fix: apply Black formatting and fix backend Dockerfile paths

Author: kamini08

.gitignore

@@ -12,7 +12,7 @@ coverage.svg
 coverage/
 frontend/node_modules/
 frontend/coverage/
-frontend/tsconfig.tsbuildinfo
+**/tsconfig.tsbuildinfo
 backend/coverage/
 htmlcov/
 .pytest_cache/

docker-compose.yml

@@ -21,7 +21,7 @@ services:
   edgeflow-api:
     build:
       context: .
-      dockerfile: backend/Dockerfile
+      dockerfile: src/edgeflow/backend/Dockerfile
     image: edgeflow-api:latest
     container_name: edgeflow-api
     ports:
@@ -45,7 +45,7 @@ services:
 
   edgeflow-frontend:
     build:
-      context: ./frontend
+      context: ./src/edgeflow/frontend
       dockerfile: Dockerfile
     image: edgeflow-frontend:latest
     container_name: edgeflow-frontend

src/edgeflow/analysis/initial_check.py

@@ -100,7 +100,9 @@ def check_compatibility(
         """Check if a model is compatible with target device."""
         profile = self.profile_model(model_path)
         device = self.spec_manager.get_device_spec(target_device or "generic")
-        print(f"DEBUG: target={target_device}, device={device.name}, ram={device.ram_mb}, max_model={device.max_model_size_mb}")
+        print(
+            f"DEBUG: target={target_device}, device={device.name}, ram={device.ram_mb}, max_model={device.max_model_size_mb}"
+        )
         print(f"DEBUG: profile.size={profile.file_size_mb}")
 
         # Use config memory_limit if specified, otherwise use device spec

src/edgeflow/backend/Dockerfile

@@ -6,21 +6,20 @@ WORKDIR /app
 RUN apt-get update && apt-get install -y curl && rm -rf /var/lib/apt/lists/*
 
 # Install backend runtime deps
-COPY backend/requirements.txt /app/requirements.txt
+COPY src/edgeflow/backend/requirements.txt /app/requirements.txt
 RUN pip install --no-cache-dir -r /app/requirements.txt
 
 # Copy backend app and core modules used by the API
-COPY backend /app/backend
-COPY edgeflowc.py /app/edgeflowc.py
-COPY parser /app/parser
-COPY parser.py /app/parser.py
-COPY optimizer.py /app/optimizer.py
-COPY edgeflow_ast.py /app/edgeflow_ast.py
-COPY code_generator.py /app/code_generator.py
-COPY benchmarker.py /app/benchmarker.py
-COPY reporter.py /app/reporter.py
-COPY device_specs.py /app/device_specs.py
-COPY initial_check.py /app/initial_check.py
+COPY src/edgeflow/backend /app/backend
+COPY src/edgeflow/compiler/edgeflowc.py /app/edgeflowc.py
+COPY src/edgeflow/parser /app/parser
+COPY src/edgeflow/optimization/optimizer.py /app/optimizer.py
+COPY src/edgeflow/ir/edgeflow_ast.py /app/edgeflow_ast.py
+COPY src/edgeflow/compiler/code_generator.py /app/code_generator.py
+COPY src/edgeflow/benchmarking/benchmarker.py /app/benchmarker.py
+COPY src/edgeflow/reporting/reporter.py /app/reporter.py
+COPY src/edgeflow/config/device_specs.py /app/device_specs.py
+COPY src/edgeflow/analysis/initial_check.py /app/initial_check.py
 
