creek

commit b26e95e9d0641d26dd9080239dc05e833a9f2318
parent 0aab94b287b57e9d4498af1b0e19e810e1cb6abf
Author: Nilotpal Gupta <nilotpalgupta0701@gmail.com>
Date:   Thu, 27 Nov 2025 23:09:23 +0530

json schema matchup = model centralization + clenup

Diffstat:

8 files changed, 97 insertions(+), 440 deletions(-)
diff --git a/lib/services/analyze/color.dart b/lib/services/analyze/color.dart
@@ -1,3 +1,5 @@
+// lib/services/color_analyzer_service.dart
+
 import 'package:flutter/foundation.dart';
 import 'python_service.dart';
 
diff --git a/lib/services/analyze/embedding.dart b/lib/services/analyze/embedding.dart
@@ -1,9 +1,11 @@
+// lib/services/embedding_analyzer_service.dart
+
 import 'dart:convert';
 import 'dart:io';
+import 'package:adobe/utils/image_utils.dart';
 import 'package:flutter/foundation.dart';
 import 'package:onnxruntime/onnxruntime.dart';
-import 'package:adobe/utils/image_utils.dart';
-import 'package:adobe/utils/clip_image_processor.dart';
+import '../../utils/clip_image_processor.dart';
 
 class EmbeddingAnalyzerService {
   OrtSession? _session;
@@ -39,9 +41,9 @@ class EmbeddingAnalyzerService {
     String? jsonPath
   }) async {
     // Safety check
-    if (modelPath == null || jsonPath == null) return null;
+    if (modelPath == null || jsonPath == null) return {'success': false, 'scores': {}, 'error': 'Paths missing'};
     await initialize(modelPath: modelPath, jsonPath: jsonPath);
-    if (_session == null || _centroids == null) return null;
+    if (_session == null || _centroids == null) return {'success': false, 'scores': {}, 'error': 'Init failed'};
 
     OrtValueTensor? inputOrt;
     OrtRunOptions? runOptions;
@@ -49,7 +51,7 @@ class EmbeddingAnalyzerService {
 
     try {
       final float32Input = await ClipImageProcessor.preprocess(imagePath);
-      if (float32Input == null) return null;
+      if (float32Input == null) return {'success': false, 'scores': {}, 'error': 'Image decode failed'};
 
       // Create Tensor
       // Note: ensure ClipImageProcessor returns a flat List<double>
@@ -94,19 +96,21 @@ class EmbeddingAnalyzerService {
       }
 
       scores = Map.fromEntries(
-        scores.entries.toList()
-          ..sort((a, b) => b.value.compareTo(a.value))
+        (scores.entries.toList()
+          ..sort((a, b) => b.value
+          .compareTo(a.value))
+        )//.take(3)
       );
 
       return {
         "success": true,
-        "predictions": scores,
-        "best": {scores.keys.first: scores.values.first},
+        "scores": scores,
+        "error": null,
       };
 
