mediSync/utils/visualization.py

import base64
import io
import logging

import cv2
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image

# Set up logging
logger = logging.getLogger(__name__)


def plot_image_prediction(image, predictions, title=None, figsize=(10, 8)):
    """

    Plot an image with its predictions.



    Args:

        image (PIL.Image or str): Image or path to image

        predictions (list): List of (label, probability) tuples

        title (str, optional): Plot title

        figsize (tuple): Figure size



    Returns:

        matplotlib.figure.Figure: The figure object

    """
    try:
        # Load image if path is provided
        if isinstance(image, str):
            img = Image.open(image)
        else:
            img = image

        # Create figure
        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=figsize)

        # Plot image
        ax1.imshow(img)
        ax1.set_title("X-ray Image")
        ax1.axis("off")

        # Plot predictions
        if predictions:
            # Sort predictions by probability
            sorted_pred = sorted(predictions, key=lambda x: x[1], reverse=True)

            # Get top 5 predictions
            top_n = min(5, len(sorted_pred))
            labels = [pred[0] for pred in sorted_pred[:top_n]]
            probs = [pred[1] for pred in sorted_pred[:top_n]]

            # Plot horizontal bar chart
            y_pos = np.arange(top_n)
            ax2.barh(y_pos, probs, align="center")
            ax2.set_yticks(y_pos)
            ax2.set_yticklabels(labels)
            ax2.set_xlabel("Probability")
            ax2.set_title("Top Predictions")
            ax2.set_xlim(0, 1)

            # Annotate probabilities
            for i, prob in enumerate(probs):
                ax2.text(prob + 0.02, i, f"{prob:.1%}", va="center")

        # Set overall title
        if title:
            fig.suptitle(title, fontsize=16)

        fig.tight_layout()
        return fig

    except Exception as e:
        logger.error(f"Error plotting image prediction: {e}")
        # Create empty figure if error occurs
        fig, ax = plt.subplots(figsize=(8, 6))
        ax.text(0.5, 0.5, f"Error: {str(e)}", ha="center", va="center")
        return fig


def create_heatmap_overlay(image, heatmap, alpha=0.4):
    """

    Create a heatmap overlay on an X-ray image to highlight areas of interest.



    Args:

        image (PIL.Image or str): Image or path to image

        heatmap (numpy.ndarray): Heatmap array

        alpha (float): Transparency of the overlay



    Returns:

        PIL.Image: Image with heatmap overlay

    """
    try:
        # Load image if path is provided
        if isinstance(image, str):
            img = cv2.imread(image)
            if img is None:
                raise ValueError(f"Could not load image: {image}")
        elif isinstance(image, Image.Image):
            img = np.array(image)
            img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
        else:
            img = image

        # Ensure image is in BGR format for OpenCV
        if len(img.shape) == 2:  # Grayscale
            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)

        # Resize heatmap to match image dimensions
        heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))

        # Normalize heatmap (0-1)
        heatmap = np.maximum(heatmap, 0)
        heatmap = np.minimum(heatmap / np.max(heatmap), 1)

        # Apply colormap (jet) to heatmap
        heatmap = np.uint8(255 * heatmap)
        heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)

        # Create overlay
        overlay = cv2.addWeighted(img, 1 - alpha, heatmap, alpha, 0)

        # Convert back to PIL image
        overlay = cv2.cvtColor(overlay, cv2.COLOR_BGR2RGB)
        overlay_img = Image.fromarray(overlay)

        return overlay_img

    except Exception as e:
        logger.error(f"Error creating heatmap overlay: {e}")
        # Return original image if error occurs
        if isinstance(image, str):
            return Image.open(image)
        elif isinstance(image, Image.Image):
            return image
        else:
            return Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))


def plot_report_entities(text, entities, figsize=(12, 8)):
    """

    Visualize entities extracted from a medical report.



    Args:

        text (str): Report text

        entities (dict): Dictionary of entities by category

        figsize (tuple): Figure size



    Returns:

        matplotlib.figure.Figure: The figure object

    """
    try:
        fig, ax = plt.subplots(figsize=figsize)
        ax.axis("off")

        # Set background color
        fig.patch.set_facecolor("#f8f9fa")
        ax.set_facecolor("#f8f9fa")

        # Title
        ax.text(
            0.5,
            0.98,
            "Medical Report Analysis",
            ha="center",
            va="top",
            fontsize=18,
            fontweight="bold",
            color="#2c3e50",
        )

        # Display entity counts
        y_pos = 0.9
        ax.text(
            0.05,
            y_pos,
            "Extracted Entities:",
            fontsize=14,
            fontweight="bold",
            color="#2c3e50",
        )
        y_pos -= 0.05

