flask_app.py

# flask_app.py
"""
Flask API for DCRM (Dynamic Contact Resistance Measurement) Analysis
Provides endpoints for uploading DCRM graph images and getting AI-powered analysis.
"""

from flask import Flask, request, jsonify
from flask_cors import CORS
import cv2
import numpy as np
import os
import json
import re
import tempfile
import base64
from werkzeug.utils import secure_filename

# Import DCRM modules
from dcrm.image_processing import process_uploaded_image
from dcrm.llm import ask_llm_for_breakage, analyze_health_with_llm
from dcrm.zone_analysis import ZoneAnalyzer

app = Flask(__name__)
CORS(app)  # Enable CORS for all routes

# Configuration
app.config["MAX_CONTENT_LENGTH"] = 16 * 1024 * 1024  # 16MB max file size
ALLOWED_EXTENSIONS = {"png", "jpg", "jpeg"}

# Default processing parameters
DEFAULT_SAT_FACTOR = 3.0
DEFAULT_GAP_SIZE = 1
DEFAULT_NOISE_THRESHOLD = 100
DEFAULT_TOTAL_DURATION = 400
DEFAULT_CROP_OPTION = True
DEFAULT_MODEL_NAME = "gemini-2.5-flash"


def allowed_file(filename):
    """Check if file extension is allowed"""
    return "." in filename and filename.rsplit(".", 1)[1].lower() in ALLOWED_EXTENSIONS


def safe_parse_llm_json(llm_response):
    """Robustly extracts JSON from LLM response, handling markdown and plain text."""
    try:
        # Try finding markdown block first
        json_match = re.search(r"```json\s*(\{.*?\})\s*```", llm_response, re.DOTALL)
        if json_match:
            return json.loads(json_match.group(1))

        # Try finding just a JSON object structure
        json_match_loose = re.search(r"(\{.*\})", llm_response, re.DOTALL)
        if json_match_loose:
            return json.loads(json_match_loose.group(1))

        # Try loading the whole string
        return json.loads(llm_response)
    except:
        return None


def convert_numpy_types(obj):
    """Convert numpy types to Python native types for JSON serialization"""
    if isinstance(obj, dict):
        return {key: convert_numpy_types(value) for key, value in obj.items()}
    elif isinstance(obj, list):
        return [convert_numpy_types(item) for item in obj]
    elif isinstance(obj, np.integer):
        return int(obj)
    elif isinstance(obj, np.floating):
        return float(obj)
    elif isinstance(obj, np.ndarray):
        return obj.tolist()
    elif hasattr(obj, "item"):  # For numpy scalar types
        return obj.item()
    else:
        return obj


def image_to_base64(img_array):
    """Convert a numpy image array to base64 string"""
    if img_array is None:
        return None
    # Ensure it's in BGR format for encoding
    if len(img_array.shape) == 3 and img_array.shape[2] == 3:
        # Convert RGB to BGR if needed (OpenCV expects BGR)
        img_bgr = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
    else:
        img_bgr = img_array

    _, buffer = cv2.imencode(".png", img_bgr)
    return base64.b64encode(buffer).decode("utf-8")


@app.route("/", methods=["GET"])
def index():
    """Root endpoint with API info"""
    return jsonify(
        {
            "service": "DCRM Analysis API",
            "version": "1.0.0",
            "endpoints": {
                "GET /health": "Health check",
                "POST /analyze": "Full DCRM analysis with AI",
                "POST /extract-curves": "Extract curves only (no AI)",
            },
            "docs": "https://github.com/YOUR_REPO/README.md",
        }
    )


@app.route("/health", methods=["GET"])
def health_check():
    """Health check endpoint"""
    return jsonify({"status": "healthy", "service": "DCRM Analysis API"})


@app.route("/analyze", methods=["POST"])
def analyze_image():
    """
    Main endpoint for DCRM image analysis.