     } catch (e) {
       debugPrint("Embeddings Analysis Error: $e");
-      return null;
+      return {'success': false, 'scores': {}, 'error': e.toString()};
     } finally {
       inputOrt?.release();
       runOptions?.release();
diff --git a/lib/services/analyze/emotional_embeddings.dart b/lib/services/analyze/emotional_embeddings.dart
@@ -1,124 +0,0 @@
-import 'dart:convert';
-import 'dart:io';
-import 'package:flutter/foundation.dart';
-import 'package:onnxruntime/onnxruntime.dart';
-import 'package:adobe/utils/image_utils.dart';
-import 'package:adobe/utils/clip_image_processor.dart';
-
-class EmotionalEmbeddingsService {
-  OrtSession? _session;
-  List<List<double>>? _centroids;
-  List<String>? _classes;
-
-  Future<void> initialize({required String modelPath, required String jsonPath}) async {
-    if (_session != null) return;
-
-    try {
-      debugPrint("Initializing Embeddings (Dino/CLIP)...");
-      
-      OrtEnv.instance.init();
-
-      // 1. Load Model from passed PATH (No rootBundle!)
-      final sessionOptions = OrtSessionOptions();
-      _session = OrtSession.fromFile(File(modelPath), sessionOptions);
-      sessionOptions.release();
-
-      final jsonString = await File(jsonPath).readAsString();
-      final jsonData = json.decode(jsonString);
-
-      _classes = List<String>.from(jsonData['classes']);
-      _centroids =
-          (jsonData['centroids'] as List)
-              .map((e) => List<double>.from(e))
-              .toList();
-    } catch (e) {
-      debugPrint("Embeddings Init Error: $e");
-    }
-  }
-
-  Future<Map<String, dynamic>?> analyze(String imagePath, {
-    String? modelPath, 
-    String? jsonPath
-  }) async {
-    // Safety check
-    if (modelPath == null || jsonPath == null) return null;
-    await initialize(modelPath: modelPath, jsonPath: jsonPath);
-    if (_session == null || _centroids == null) return null;
-
-    OrtValueTensor? inputOrt;
-    OrtRunOptions? runOptions;
-    List<OrtValue?>? outputs;
-
-    try {
-      final float32Input = await ClipImageProcessor.preprocess(imagePath);
-      //debugPrint("[EMOTION] Preprocess length: ${float32Input?.length}");
-      if (float32Input == null) return null;
-
-      // Create Tensor
-      // Note: ensure ClipImageProcessor returns a flat List<double>
-      inputOrt = OrtValueTensor.createTensorWithDataList(
-          float32Input, [1, 3, 224, 224]);
-
-      runOptions = OrtRunOptions();
-
-      // Run Inference
-      // 'image' is the input name for CLIP.
-      outputs = _session!.run(runOptions, {"image": inputOrt});
-      //debugPrint("[EMOTION] Output keys: ${outputs.keys}");
-
-      if (outputs.isEmpty) throw Exception("No output from model");
-
-      // Get Output
-      final dynamic outputRaw = outputs[0]?.value;
-
-      // Flatten Output
-      final List<double> imgFeat = [];
-
-      void flatten(dynamic data) {
-        if (data is num) {
-          imgFeat.add(data.toDouble());
-        } else if (data is List) {
-          for (var item in data) {
-            flatten(item);
-          }
-        }
-      }
-
-      flatten(outputRaw);
-
-      // debugPrint("[EMOTION] Embedding length: ${imgFeat.length}");
-      // debugPrint("[EMOTION] Centroid length: ${_centroids?[0].length}");
-
-      // Normalize & Compare
-      final normFeat = l2Normalize(imgFeat);
-      Map<String, dynamic> scores = {};
-
-      for (int i = 0; i < _classes!.length; i++) {
-        double score = dotProduct(normFeat, _centroids![i]);
-        scores[_classes![i]] = score * 100.0;
-      }
-
-      scores = Map.fromEntries(
-        scores.entries.toList()
-          ..sort((a, b) => b.value.compareTo(a.value))
-      );
-
-      return {
-        "success": true,
-        "predictions": scores,
-        "best": {scores.keys.first: scores.values.first},
-      };
-    } catch (e) {
-      debugPrint("Embeddings Analysis Error: $e");
-      return null;
-    } finally {
-      inputOrt?.release();
-      runOptions?.release();
-      outputs?.forEach((element) => element?.release());
-    }
-  }
-
-  void dispose() {
-    _session?.release();
