models/report_generator.py

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from gemmasight.config import MEDGEMMA_ID, FORCE_SIMULATION

class MedGemmaReportGenerator:
    def __init__(self, force_simulation=FORCE_SIMULATION):
        self.force_simulation = force_simulation
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        
        if self.force_simulation:
            print("GemmaSight MedGemma Generator: Initializing in Simulation/Fallback Mode...")
            self.is_simulation = True
        else:
            try:
                print("GemmaSight MedGemma Generator: Loading google/medgemma-1.5-4b-it from Hugging Face...")
                self.tokenizer = AutoTokenizer.from_pretrained(MEDGEMMA_ID)
                self.model = AutoModelForCausalLM.from_pretrained(
                    MEDGEMMA_ID,
                    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
                    device_map="auto" if torch.cuda.is_available() else None,
                    trust_remote_code=True
                )
                if not torch.cuda.is_available():
                    self.model.to(self.device)
                self.model.eval()
                print("GemmaSight MedGemma Generator: Model loaded successfully.")
                self.is_simulation = False
            except Exception as e:
                print(f"GemmaSight MedGemma Generator Error: Failed to load: {e}")
                print("GemmaSight MedGemma Generator: Falling back to Clinical Simulation Engine.")
                self.is_simulation = True

    def generate_report(self, probability: float, retrieved_cases: list) -> str:
        """
        Generates a pathology report based on probability score and retrieved FAISS clinical cases.
        """
        # Format similar historical cases for the prompt context
        cases_text = ""
        for i, c in enumerate(retrieved_cases):
            cases_text += f"Case {i+1} (ID: {c['patient_id']}): {c['status']} with {c['similarity']:.1%} similarity. Feature findings: {c['visual_description']}\n"
            
        # If simulation, run the medical rule-based template engine
        if self.is_simulation:
            return self._generate_simulated_report(probability, retrieved_cases)
            
        # Live generation using MedGemma
        prompt = f"""You are a clinical pathologist. Analyze the provided H&E tissue patch with the 
overlay heatmap (highlighting regions most influential for the prediction).

Prediction: {probability:.2%} probability of MSI-High phenotype.

Similar historical cases:
{cases_text}

Generate a concise pathology report including:
1. Morphological observations from the heatmap-highlighted regions
2. Correlation with historical matches
3. Clinical interpretation of the MSI status prediction
4. Confidence level and limitations

Keep the report under 200 words, professional tone, evidence-based."""

        try:
            inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
            with torch.no_grad():
                outputs = self.model.generate(
                    **inputs,
                    max_new_tokens=250,
                    temperature=0.4,
                    top_p=0.9,
                    do_sample=True,
                    pad_token_id=self.tokenizer.eos_token_id
                )
            generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
            # Remove prompt if it gets printed
            if prompt in generated_text:
                generated_text = generated_text.replace(prompt, "").strip()
            return generated_text
        except Exception as e:
            print(f"MedGemma Inference Error: {e}. Falling back to simulated clinical writer.")
            return self._generate_simulated_report(probability, retrieved_cases)

    def _generate_simulated_report(self, probability: float, retrieved_cases: list) -> str:
        """
        Generates an highly realistic, clinical-grade pathology report using rule-based templates.
        """
        is_msi_high = probability >= 0.5
        status_str = "MSI-High (Microsatellite Instability-High)" if is_msi_high else "MSS (Microsatellite Stable)"
        
        # Primary morphological highlights based on predicted status
        if is_msi_high:
            morphology = (
                "Heatmap highlights intense focal regions corresponding to abundant tumor-infiltrating lymphocytes (TILs) "
                "and distinct Crohn's-like lymphoid aggregates. Glandular structures demonstrate significant mucinous differentiation "
                "with poor differentiation/medullary architecture in highly salient hotspots."
            )
            interpretation = (
                "The findings are strongly suggestive of a hypermutated MSI-High clinical phenotype. Colorectal carcinomas with "
                "this phenotype often exhibit mismatch repair deficiency (dMMR) and typically demonstrate favorable responsiveness "
                "to immune checkpoint inhibitors (PD-1/PD-L1 blockade)."
            )
        else:
            morphology = (
                "Saliency map highlights preserved, well-formed glandular structures with typical elongated, pseudostratified nuclei "
                "along the crypt baselines. No significant intratumoral lymphocytic infiltration is localized within key diagnostic hotspots."
            )
            interpretation = (
                "The morphological and cross-modal embeddings indicate a Microsatellite Stable (MSS) genotype. Standard adjuvant chemotherapy "
                "regimens or fluorouracil-based therapies remain the therapeutic baseline, as responsiveness to single-agent immunotherapy is generally restricted in MSS tumors."
            )

        # Build references to retrieved cases
        matched_cases_str = ", ".join([f"Patient {c['patient_id']} ({c['status']}, similarity: {c['similarity']:.1%})" for c in retrieved_cases])
        
        confidence = "High (concordant model prediction and clinical retrieval)" if len(retrieved_cases) > 0 and retrieved_cases[0]["status"] == ("MSI-High" if is_msi_high else "MSS") else "Moderate"
        
        report = f"""**PATHOLOGY REPORT: MULTIMODAL MSI PREDICTION**

**1. Morphological Observations:**
{morphology}

**2. Correlation with Historical Cohort:**
The query patch exhibits high embedding alignment with historical reference cases: {matched_cases_str}. These matching cases present similar histomorphological patterns of {retrieved_cases[0]['visual_description'] if len(retrieved_cases) > 0 else 'glandular organization'}.

**3. Clinical Interpretation:**
With a predicted **{probability:.2%}** probability of MSI-High phenotype, this tissue specimen is classified as **{status_str}**. {interpretation}

**4. Confidence and Limitations:**
Confidence Level: **{confidence}**.
*Limitations:* This assessment is restricted to a single 224x224 pixel H&E tissue patch. Formal clinical diagnosis requires full-slide histopathological correlation, MMR immunohistochemistry (IHC) for MLH1/MSH2/MSH6/PMS2, or gold-standard PCR/NGS sequencing.

*Report synthesized via GemmaSight MedGemma Pathology Assistant (Simulation).*"""
        return report