    Expects:
        - image: File upload (multipart/form-data) or base64 encoded image
        - api_key: Gemini API key (required)
        - sat_factor: Saturation boost factor (optional, default: 3.0)
        - gap_size: Gap fill size (optional, default: 1)
        - noise_threshold: Minimum object area (optional, default: 100)
        - total_duration: Graph duration in ms (optional, default: 400)
        - crop_option: Auto-crop option (optional, default: true)
        - analysis_method: "image" or "csv" (optional, default: "image")

    Returns:
        JSON response with analysis results
    """
    try:
        # Get API key
        api_key = (
            request.form.get("api_key") or request.json.get("api_key")
            if request.is_json
            else request.form.get("api_key")
        )

        if not api_key:
            # Try to get from environment
            api_key = os.environ.get("GEMINI_API_KEY") or os.environ.get(
                "GOOGLE_API_KEY"
            )

        if not api_key:
            return (
                jsonify(
                    {
                        "error": "API key is required. Provide 'api_key' in the request or set GEMINI_API_KEY environment variable."
                    }
                ),
                400,
            )

        # Get image data
        file_bytes = None

        # Check for file upload
        if "image" in request.files:
            file = request.files["image"]
            if file.filename == "":
                return jsonify({"error": "No file selected"}), 400
            if not allowed_file(file.filename):
                return (
                    jsonify(
                        {
                            "error": f"Invalid file type. Allowed: {', '.join(ALLOWED_EXTENSIONS)}"
                        }
                    ),
                    400,
                )
            file_bytes = file.read()

        # Check for base64 image
        elif request.is_json and "image_base64" in request.json:
            try:
                file_bytes = base64.b64decode(request.json["image_base64"])
            except Exception as e:
                return jsonify({"error": f"Invalid base64 image: {str(e)}"}), 400

        else:
            return (
                jsonify(
                    {
                        "error": "No image provided. Use 'image' file upload or 'image_base64' in JSON."
                    }
                ),
                400,
            )

        # Get processing parameters
        if request.is_json:
            params = request.json
        else:
            params = request.form

        sat_factor = float(params.get("sat_factor", DEFAULT_SAT_FACTOR))
        gap_size = int(params.get("gap_size", DEFAULT_GAP_SIZE))
        noise_threshold = int(params.get("noise_threshold", DEFAULT_NOISE_THRESHOLD))
        total_duration = int(params.get("total_duration", DEFAULT_TOTAL_DURATION))
        crop_option = str(params.get("crop_option", "true")).lower() == "true"
        analysis_method = params.get("analysis_method", "image")
        model_name = params.get("model_name", DEFAULT_MODEL_NAME)
        include_debug_images = (
            str(params.get("include_debug_images", "false")).lower() == "true"
        )

        # Step 1: Extract curves from image
        df_result, debug_images, bounds, error_msg, _ = process_uploaded_image(
            file_bytes,
            sat_factor,
            gap_size,
            noise_threshold,
            crop_option,
            total_duration,
        )

        if error_msg:
            return (
                jsonify(
                    {
                        "error": f"Curve extraction failed: {error_msg}",
                        "stage": "extraction",
                    }
                ),
                400,
            )

        # Step 2: Get LLM segmentation
        cropped_bytes = None
        if bounds:
            try:
                sx, ex = bounds
                nparr = np.frombuffer(file_bytes, np.uint8)
                img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
                if img is not None:
                    cropped_img = img[:, sx:ex]
                    is_success, buffer = cv2.imencode(".jpg", cropped_img)
                    if is_success:
                        cropped_bytes = buffer.tobytes()
            except Exception as e:
                pass  # Continue without cropped image

        df_result, result_json = ask_llm_for_breakage(
            df_result, api_key, model_name, image_bytes=cropped_bytes
        )

        if not result_json or "error" in result_json:
            return (
                jsonify(
                    {
                        "error": "AI segmentation failed",
                        "details": (
                            result_json.get("error") if result_json else "Unknown error"
                        ),
                        "stage": "segmentation",
                    }
                ),
                400,
            )