-  }
-}
diff --git a/lib/services/analyze/era.dart b/lib/services/analyze/era.dart
@@ -1,120 +0,0 @@
-import 'dart:convert';
-import 'dart:io';
-import 'package:flutter/foundation.dart';
-import 'package:onnxruntime/onnxruntime.dart';
-import 'package:adobe/utils/image_utils.dart';
-import 'package:adobe/utils/clip_image_processor.dart';
-
-class EraEmbeddingsService {
-  OrtSession? _session;
-  List<List<double>>? _centroids;
-  List<String>? _classes;
-
-  Future<void> initialize({required String modelPath, required String jsonPath}) async {
-    if (_session != null) return;
-
-    try {
-      debugPrint("Initializing Embeddings (Dino/CLIP)...");
-      
-      OrtEnv.instance.init();
-
-      // 1. Load Model from passed PATH (No rootBundle!)
-      final sessionOptions = OrtSessionOptions();
-      _session = OrtSession.fromFile(File(modelPath), sessionOptions);
-      sessionOptions.release();
-
-      final jsonStr = await File(jsonPath).readAsString();
-      final jsonData = json.decode(jsonStr);
-
-      _classes = List<String>.from(jsonData['classes']);
-      _centroids =
-          (jsonData['centroids'] as List)
-              .map((e) => List<double>.from(e))
-              .toList();
-    } catch (e) {
-      debugPrint("Embeddings Init Error: $e");
-    }
-  }
-
-  Future<Map<String, dynamic>?> analyze(String imagePath, {
-    String? modelPath,
-    String? jsonPath,
-  }) async {
-    // Safety check
-    if (modelPath == null || jsonPath == null) return null;
-    await initialize(modelPath: modelPath, jsonPath: jsonPath);
-    if (_session == null || _centroids == null) return null;
-
-    OrtValueTensor? inputOrt;
-    OrtRunOptions? runOptions;
-    List<OrtValue?>? outputs;
-
-    try {
-      final float32Input = await ClipImageProcessor.preprocess(imagePath);
-      if (float32Input == null) return null;
-
-      // Create Tensor
-      // Note: ensure ClipImageProcessor returns a flat List<double>
-      inputOrt = OrtValueTensor.createTensorWithDataList(
-          float32Input, [1, 3, 224, 224]);
-
-      runOptions = OrtRunOptions();
-
-      // Run Inference
-      // 'image' is the input name for CLIP.
-      outputs = _session!.run(runOptions, {"image": inputOrt}); // <--- UPDATED
-      //debugPrint("[LIGHTING] Output keys: ${outputs.keys}");
-
-      if (outputs.isEmpty) throw Exception("No output from model");
-
-      // Get Output
-      final dynamic outputRaw = outputs[0]?.value;
-
-      // Flatten Output
-      final List<double> imgFeat = [];
-
-      void flatten(dynamic data) {
-        if (data is num) {
-          imgFeat.add(data.toDouble());
-        } else if (data is List) {
-          for (var item in data) {
-            flatten(item);
-          }
-        }
-      }
-
-      flatten(outputRaw);
-
-      // Normalize & Compare
-      final normFeat = l2Normalize(imgFeat);
-      Map<String, dynamic> scores = {};
-
-      for (int i = 0; i < _classes!.length; i++) {
-        double score = dotProduct(normFeat, _centroids![i]);
-        scores[_classes![i]] = score * 100.0;
-      }
-
-      scores = Map.fromEntries(
-        scores.entries.toList()
-          ..sort((a, b) => b.value.compareTo(a.value)),
-      );
-
-      return {
-        "success": true,
-        "predictions": scores,
-        "best": {scores.keys.first: scores.values.first},
-      };
-    } catch (e) {
-      debugPrint("Embeddings Analysis Error: $e");
-      return null;
-    } finally {
-      inputOrt?.release();
-      runOptions?.release();
-      outputs?.forEach((element) => element?.release());
-    }
-  }
-
-  void dispose() {
-    _session?.release();
-  }
-}
diff --git a/lib/services/analyze/image_analyzer.dart b/lib/services/analyze/image_analyzer.dart
@@ -1,3 +1,5 @@
+// lib/services/image_analyzer_service.dart
+
 import 'dart:async';
 import 'dart:io';
 import 'dart:isolate';
@@ -6,15 +8,13 @@ import 'dart:developer' as dev;
 import 'package:flutter/foundation.dart';
 import 'package:flutter/services.dart';
 import 'package:path_provider/path_provider.dart';
+import 'package:path/path.dart' as p;
 
