readme

README.md

@@ -1,8 +1,8 @@
 ---
 title: BoAmps Report Creation
-emoji: 🐢
-colorFrom: purple
-colorTo: purple
+emoji: 🌿
+colorFrom: red
+colorTo: green
 sdk: gradio
 sdk_version: 5.15.0
 app_file: app.py
@@ -11,6 +11,7 @@ license: apache-2.0
 short_description: Create a report in BoAmps format
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+This tool is part of the initiative [BoAmps](https://github.com/Boavizta/BoAmps). 
+The purpose of the BoAmps project is to build a large, open, database of energy consumption of IT / AI tasks depending on data nature, algorithms, hardware, etc., in order to improve energy efficiency approaches based on empiric knowledge.
 
-This project was initiated by a group of students from Sud Telecom Paris.
+This space was initiated by a group of students from Sud Telecom Paris, many thanks to [Hicham FILALI](https://huggingface.co/FILALIHicham) for his work.

src/services/huggingface.py

@@ -52,7 +52,7 @@ def create_flattened_data(data):
               "parametersNumber", "framework",  "frameworkVersion", "classPath", "layersNumber", "epochsNumber", "optimizer", "quantization"]
     """Create a flattened data structure for the algorithms."""
     algorithms_data = {field: "| ".join(str(algo.get(
-        field)) for algo in algorithms if algo.get(field)) or None for field in fields}
+        field)) for algo in algorithms if algo.get(field)) or "" for field in fields}
     trainingType_str = algorithms_data["trainingType"]
     algorithmType_str = algorithms_data["algorithmType"]
     algorithmName_str = algorithms_data["algorithmName"]
@@ -75,7 +75,7 @@ def create_flattened_data(data):
               "dataQuantity", "shape", "source", "sourceUri",  "owner"]
     """Create a flattened data structure for the dataset."""
     dataset_data = {field: "| ".join(
-        str(d.get(field)) for d in dataset if d.get(field)) or None for field in fields}
+        str(d.get(field)) for d in dataset if d.get(field)) or "" for field in fields}
     dataUsage_str = dataset_data["dataUsage"]
     dataType_str = dataset_data["dataType"]
     dataFormat_str = dataset_data["dataFormat"]
@@ -93,7 +93,7 @@ def create_flattened_data(data):
               "powerCalibrationMeasurement",  "durationCalibrationMeasurement", "powerConsumption", "measurementDuration", "measurementDateTime"]
     """Create a flattened data structure for the measures."""
     measures_data = {field: "| ".join(str(measure.get(
-        field)) for measure in measures if measure.get(field)) or None for field in fields}
+        field)) for measure in measures if measure.get(field)) or "" for field in fields}
     measurementMethod_str = measures_data["measurementMethod"]
     manufacturer_str = measures_data["manufacturer"]
     version_str = measures_data["version"]
@@ -114,7 +114,7 @@ def create_flattened_data(data):
 
     # Generate concatenated strings for each field
     component_data = {field: "| ".join(str(comp.get(
-        field)) for comp in components if comp.get(field)) or None for field in fields}
+        field)) for comp in components if comp.get(field)) or "" for field in fields}
 
     componentName_str = component_data["componentName"]
     componentType_str = component_data["componentType"]
@@ -219,7 +219,7 @@ def create_flattened_data(data):
         "powerSourceCarbonIntensity": [data.get("environment", {}).get("powerSourceCarbonIntensity", "")],
 
         # Quality
-        "quality": [data.get("quality", "null")],
+        "quality": [data.get("quality", "")],
     }