Upload folder using huggingface_hub (#18)

- Upload folder using huggingface_hub (28069d83f12fea0d67844928ca5b6b040aef854a)


Co-authored-by: nicanor zousko <zousko-stark@users.noreply.huggingface.co>

main.py

@@ -1022,13 +1022,47 @@ class MedSigClipWrapper:
                     
                     if not qc_result['passed']:
                         logger.warning(f"⛔ Quality Control Failed: {qc_result['reasons']}")
-                        # We return early, preserving the basic Classification but flagging the Quality Failure
-                        # ensuring the Frontend shows the error.
+                        # STRICT REJECTION: Override diagnosis and clear predictions
                         enhanced_result['diagnosis'] = "Analyse Refusée (Qualité Insuffisante)"
                         enhanced_result['confidence'] = 0.0
+                        enhanced_result['specific'] = [] # Clear predictions
                         enhanced_result['quality_failure_reasons'] = qc_result['reasons']
-                        # Stop explainability
-                        return localized_result
+                        enhanced_result['image_quality'] = {
+                             "quality_score": qc_result['quality_score'],
+                             "passed": False,
+                             "reasons": qc_result['reasons'],
+                             "metrics": qc_result['metrics']
+                        }
+                        
+                        # FIX: Construct localized_result explicitly as it is not defined yet
+                        rejection_result = {
+                            "domain": {
+                                "label": best_domain_key,
+                                "description": MEDICAL_DOMAINS[best_domain_key]['domain_prompt'],
+                                "probability": round(best_domain_prob, 2)
+                            },
+                            "specific": [],
+                            "heatmap": None,
+                            "original_image": None, # Will be handled by frontend fallback or we can encode it here if mostly wanted
+                            "preprocessing": preprocessing_log,
+                            "explainability": {
+                                "method": "QC Rejection",
+                                "reliability": 0.0
+                            }
+                        }
+                        # We merge enhanced info
+                        rejection_result.update(enhanced_result)
+                        
+                        # Encode Original Image even on Rejection for Context
+                        try:
+                            img_tensor = np.array(image).astype(np.float32) / 255.0
+                            original_uint8 = (img_tensor * 255).astype(np.uint8)
+                            _, buffer_orig = cv2.imencode('.png', cv2.cvtColor(original_uint8, cv2.COLOR_RGB2BGR))
+                            rejection_result["original_image"] = base64.b64encode(buffer_orig).decode('utf-8')
+                        except:
+                            pass
+
+                        return rejection_result
                     
                     # If QC Passed, Proceed to Explanation
                     import explainability