        # Step 3: Perform zone health analysis
        zone_analysis = {}
        analysis_type = ""
        analysis_data = None
        executive_lead = None
        issues = []

        success_expert_image = False

        if analysis_method.lower() == "image":
            # Image-based analysis
            numerical_context = {}
            if "Resistance" in df_result.columns:
                valid_res = df_result["Resistance"].dropna()
                if not valid_res.empty:
                    numerical_context["min_resistance"] = float(valid_res.min())
                    numerical_context["median_resistance"] = float(valid_res.median())

            img_bytes_for_analysis = cropped_bytes if cropped_bytes else file_bytes
            llm_response = analyze_health_with_llm(
                img_bytes_for_analysis, api_key, model_name, numerical_context
            )

            if isinstance(llm_response, dict) and "error" in llm_response:
                analysis_type = "Image-Based (Failed) - Fallback to CSV"
                success_expert_image = False
            else:
                analysis_data = safe_parse_llm_json(llm_response)

                if analysis_data:
                    executive_lead = llm_response.split("{")[0].strip()
                    if "```json" in executive_lead:
                        executive_lead = executive_lead.replace("```json", "").strip()

                    issues = analysis_data.get("detected_issues", [])

                    extracted_score = analysis_data.get("health_score")
                    status = analysis_data.get("overall_condition", "Unknown")

                    if extracted_score is None:
                        if status == "Healthy":
                            extracted_score = 100
                        elif status == "Warning":
                            extracted_score = 60
                        elif status == "Critical":
                            extracted_score = 20
                        else:
                            extracted_score = 0

                    zone_analysis = {
                        "overall_health": {
                            "status": status,
                            "overall_score": extracted_score,
                            "recommendation": analysis_data.get(
                                "maintenance_recommendation"
                            ),
                            "total_issues": len(issues),
                            "critical_issues": [],
                        }
                    }
                    analysis_type = "Expert Image Diagnostic"
                    success_expert_image = True
                else:
                    analysis_type = "Image-Based (Parse Error) - Fallback to CSV"
                    success_expert_image = False

        # Fallback to CSV analysis
        if not success_expert_image:
            analyzer = ZoneAnalyzer(df_result, result_json)
            zone_analysis = analyzer.analyze_all_zones()
            analysis_type = "CSV-Based"

        # Prepare response
        response_data = {
            "success": True,
            "analysis_type": analysis_type,
            "segmentation": convert_numpy_types(result_json),
            "zone_analysis": convert_numpy_types(zone_analysis),
            "curve_data": {
                "columns": df_result.columns.tolist(),
                "data": df_result.to_dict(orient="records"),
                "num_points": len(df_result),
            },
            "processing_params": {
                "sat_factor": sat_factor,
                "gap_size": gap_size,
                "noise_threshold": noise_threshold,
                "total_duration": total_duration,
                "crop_option": crop_option,
            },
        }

        # Add expert analysis details if available
        if analysis_data:
            response_data["expert_analysis"] = {
                "executive_summary": executive_lead,
                "detailed_analysis": convert_numpy_types(analysis_data),
                "issues": convert_numpy_types(issues),
            }

        # Include debug images if requested
        if include_debug_images and debug_images:
            response_data["debug_images"] = {}
            for name, img in debug_images.items():
                img_b64 = image_to_base64(img)
                if img_b64:
                    response_data["debug_images"][name] = img_b64

        return jsonify(convert_numpy_types(response_data))

    except Exception as e:
        import traceback

        return (
            jsonify(
                {
                    "error": f"Internal server error: {str(e)}",
                    "traceback": traceback.format_exc(),
                }
            ),
            500,
        )


@app.route("/extract-curves", methods=["POST"])
def extract_curves():
    """
    Lightweight endpoint that only extracts curves without LLM analysis.
    Useful for quick data extraction without AI processing.