 // Import analysis services
-import 'layout.dart';
 import 'color.dart';
 import 'texture.dart';
 import 'embedding.dart';
-import 'emotional_embeddings.dart';
-import 'lighting.dart';
-import 'era.dart';
+import 'layout.dart';
 
 class ImageAnalyzerService {
 
@@ -26,7 +26,6 @@ class ImageAnalyzerService {
     Future<String> copy(String assetName, {String? filename}) async {
       final name = filename ?? assetName;
       final file = File('${dir.path}/$name');
-      // Only copy if it doesn't exist or is empty
       if (!await file.exists() || await file.length() == 0) {
         final data = await rootBundle.load('assets/$assetName');
         await file.writeAsBytes(data.buffer.asUint8List(), flush: true);
@@ -35,14 +34,13 @@ class ImageAnalyzerService {
     }
 
     // Copy everything needed by the services
-    // Make sure your assets/ folder actually contains these files!
     final paths = await Future.wait([
-      // Texture
+      // DINO
       copy('dinov2.onnx'), 
       copy('dinov2.onnx.data'),
       copy('texture_centroids.json'),
       
-      // CLIP (Embeddings/Emotion/Lighting)
+      // CLIP
       copy('clip_image_encoder.onnx'),
       copy('style_embeddings.json'),
       copy('emotion_centroids.json'),
@@ -67,7 +65,6 @@ class ImageAnalyzerService {
     required String imagePath,
     required RootIsolateToken rootToken,
     required bool runInIsolate,
-    // Pass config map to the task
     required Map<String, String> assetPaths,
     required Future<dynamic> Function(String, Map<String, String>) task,
   }) async {
@@ -92,16 +89,20 @@ class ImageAnalyzerService {
       
       stopwatch.stop();
       timelineTask.finish();
+      result['execution_time']= stopwatch.elapsedMilliseconds;
+      return result;
 
-      return {
-        'data': result,
-        'stats': {'execution_time_ms': stopwatch.elapsedMilliseconds}
-      };
     } catch (e) {
       stopwatch.stop();
       timelineTask.finish();
       debugPrint("[$name] Failed: $e");
-      return {'error': e.toString()};
+
+      return {
+        'success': false,
+        'scores': {},
+        'error': e.toString(),
+        'execution_time': stopwatch.elapsedMilliseconds,
+      };
     }
   }
 
@@ -169,7 +170,7 @@ class ImageAnalyzerService {
           name: 'Emotional', imagePath: imagePath, rootToken: token, runInIsolate: true, 
           assetPaths: assetPaths,
           task: (path, assets) async {
-             final service = EmotionalEmbeddingsService();
+             final service = EmbeddingAnalyzerService();
              final res = await service.analyze(path,
                 modelPath: assets['clip_model'],
                 jsonPath: assets['emotion_json']
@@ -183,7 +184,7 @@ class ImageAnalyzerService {
           name: 'Lighting', imagePath: imagePath, rootToken: token, runInIsolate: true, 
           assetPaths: assetPaths,
           task: (path, assets) async {
-             final service = LightingEmbeddingsService();
+             final service = EmbeddingAnalyzerService();
              final res = await service.analyze(path,
                 modelPath: assets['clip_model'],
                 jsonPath: assets['lighting_json']
@@ -197,7 +198,7 @@ class ImageAnalyzerService {
           name: 'Era', imagePath: imagePath, rootToken: token, runInIsolate: true, 
           assetPaths: assetPaths,
           task: (path, assets) async {
-             final service = EraEmbeddingsService();
+             final service = EmbeddingAnalyzerService();
              final res = await service.analyze(path,
                 modelPath: assets['clip_model'],
                 jsonPath: assets['era_json']
@@ -210,61 +211,78 @@ class ImageAnalyzerService {
 
       totalSw.stop();
 
