MultiModalRLMAnalyzer.py

# MultiModalRLMAnalyzer.py - Core analysis framework
import torch
import numpy as np
from typing import List, Dict, Tuple
from sentence_transformers import SentenceTransformer
from PIL import Image
import requests
from io import BytesIO


class MultiModalFailureAnalyzer:
    """Analyze failure modes specific to multi-modal RLMs"""

    def __init__(self):
        # Load text embedding model for semantic analysis
        self.text_model = SentenceTransformer('all-MiniLM-L6-v2')

    def analyze_cross_modal_drift(self, text_outputs: List[str],
                                  image_descriptions: List[str]) -> Dict:
        """Detect drift between text and image modalities"""
        drift_metrics = {
            'semantic_alignment': [],
            'temporal_consistency': [],
            'modality_gap': []
        }

        for i in range(1, len(text_outputs)):
            # Text similarity
            text_embeds = self.text_model.encode([text_outputs[i - 1], text_outputs[i]])
            text_sim = np.dot(text_embeds[0], text_embeds[1]) / (
                np.linalg.norm(text_embeds[0]) * np.linalg.norm(text_embeds[1])
            )

            # Image description similarity
            if i < len(image_descriptions):
                img_embeds = self.text_model.encode([
                    image_descriptions[i - 1], image_descriptions[i]
                ])
                img_sim = np.dot(img_embeds[0], img_embeds[1]) / (
                    np.linalg.norm(img_embeds[0]) * np.linalg.norm(img_embeds[1])
                )

                # Cross-modal alignment
                alignment = abs(text_sim - img_sim)
                drift_metrics['semantic_alignment'].append(alignment)
                drift_metrics['modality_gap'].append(abs(text_sim - img_sim))

        return drift_metrics

    def identify_failure_patterns(self, multimodal_outputs: List[Dict]) -> Dict:
        """Identify specific multi-modal failure patterns"""
        failure_patterns = {
            'cross_modal_inconsistency': 0,
            'visual_reasoning_breakdown': 0,
            'text_image_misalignment': 0,
            'modality_dominance': 0
        }

        for output in multimodal_outputs:
            # Check for visual-text inconsistencies
            if 'image_caption' in output and 'text_response' in output:
                # Simple keyword mismatch detection
                text_keywords = set(output['text_response'].lower().split())
                image_keywords = set(output['image_caption'].lower().split())

                if len(text_keywords & image_keywords) / max(1, len(text_keywords | image_keywords)) < 0.3:
                    failure_patterns['cross_modal_inconsistency'] += 1

                # Check if one modality dominates
                if len(output['text_response']) > 5 * len(output['image_caption']):
                    failure_patterns['modality_dominance'] += 1

        return failure_patterns


class BasicMultiModalRLM:
    """Basic multi-modal recursive language model (requires BLIP-2 download)"""

    def __init__(self, model_name="Salesforce/blip2-flan-t5-xl"):
        from transformers import Blip2Processor, Blip2ForConditionalGeneration
        self.processor = Blip2Processor.from_pretrained(model_name)
        self.model = Blip2ForConditionalGeneration.from_pretrained(model_name)
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.model.to(self.device)

    def generate_response(self, image, prompt: str, max_length: int = 100) -> Dict:
        """Generate both text and image understanding"""
        inputs = self.processor(images=image, text=prompt, return_tensors="pt").to(self.device)

        with torch.no_grad():
            generated_ids = self.model.generate(**inputs, max_length=max_length)

        generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

        # Also get image understanding
        image_inputs = self.processor(images=image, return_tensors="pt").to(self.device)
        with torch.no_grad():
            image_caption_ids = self.model.generate(**image_inputs, max_length=50)
        image_caption = self.processor.batch_decode(image_caption_ids, skip_special_tokens=True)[0]

        return {
            'text_response': generated_text,
            'image_caption': image_caption,
            'combined_output': f"Text: {generated_text} | Image: {image_caption}"
        }


class MultiModalBenchmark:
    """Benchmark system for multi-modal RLM evaluation"""

    def __init__(self):
        self.test_cases = [
            {
                'name': 'Visual Question Answering',
                'task': 'Answer questions about images',
                'prompts': [
                    "What objects are in this image?",
                    "Describe the scene in detail.",
                    "What is the main subject?"
                ]
            },
            {
                'name': 'Image Caption Enhancement',
                'task': 'Improve image captions iteratively',
                'prompts': [
                    "Make this caption more descriptive:",
                    "Add more details to this description:",
                    "Enhance the storytelling aspect:"
                ]
            },
            {
                'name': 'Visual Reasoning Chain',
                'task': 'Solve problems using both text and images',
                'prompts': [
                    "Based on this diagram, explain the process:",
                    "Analyze this chart and describe trends:",
                    "Interpret this infographic step by step:"
                ]
            }
        ]

    def load_test_image(self, url: str) -> Image.Image:
        """Load test image from URL"""
        response = requests.get(url)
        return Image.open(BytesIO(response.content))


# ── Phase 1 driver ──────────────────────────────────────────────────────────
def phase1_initial_analysis():
    """Phase 1: Initial multi-modal analysis"""
    print("=" * 60)
    print("PHASE 1: MULTI-MODAL CONVERGENCE ANALYSIS")
    print("=" * 60)

    # Initialize analyzer
    analyzer = MultiModalFailureAnalyzer()
    benchmark = MultiModalBenchmark()

    # Test with sample data
    sample_text_outputs = [
        "The image shows a red apple on a wooden table.",
        "The image depicts a red apple sitting on a brown wooden surface.",
        "A crimson apple rests upon a rustic wooden tabletop in the image."
    ]

    sample_image_descriptions = [
        "red apple wooden table",
        "apple on table brown wood",
        "crimson fruit wooden surface"
    ]

    # Analyze cross-modal drift
    drift_analysis = analyzer.analyze_cross_modal_drift(
        sample_text_outputs, sample_image_descriptions
    )

    print("\nCross-Modal Drift Analysis:")
    print(f"  Average Semantic Alignment: {np.mean(drift_analysis['semantic_alignment']):.3f}")
    print(f"  Average Modality Gap: {np.mean(drift_analysis['modality_gap']):.3f}")

    # Identify failure patterns
    sample_multimodal_outputs = [
        {
            'text_response': "The image shows a beautiful sunset over mountains.",
            'image_caption': "mountains landscape nature"
        },
        {
            'text_response': "This depicts a serene lake reflecting clouds in the sky.",
            'image_caption': "water body peaceful scenery"
        }
    ]

    failure_patterns = analyzer.identify_failure_patterns(sample_multimodal_outputs)
    print("\nFailure Pattern Analysis:")
    for pattern, count in failure_patterns.items():
        print(f"  {pattern}: {count}")

    return drift_analysis, failure_patterns


if __name__ == "__main__":
    phase1_initial_analysis()