Delete utils

utils/visualization.py

@@ -1,152 +0,0 @@
-import io
-import matplotlib.pyplot as plt
-import numpy as np
-from PIL import Image, ImageDraw, ImageFont
-
-
-def draw_findings_on_image(image, findings):
-    """
-    Add annotations to X-ray image based on findings
-
-    Args:
-        image (PIL.Image): Original X-ray image
-        findings (dict): Analysis findings with probabilities
-
-    Returns:
-        PIL.Image: Annotated image
-    """
-    # Create a copy of the image to draw on
-    img = image.copy()
-    draw = ImageDraw.Draw(img)
-
-    # Get image dimensions
-    width, height = img.size
-
-    # Try to use a nice font, fall back to default if not available
-    try:
-        font = ImageFont.truetype("arial.ttf", 20)
-        small_font = ImageFont.truetype("arial.ttf", 16)
-    except IOError:
-        font = ImageFont.load_default()
-        small_font = ImageFont.load_default()
-
-    # Add findings at the top
-    y_position = 10
-    for finding, probability in findings.items():
-        if isinstance(probability, float):
-            text = f"{finding}: {probability:.2f}"
-            # Color code based on probability and finding type
-            if finding == "No findings":
-                color = (0, 128, 0)  # Green for no findings
-            elif probability > 0.5:
-                color = (255, 0, 0)  # Red for high probability issues
-            else:
-                color = (255, 165, 0)  # Orange for lower probability issues
-
-            draw.text((10, y_position), text, fill=color, font=small_font)
-            y_position += 25
-
-    return img
-
-
-def create_combined_visualization(image, image_results, text_results, combined_results):
-    """
-    Create a comprehensive visualization of all analysis results
-
-    Args:
-        image (PIL.Image): Original X-ray image
-        image_results (dict): Image analysis results
-        text_results (dict): Text analysis results
-        combined_results (dict): Combined multimodal results
-
-    Returns:
-        PIL.Image: Visualization image
-    """
-    # Create a copy of the image to draw on
-    img = image.copy()
-
-    # Create a header with the recommendation
-    recommendation = combined_results.get("Recommendation", "No recommendation")
-    confidence = combined_results.get("Confidence", "N/A")
-
-    # Create a white background for the header
-    header_height = 60
-    header_img = Image.new("RGB", (img.width, header_height), color=(255, 255, 255))
-    header_draw = ImageDraw.Draw(header_img)
-
-    # Try to use a nice font, fall back to default if not available
-    try:
-        font = ImageFont.truetype("arial.ttf", 18)
-        small_font = ImageFont.truetype("arial.ttf", 14)
-    except IOError:
-        font = ImageFont.load_default()
-        small_font = ImageFont.load_default()
-
-    # Add recommendation text
-    header_draw.text((10, 5), recommendation, fill=(0, 0, 0), font=font)
-    header_draw.text(
-        (10, 35), f"Confidence: {confidence}", fill=(100, 100, 100), font=small_font
-    )
-
-    # Combine the header and image
-    combined_img = Image.new("RGB", (img.width, img.height + header_height))
-    combined_img.paste(header_img, (0, 0))
-    combined_img.paste(img, (0, header_height))
-
-    return combined_img
-
-
-def generate_report_plot(image_findings, text_findings):
-    """
-    Generate a comparison plot of image and text findings
-
-    Args:
-        image_findings (dict): Image analysis results
-        text_findings (dict): Text analysis results
-
-    Returns:
-        bytes: PNG image data as bytes
-    """
-    # Create figure
-    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
-
-    # Plot image findings
-    image_labels = []
-    image_values = []
-    for k, v in image_findings.items():
-        if isinstance(v, float):
-            image_labels.append(k)
-            image_values.append(v)
-
-    # Sort by value for better visualization
-    sorted_indices = np.argsort(image_values)[::-1]  # Descending order
-    image_labels = [image_labels[i] for i in sorted_indices]
-    image_values = [image_values[i] for i in sorted_indices]
-
-    # Plot bars for image findings
-    ax1.barh(image_labels, image_values, color="skyblue")
-    ax1.set_xlim(0, 1)
-    ax1.set_title("X-ray Analysis")
-    ax1.set_xlabel("Probability")
-
-    # Plot text findings (assuming text_findings has a structure to visualize)
-    ax2.axis("off")  # Turn off axis
-    ax2.text(0.1, 0.9, "Text Analysis Results:", fontweight="bold")
-    y_pos = 0.8
-    for key, value in text_findings.items():
-        if key != "Entities":
-            ax2.text(0.1, y_pos, f"{key}: {value}")
-            y_pos -= 0.1
-
-    # Adjust layout
-    plt.tight_layout()
-
-    # Convert to image bytes
-    buf = io.BytesIO()
-    plt.savefig(buf, format="png")
-    buf.seek(0)
-
-    # Close the plot to avoid memory leaks
-    plt.close(fig)
-
-    return buf.getvalue()