    Expects:
        - image: File upload (multipart/form-data) or base64 encoded image
        - sat_factor, gap_size, noise_threshold, total_duration, crop_option (optional)

    Returns:
        JSON with extracted curve data
    """
    try:
        # Get image data
        file_bytes = None

        if "image" in request.files:
            file = request.files["image"]
            if file.filename == "":
                return jsonify({"error": "No file selected"}), 400
            if not allowed_file(file.filename):
                return (
                    jsonify(
                        {
                            "error": f"Invalid file type. Allowed: {', '.join(ALLOWED_EXTENSIONS)}"
                        }
                    ),
                    400,
                )
            file_bytes = file.read()

        elif request.is_json and "image_base64" in request.json:
            try:
                file_bytes = base64.b64decode(request.json["image_base64"])
            except Exception as e:
                return jsonify({"error": f"Invalid base64 image: {str(e)}"}), 400

        else:
            return jsonify({"error": "No image provided"}), 400

        # Get processing parameters
        if request.is_json:
            params = request.json
        else:
            params = request.form

        sat_factor = float(params.get("sat_factor", DEFAULT_SAT_FACTOR))
        gap_size = int(params.get("gap_size", DEFAULT_GAP_SIZE))
        noise_threshold = int(params.get("noise_threshold", DEFAULT_NOISE_THRESHOLD))
        total_duration = int(params.get("total_duration", DEFAULT_TOTAL_DURATION))
        crop_option = str(params.get("crop_option", "true")).lower() == "true"
        include_debug_images = (
            str(params.get("include_debug_images", "false")).lower() == "true"
        )

        # Extract curves
        df_result, debug_images, bounds, error_msg, _ = process_uploaded_image(
            file_bytes,
            sat_factor,
            gap_size,
            noise_threshold,
            crop_option,
            total_duration,
        )

        if error_msg:
            return jsonify({"error": f"Curve extraction failed: {error_msg}"}), 400

        response_data = {
            "success": True,
            "curve_data": {
                "columns": df_result.columns.tolist(),
                "data": df_result.to_dict(orient="records"),
                "num_points": len(df_result),
            },
            "bounds": bounds,
            "processing_params": {
                "sat_factor": sat_factor,
                "gap_size": gap_size,
                "noise_threshold": noise_threshold,
                "total_duration": total_duration,
                "crop_option": crop_option,
            },
        }

        if include_debug_images and debug_images:
            response_data["debug_images"] = {}
            for name, img in debug_images.items():
                img_b64 = image_to_base64(img)
                if img_b64:
                    response_data["debug_images"][name] = img_b64

        return jsonify(convert_numpy_types(response_data))

    except Exception as e:
        import traceback

        return (
            jsonify(
                {
                    "error": f"Internal server error: {str(e)}",
                    "traceback": traceback.format_exc(),
                }
            ),
            500,
        )


@app.errorhandler(413)
def too_large(e):
    return jsonify({"error": "File too large. Maximum size is 16MB."}), 413


@app.errorhandler(404)
def not_found(e):
    return jsonify({"error": "Endpoint not found"}), 404


@app.errorhandler(500)
def internal_error(e):
    return jsonify({"error": "Internal server error"}), 500


if __name__ == "__main__":
    # Get port from environment or use default (7860 for Hugging Face Spaces)
    port = int(os.environ.get("PORT", 7860))
    debug = os.environ.get("FLASK_DEBUG", "false").lower() == "true"

    print(
        f"""
    ╔══════════════════════════════════════════════════════════════╗
    ║          DCRM Analysis API - Flask Server                    ║
    ╠══════════════════════════════════════════════════════════════╣
    ║  Endpoints:                                                  ║
    ║    GET  /health         - Health check                       ║
    ║    POST /analyze        - Full DCRM analysis with AI         ║
    ║    POST /extract-curves - Extract curves only (no AI)        ║
    ╚══════════════════════════════════════════════════════════════╝
    """
    )

    app.run(host="0.0.0.0", port=port, debug=debug)