models/multimodal_fusion.py

class MultimodalFusion:
    """
    Combines insights from image analysis and text analysis
    to provide comprehensive medical assessment
    """

    def __init__(self):
        pass

    def fuse_insights(self, image_results, text_results):
        """
        Fuse insights from image and text analysis

        Args:
            image_results (dict): Results from image analysis
            text_results (dict): Results from text analysis

        Returns:
            dict: Combined insights with recommendation
        """
        # In a real implementation, this would use more sophisticated fusion techniques
        combined_insights = {
            "Image findings": image_results,
            "Text findings": text_results,
        }

        # Simple fusion logic
        confidence_scores = [
            value for key, value in image_results.items() if isinstance(value, float)
        ]
        avg_confidence = (
            sum(confidence_scores) / len(confidence_scores) if confidence_scores else 0
        )

        # Determine if any abnormalities are detected in image
        image_abnormal = any(
            key != "No findings" and value > 0.5
            for key, value in image_results.items()
            if isinstance(value, float)
        )

        # Check if text analysis found concerning elements
        text_concerning = text_results.get("Sentiment") == "Concerning"

        # Generate recommendation
        if image_abnormal and text_concerning:
            recommendation = "High priority: Both image and text indicate abnormalities"
        elif image_abnormal:
            recommendation = "Medium priority: Image shows potential abnormalities"
        elif text_concerning:
            recommendation = "Medium priority: Text report indicates concerns"
        else:
            recommendation = "Low priority: No significant findings detected"

        combined_insights["Recommendation"] = recommendation
        combined_insights["Confidence"] = f"{avg_confidence:.2f}"

        return combined_insights