 EXPOSE 8000
 

src/edgeflow/compiler/edgeflowc.py

@@ -20,6 +20,7 @@
 import logging
 import os
 import sys
+
 try:
     from edgeflow.parser import parse_edgeflow_file as _parse_edgeflow_file
     from edgeflow.parser import parse_edgeflow_file as parse_ef
@@ -46,7 +47,13 @@
 )
 from edgeflow.compiler.code_generator import CodeGenerator, generate_code
 from edgeflow.ir.edgeflow_ast import create_program_from_dict
-from edgeflow.ir.edgeflow_ir import FusionPass, IRBuilder, IRGraph, QuantizationPass, SchedulingPass
+from edgeflow.ir.edgeflow_ir import (
+    FusionPass,
+    IRBuilder,
+    IRGraph,
+    QuantizationPass,
+    SchedulingPass,
+)
 from edgeflow.reporting.explainability_reporter import generate_explainability_report
 from edgeflow.optimization.fast_compile import FastCompileResult, fast_compile_config
 from edgeflow.reporting.reporter import generate_report
@@ -58,8 +65,14 @@
 
 # Import new comprehensive pipeline components
 try:
-    from edgeflow.deployment.deployment_orchestrator import CrossPlatformDeployer, DeploymentTarget
-    from edgeflow.config.dynamic_device_profiles import get_device_profile, get_profile_manager
+    from edgeflow.deployment.deployment_orchestrator import (
+        CrossPlatformDeployer,
+        DeploymentTarget,
+    )
+    from edgeflow.config.dynamic_device_profiles import (
+        get_device_profile,
+        get_profile_manager,
+    )
     from edgeflow.pipeline.end_to_end_pipeline import EdgeFlowPipeline
     from edgeflow.reporting.integrated_error_system import (
         ErrorCategory,
@@ -72,7 +85,10 @@
         OptimizationOrchestrator,
         OptimizationStrategy,
     )
-    from edgeflow.reporting.traceability_system import export_session_report, get_global_tracker
+    from edgeflow.reporting.traceability_system import (
+        export_session_report,
+        get_global_tracker,
+    )
 
     ENHANCED_FEATURES_AVAILABLE = True
 except ImportError as e:
@@ -334,10 +350,14 @@ def load_config(
         # Prefer modern parser API if available
         spinner.update("Parsing EdgeFlow file")
         if _parse_edgeflow_file is not None:
-            print(f"DEBUG: Using _parse_edgeflow_file from {getattr(_parse_edgeflow_file, '__module__', 'unknown')}")
+            print(
+                f"DEBUG: Using _parse_edgeflow_file from {getattr(_parse_edgeflow_file, '__module__', 'unknown')}"
+            )
             config = _parse_edgeflow_file(file_path)
         else:
-            print(f"DEBUG: Using parse_ef from {getattr(parse_ef, '__module__', 'unknown')}")
+            print(
+                f"DEBUG: Using parse_ef from {getattr(parse_ef, '__module__', 'unknown')}"
+            )
             config = parse_ef(file_path)
 
         # Early validation for fast feedback

src/edgeflow/compiler/framework_parsers.py

@@ -70,9 +70,11 @@ def _parse_saved_model(self, model_path: str) -> UIRGraph:
             framework_metadata={
                 "model_path": model_path,
                 "model_type": "saved_model",
-                "signatures": list(model.signatures.keys())
-                if hasattr(model, "signatures")
-                else [],
+                "signatures": (
+                    list(model.signatures.keys())
+                    if hasattr(model, "signatures")
+                    else []
+                ),
             },
         )
 
@@ -102,9 +104,9 @@ def _parse_keras_model(self, model_path: str) -> UIRGraph:
             framework_metadata={
                 "model_path": model_path,
                 "model_type": "keras",
-                "model_config": model.get_config()
-                if hasattr(model, "get_config")
-                else {},
+                "model_config": (
+                    model.get_config() if hasattr(model, "get_config") else {}
+                ),
             },
         )
 