-      final finalResult = {
+      // This is only for testing
+      final logResult = {
         'success': true,
-        'total_time_ms': totalSw.elapsedMilliseconds,
-        'timestamp': DateTime.now().toIso8601String(),
+        'total_time': totalSw.elapsedMilliseconds,
+        'filename': p.basename(imagePath),
         'results': {
-          'layout': results[0],
-          'color': results[1],
-          'texture': results[2],
-          'embedding': results[3],
-          'emotions': results[4],
-          'lighting': results[5],
-          'era': results[6],
+          'Layout': results[0]['execution_time'],
+          'Color': results[1]['execution_time'],
+          'Texture': results[2]['execution_time'],
+          'Style': results[3]['execution_time'],
+          'Emotions': results[4]['execution_time'],
+          'Lighting': results[5]['execution_time'],
+          'Era': results[6]['execution_time'],
         }
       };
+      _logSummary(logResult);
 
-      _logSummary(finalResult);
-      print("\n\n\n layout: ${results[0]} \n\n\n");
-      print("color: ${results[1]} \n\n\n");
-      print("texture: ${results[2]} \n\n\n");
-      print("embedding: ${results[3]} \n\n\n");
-      print("emotions: ${results[4]} \n\n\n");
-      print("lighting: ${results[5]} \n\n\n");
-      print("era: ${results[6]} \n\n\n");
+      final finalResult = {
+        'success': true,
+        'data': {
+          'filename': p.basename(imagePath),
+          'results': {
+            'Style': {"scores": results[3]['scores']},
+            'Texture': {"scores": results[2]['scores']},
+            'Lighting': {"scores": results[5]['scores']},
+            'Colour Pallete': {"scores": results[1]['scores']},
+            'Emotions': {"scores": results[4]['scores']},
+            'Era': {"scores": results[6]['scores']},
+            'Layout': {"scores": results[0]['scores']}
+          }
+        },
+        'error': null,
+      };
+      debugPrint(finalResult.toString());
       return finalResult;
 
     } catch (e) {
       debugPrint("Master Analysis Failed: $e");
-      return {'success': false, 'error': e.toString()};
+      return {
+        'success': false,
+        'data': {
+          'filename': '',
+          'results': {
+            'Style': {"scores": {}},
+            'Texture': {"scores": {}},
+            'Lighting': {"scores": {}},
+            'Colour Pallete': {"scores": {}},
+            'Emotions': {"scores": {}},
+            'Era': {"scores": {}},
+          }
+        },
+        'error': e.toString()
+      };
     }
   }
 
   static void _logSummary(Map<String, dynamic> result) {
-    final int total = result['total_time_ms'];
+    final int total = result['total_time'];
     final Map<String, dynamic> subTasks = result['results'];
-
     StringBuffer output = StringBuffer();
+
     output.writeln('\n══════════ ⚡ HYBRID PARALLEL REPORT ⚡ ══════════');
     output.writeln(' 🕒 Total Wall Time: ${total}ms');
     output.writeln('──────────────────────────────────────────────────────');
     
     subTasks.forEach((key, value) {
-      if (value is Map && value.containsKey('stats')) {
-        final int time = value['stats']['execution_time_ms'];
-        final int barLength = (time / 50).ceil().clamp(0, 20);
-        final String bar = '█' * barLength;
-        
-        output.writeln(' ${key.padRight(12)} : ${time.toString().padLeft(4)}ms $bar');
-      } else {
-        String err = value['error'] ?? "Unknown";
-        if (err.length > 30) err = "${err.substring(0, 30)}...";
-        output.writeln(' ${key.padRight(12)} : FAILED ❌ ($err)');
-      }
+      final int barLength = (value / 50).ceil().clamp(0, 20);
+      final String bar = '█' * barLength;
+      output.writeln(' ${key.padRight(12)} : ${value.toString().padLeft(4)}ms $bar');
     });
-    output.writeln('══════════════════════════════════════════════════════\n');
 
