isinstance(st.session_state.analysis_results, dict) and "raw_response" in st.session_state.analysis_results:
                st.subheader("Raw AI Response")
                st.code(st.session_state.analysis_results["raw_response"], language='text')
        elif st.session_state.analysis_results:
            display_results(st.session_state.analysis_results, selected_analyses)
        else:
            st.info("Analysis initiated, but no results or errors were stored. Please try again.")

elif not uploaded_file:
    results_placeholder.info("Upload a ZIP file containing your source code to begin.")

results_placeholder.divider()
results_placeholder.markdown("_Assistant powered by Google Gemini._")