        # Define colors for different entity categories
        category_colors = {
            "problem": "#e74c3c",  # Red
            "test": "#3498db",  # Blue
            "treatment": "#2ecc71",  # Green
            "anatomy": "#9b59b6",  # Purple
        }

        # Display entities by category
        for category, items in entities.items():
            if items:
                y_pos -= 0.05
                ax.text(
                    0.1,
                    y_pos,
                    f"{category.capitalize()}:",
                    fontsize=12,
                    fontweight="bold",
                )
                y_pos -= 0.05
                ax.text(
                    0.15,
                    y_pos,
                    ", ".join(items),
                    wrap=True,
                    fontsize=11,
                    color=category_colors.get(category, "black"),
                )

        # Add the report text with highlighted entities
        y_pos -= 0.1
        ax.text(
            0.05,
            y_pos,
            "Report Text (with highlighted entities):",
            fontsize=14,
            fontweight="bold",
            color="#2c3e50",
        )
        y_pos -= 0.05

        # Get all entities to highlight
        all_entities = []
        for category, items in entities.items():
            for item in items:
                all_entities.append((item, category))

        # Sort entities by length (longest first to avoid overlap issues)
        all_entities.sort(key=lambda x: len(x[0]), reverse=True)

        # Highlight entities in text
        highlighted_text = text
        for entity, category in all_entities:
            # Escape regex special characters
            entity_escaped = (
                entity.replace("(", r"\(")
                .replace(")", r"\)")
                .replace("[", r"\[")
                .replace("]", r"\]")
            )

            # Find entity in text (word boundary)
            pattern = r"\b" + entity_escaped + r"\b"
            color_code = category_colors.get(category, "black")
            replacement = f"\\textcolor{{{color_code}}}{{{entity}}}"
            highlighted_text = highlighted_text.replace(entity, replacement)

        # Display highlighted text
        ax.text(0.05, y_pos, highlighted_text, va="top", fontsize=10, wrap=True)

        fig.tight_layout(rect=[0, 0.03, 1, 0.97])
        return fig

    except Exception as e:
        logger.error(f"Error plotting report entities: {e}")
        # Create empty figure if error occurs
        fig, ax = plt.subplots(figsize=(8, 6))
        ax.text(0.5, 0.5, f"Error: {str(e)}", ha="center", va="center")
        return fig


def plot_multimodal_results(

    fused_results, image=None, report_text=None, figsize=(12, 10)

):
    """

    Visualize the results of multimodal analysis.



    Args:

        fused_results (dict): Results from multimodal fusion

        image (PIL.Image or str, optional): Image or path to image

        report_text (str, optional): Report text

        figsize (tuple): Figure size



    Returns:

        matplotlib.figure.Figure: The figure object

    """
    try:
        # Create figure with a grid layout
        fig = plt.figure(figsize=figsize)
        gs = fig.add_gridspec(2, 2)

        # Add title
        fig.suptitle(
            "Multimodal Medical Analysis Results",
            fontsize=18,
            fontweight="bold",
            y=0.98,
        )

        # 1. Overview panel (top left)
        ax_overview = fig.add_subplot(gs[0, 0])
        ax_overview.axis("off")

        # Get severity info
        severity = fused_results.get("severity", {})
        severity_level = severity.get("level", "Unknown")
        severity_score = severity.get("score", 0)

        # Get primary finding
        primary_finding = fused_results.get("primary_finding", "Unknown")

        # Get agreement score
        agreement = fused_results.get("agreement_score", 0)

        # Create overview text
        overview_text = [
            "ANALYSIS OVERVIEW",
            f"Primary Finding: {primary_finding}",
            f"Severity Level: {severity_level} ({severity_score}/4)",
            f"Agreement Score: {agreement:.0%}",
        ]

        # Define severity colors
        severity_colors = {
            "Normal": "#2ecc71",  # Green
            "Mild": "#3498db",  # Blue
            "Moderate": "#f39c12",  # Orange
            "Severe": "#e74c3c",  # Red
            "Critical": "#c0392b",  # Dark Red
        }

        # Add overview text to the panel
        y_pos = 0.9
        ax_overview.text(
            0.5,
            y_pos,
            overview_text[0],
            fontsize=14,
            fontweight="bold",
            ha="center",
            va="center",
        )
        y_pos -= 0.15

        ax_overview.text(
            0.1, y_pos, overview_text[1], fontsize=12, ha="left", va="center"
        )
        y_pos -= 0.1