+    output.writeln('══════════════════════════════════════════════════════\n');
     dev.log(output.toString(), name: 'ImageAnalyzer');
   }
 }
diff --git a/lib/services/analyze/layout.dart b/lib/services/analyze/layout.dart
@@ -1,3 +1,5 @@
+// lib/services/layout_analyzer_service.dart
+
 import 'package:flutter/foundation.dart';
 import 'python_service.dart';
 
diff --git a/lib/services/analyze/lighting.dart b/lib/services/analyze/lighting.dart
@@ -1,120 +0,0 @@
-import 'dart:convert';
-import 'dart:io';
-import 'package:flutter/foundation.dart';
-import 'package:onnxruntime/onnxruntime.dart';
-import 'package:adobe/utils/image_utils.dart';
-import 'package:adobe/utils/clip_image_processor.dart';
-
-class LightingEmbeddingsService {
-  OrtSession? _session;
-  List<List<double>>? _centroids;
-  List<String>? _classes;
-
-  Future<void> initialize({required String modelPath, required String jsonPath}) async {
-    if (_session != null) return;
-
-    try {
-      debugPrint("Initializing Embeddings (Dino/CLIP)...");
-      
-      OrtEnv.instance.init();
-
-      // 1. Load Model from passed PATH (No rootBundle!)
-      final sessionOptions = OrtSessionOptions();
-      _session = OrtSession.fromFile(File(modelPath), sessionOptions);
-      sessionOptions.release();
-
-      final jsonStr = await File(jsonPath).readAsString();
-      final jsonData = json.decode(jsonStr);
-
-      _classes = List<String>.from(jsonData['classes']);
-      _centroids =
-          (jsonData['centroids'] as List)
-              .map((e) => List<double>.from(e))
-              .toList();
-    } catch (e) {
-      debugPrint("Embeddings Init Error: $e");
-    }
-  }
-
-  Future<Map<String, dynamic>?> analyze(String imagePath, {
-    String? modelPath,
-    String? jsonPath,
-  }) async {
-    // Safety check
-    if (modelPath == null || jsonPath == null) return null;
-    await initialize(modelPath: modelPath, jsonPath: jsonPath);
-    if (_session == null || _centroids == null) return null;
-
-    OrtValueTensor? inputOrt;
-    OrtRunOptions? runOptions;
-    List<OrtValue?>? outputs;
-
-    try {
-      final float32Input = await ClipImageProcessor.preprocess(imagePath);
-      if (float32Input == null) return null;
-
-      // Create Tensor
-      // Note: ensure ClipImageProcessor returns a flat List<double>
-      inputOrt = OrtValueTensor.createTensorWithDataList(
-          float32Input, [1, 3, 224, 224]);
-
-      runOptions = OrtRunOptions();
-
-      // Run Inference
-      // 'image' is the input name for CLIP.
-      outputs = _session!.run(runOptions, {"image": inputOrt}); // <--- UPDATED
-      //debugPrint("[LIGHTING] Output keys: ${outputs.keys}");
-
-      if (outputs.isEmpty) throw Exception("No output from model");
-
-      // Get Output
-      final dynamic outputRaw = outputs[0]?.value;
-
-      // Flatten Output
-      final List<double> imgFeat = [];
-
-      void flatten(dynamic data) {
-        if (data is num) {
-          imgFeat.add(data.toDouble());
-        } else if (data is List) {
-          for (var item in data) {
-            flatten(item);
-          }
-        }
-      }
-
-      flatten(outputRaw);
-
-      // Normalize & Compare
-      final normFeat = l2Normalize(imgFeat);
-      Map<String, dynamic> scores = {};
-
-      for (int i = 0; i < _classes!.length; i++) {
-        double score = dotProduct(normFeat, _centroids![i]);
-        scores[_classes![i]] = score * 100.0;
-      }
-
-      scores = Map.fromEntries(
-        scores.entries.toList()
-          ..sort((a, b) => b.value.compareTo(a.value)),
-      );
-
-      return {
-        "success": true,
-        "predictions": scores,
-        "best": {scores.keys.first: scores.values.first},
-      };
-    } catch (e) {
-      debugPrint("Embeddings Analysis Error: $e");
-      return null;
-    } finally {
-      inputOrt?.release();
-      runOptions?.release();
-      outputs?.forEach((element) => element?.release());
-    }
-  }
-
-  void dispose() {
-    _session?.release();
-  }
-}
diff --git a/lib/services/analyze/texture.dart b/lib/services/analyze/texture.dart
@@ -1,9 +1,11 @@
+// lib/services/texture_analyzer_service.dart
+
 import 'dart:io';
 import 'dart:convert';
 import 'package:flutter/foundation.dart';
 import 'package:image/image.dart' as img;
 import 'package:onnxruntime/onnxruntime.dart';
-import 'package:adobe/utils/image_utils.dart';
+import '../../utils/image_utils.dart';
 