@@ -177,9 +179,9 @@ def _convert_keras_layer_to_uir(self, layer, layer_index: int) -> UIRNode:
             framework_type=FrameworkType.TENSORFLOW,
             framework_metadata={
                 "keras_layer_type": type(layer).__name__,
-                "keras_layer_config": layer.get_config()
-                if hasattr(layer, "get_config")
-                else {},
+                "keras_layer_config": (
+                    layer.get_config() if hasattr(layer, "get_config") else {}
+                ),
                 "layer_index": layer_index,
             },
         )
@@ -406,9 +408,9 @@ def parse_model(self, model_path: str) -> UIRGraph:
                     "model_path": model_path,
                     "model_type": "onnx",
                     "ir_version": model.ir_version,
-                    "opset_version": model.opset_import[0].version
-                    if model.opset_import
-                    else None,
+                    "opset_version": (
+                        model.opset_import[0].version if model.opset_import else None
+                    ),
                     "producer_name": model.producer_name,
                     "producer_version": model.producer_version,
                 },

src/edgeflow/compiler/mlir_dialect.py

@@ -487,10 +487,10 @@ def transform(self, graph: UIRGraph) -> UIRGraph:
             # Check if nodes were quantized (renamed) or kept as is
             new_from_q = f"{from_node}_quantized"
             new_to_q = f"{to_node}_quantized"
-            
+
             new_from = new_from_q if new_from_q in optimized_graph.nodes else from_node
             new_to = new_to_q if new_to_q in optimized_graph.nodes else to_node
-            
+
             if new_from in optimized_graph.nodes and new_to in optimized_graph.nodes:
                 optimized_graph.add_edge(new_from, new_to, tensor_name)
 

src/edgeflow/config/config_suggester.py

@@ -594,15 +594,15 @@ def analyze_configuration_performance(
             "use_case": use_case.value if use_case else "unknown",
             "optimization_focus": config.get("optimize_for", "balanced"),
             "quantization_strategy": config.get("quantize", "none"),
-            "memory_efficiency": "high"
-            if config.get("memory_limit", 256) < 256
-            else "medium",
-            "latency_optimization": "high"
-            if config.get("buffer_size", 1) == 1
-            else "medium",
-            "accuracy_preservation": "high"
-            if config.get("quantize") == "none"
-            else "medium",
+            "memory_efficiency": (
+                "high" if config.get("memory_limit", 256) < 256 else "medium"
+            ),
+            "latency_optimization": (
+                "high" if config.get("buffer_size", 1) == 1 else "medium"
+            ),
+            "accuracy_preservation": (
+                "high" if config.get("quantize") == "none" else "medium"
+            ),
             "compatibility_score": validation_result.compatibility_score,
             "estimated_performance": validation_result.estimated_performance_impact,
             "recommendations": [],

src/edgeflow/config/dynamic_device_profiles.py

@@ -253,9 +253,9 @@ def get_optimization_config(self) -> Dict[str, Any]:
         """
         config: Dict[str, Any] = {
             "quantization": self.preferred_quantization,
-            "preferred_precision": "int8"
-            if "int8" in self.preferred_quantization
-            else "fp16",
+            "preferred_precision": (
+                "int8" if "int8" in self.preferred_quantization else "fp16"
+            ),
             "enabled_optimizations": [],
             "optimization_flags": {},
             "target_device": self.device_id,

src/edgeflow/deployment/deployment_packager.py

@@ -504,9 +504,11 @@ def _package_model_binary(
         # Create model metadata
         metadata = {
             "device_type": device_type.value,
-            "original_size_mb": os.path.getsize(model_path) / (1024 * 1024)
-            if os.path.exists(model_path)
-            else 0.0,
+            "original_size_mb": (
+                os.path.getsize(model_path) / (1024 * 1024)
+                if os.path.exists(model_path)
+                else 0.0
+            ),
             "optimized_size_mb": os.path.getsize(optimized_model_path) / (1024 * 1024),
             "optimization_flags": constraints.optimization_flags,
             "compression_level": constraints.compression_level,
@@ -593,9 +595,11 @@ def _package_inference_code(
             size_mb=os.path.getsize(package_path) / (1024 * 1024),
             dependencies=constraints.runtime_requirements,
             metadata={
-                "language": "python"
-                if device_type not in [DeviceType.CORTEX_M4, DeviceType.CORTEX_M7]
-                else "cpp"
+                "language": (
+                    "python"
+                    if device_type not in [DeviceType.CORTEX_M4, DeviceType.CORTEX_M7]
+                    else "cpp"
+                )
             },
         )