        # Severity with color
        severity_color = severity_colors.get(severity_level, "black")
        ax_overview.text(
            0.1, y_pos, "Severity Level:", fontsize=12, ha="left", va="center"
        )
        ax_overview.text(
            0.4,
            y_pos,
            severity_level,
            fontsize=12,
            color=severity_color,
            fontweight="bold",
            ha="left",
            va="center",
        )
        ax_overview.text(
            0.6, y_pos, f"({severity_score}/4)", fontsize=10, ha="left", va="center"
        )
        y_pos -= 0.1

        # Agreement score with color
        agreement_color = (
            "#2ecc71"
            if agreement > 0.7
            else "#f39c12"
            if agreement > 0.4
            else "#e74c3c"
        )
        ax_overview.text(
            0.1, y_pos, "Agreement Score:", fontsize=12, ha="left", va="center"
        )
        ax_overview.text(
            0.4,
            y_pos,
            f"{agreement:.0%}",
            fontsize=12,
            color=agreement_color,
            fontweight="bold",
            ha="left",
            va="center",
        )

        # 2. Findings panel (top right)
        ax_findings = fig.add_subplot(gs[0, 1])
        ax_findings.axis("off")

        # Get findings
        findings = fused_results.get("findings", [])

        # Add findings to the panel
        y_pos = 0.9
        ax_findings.text(
            0.5,
            y_pos,
            "KEY FINDINGS",
            fontsize=14,
            fontweight="bold",
            ha="center",
            va="center",
        )
        y_pos -= 0.1

        if findings:
            for i, finding in enumerate(findings[:5]):  # Limit to 5 findings
                ax_findings.text(0.05, y_pos, "•", fontsize=14, ha="left", va="center")
                ax_findings.text(
                    0.1, y_pos, finding, fontsize=11, ha="left", va="center", wrap=True
                )
                y_pos -= 0.15
        else:
            ax_findings.text(
                0.1,
                y_pos,
                "No specific findings detailed.",
                fontsize=11,
                ha="left",
                va="center",
            )

        # 3. Image panel (bottom left)
        ax_image = fig.add_subplot(gs[1, 0])

        if image is not None:
            # Load image if path is provided
            if isinstance(image, str):
                img = Image.open(image)
            else:
                img = image

            # Display image
            ax_image.imshow(img)
            ax_image.set_title("X-ray Image", fontsize=12)
        else:
            ax_image.text(0.5, 0.5, "No image available", ha="center", va="center")

        ax_image.axis("off")

        # 4. Recommendation panel (bottom right)
        ax_rec = fig.add_subplot(gs[1, 1])
        ax_rec.axis("off")

        # Get recommendations
        recommendations = fused_results.get("followup_recommendations", [])

        # Add recommendations to the panel
        y_pos = 0.9
        ax_rec.text(
            0.5,
            y_pos,
            "RECOMMENDATIONS",
            fontsize=14,
            fontweight="bold",
            ha="center",
            va="center",
        )
        y_pos -= 0.1

        if recommendations:
            for i, rec in enumerate(recommendations):
                ax_rec.text(0.05, y_pos, "•", fontsize=14, ha="left", va="center")
                ax_rec.text(
                    0.1, y_pos, rec, fontsize=11, ha="left", va="center", wrap=True
                )
                y_pos -= 0.15
        else:
            ax_rec.text(
                0.1,
                y_pos,
                "No specific recommendations provided.",
                fontsize=11,
                ha="left",
                va="center",
            )

        # Add disclaimer
        fig.text(
            0.5,
            0.03,
            "DISCLAIMER: This analysis is for informational purposes only and should not replace professional medical advice.",
            fontsize=9,
            style="italic",
            ha="center",
        )

        fig.tight_layout(rect=[0, 0.05, 1, 0.95])
        return fig

    except Exception as e:
        logger.error(f"Error plotting multimodal results: {e}")
        # Create empty figure if error occurs
        fig, ax = plt.subplots(figsize=(8, 6))
        ax.text(0.5, 0.5, f"Error: {str(e)}", ha="center", va="center")
        return fig


def figure_to_base64(fig):
    """

    Convert matplotlib figure to base64 string.



    Args:

        fig (matplotlib.figure.Figure): Figure object



    Returns:

        str: Base64 encoded string

    """
    try:
        buf = io.BytesIO()
        fig.savefig(buf, format="png", bbox_inches="tight")
        buf.seek(0)
        img_str = base64.b64encode(buf.read()).decode("utf-8")
        return img_str

    except Exception as e:
        logger.error(f"Error converting figure to base64: {e}")
        return ""