 class TextureAnalyzerService {
   OrtSession? _session;
@@ -46,9 +48,9 @@ class TextureAnalyzerService {
     String? jsonPath
   }) async {
     // Safety check
-    if (modelPath == null || jsonPath == null) return {'success': false, 'error': 'Paths missing'};
+    if (modelPath == null || jsonPath == null) return {'success': false, 'scores': {}, 'error': 'Paths missing'};
     await initialize(modelPath: modelPath, jsonPath: jsonPath);
-    if (_session == null || _centroids == null) return {'success': false, 'error': 'Init failed'};
+    if (_session == null || _centroids == null) return {'success': false, 'scores': {}, 'error': 'Init failed'};
 
     OrtValueTensor? inputOrt;
     OrtRunOptions? runOptions;
@@ -58,7 +60,7 @@ class TextureAnalyzerService {
       // 1. Preprocess
       final bytes = await File(path).readAsBytes();
       final image = img.decodeImage(bytes);
-      if (image == null) return {'success': false, 'error': 'Image decode failed'};
+      if (image == null) return {'success': false, 'scores': {}, 'error': 'Image decode failed'};
 
       final resized = img.copyResize(image, width: INPUT_SIZE, height: INPUT_SIZE);
 
@@ -125,27 +127,20 @@ class TextureAnalyzerService {
       var sorted = rawScores.entries.toList()..sort((a, b) => b.value.compareTo(a.value));
       Map<String, dynamic> finalResults = {};
 
-      if (sorted.isNotEmpty) {
-        double topScore = sorted.first.value;
-        for (var entry in sorted) {
-          if (finalResults.length >= MAX_COUNT) break;
-          if (entry.value < ABSOLUTE_FLOOR) continue;
-          if (entry.value < (topScore * RELATIVE_THRESH)) break;
-          
-          finalResults[entry.key] = entry.value;
-        }
+      for(var entry in sorted) { //.take(3)
+        finalResults[entry.key] = entry.value;
       }
 
       // Return Map consistent with other services
       return {
         'success': true,
-        'predictions': finalResults, 
-        'best': {finalResults.keys.first: finalResults.values.first},
+        'scores': finalResults,
+        'error': null,
       };
 
     } catch (e) {
       debugPrint("Texture Analysis Failed: $e");
-      return {'success': false, 'error': e.toString()};
+      return {'success': false, 'scores': {}, 'error': e.toString()};
     } finally {
       // FFI Requires manual release of C++ resources
       inputOrt?.release();