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Isaacgv commited on Mar 29, 2023

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b6d2398

1 Parent(s): 2de4b7f

update project

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Files changed (13) hide show

README.md +0 -12
app.py +117 -40
configtest.json → config_file/configtest.json +0 -0
config_file/demo_full_yvesrocher.json +209 -0
config_file/demo_yvesrocher.json +139 -0
config_parser.py +2 -2
demo_full_yvesrocher.json +0 -100
demo_yvesrocher.json +0 -55
inferencer.py +105 -56
poetry.lock +4 -26
pyproject.toml +0 -1
requirements.txt +0 -0
requirements_test.txt +92 -0

README.md DELETED Viewed

@@ -1,12 +0,0 @@
----
-title: Classification
-emoji: ⚡
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py CHANGED Viewed

@@ -12,70 +12,147 @@ Title : Inference With Gradio running an onnxruntime backend
 import os
 import gradio as gr
 import sys
 sys.path.append(".")
 from config_parser import *
 from inferencer import *
 gr.close_all()
-def infertace_builder():
-    interface_list = []
     logging.log(level=logging.INFO, msg=f"Building Interfaces")
     for task_number in range(0, len(tasks)):
-        logging.log(level=logging.INFO, msg=f"Building Interface n° {task_number}")
-        interface_list.append(
-            gr.Interface(
-                title=tasks[task_number],
-                description=tasks_description[task_number],
-                fn=analysis,
-                # if tasks_name[task_number] == "Étiquetage"
-                # else corck_screwing_analysis,
-                allow_flagging="never",
-                inputs=[
-                    gr.Textbox(
-                        value=f"task{task_number+1}", visible=False, interactive=False
-                    ),
-                    gr.Dropdown(
-                        tasks_products[task_number],
-                        type="value",
-                        value=tasks_products[task_number][0],
-                        label="Choix",
-                        visible=True if len(tasks_products[task_number]) > 1 else False,
-                        info="Sur quel type de produit, voulez vous lancer l'analyse ?",
-                    ),
-                    gr.Image(
-                        label="Image à analyser",
-                        shape=None,
-                        image_mode="RGB",
-                        invert_colors=False,
-                        source="upload",
-                        tool="editor",
-                        type="numpy",
-                    ),
-                ],
-                outputs=gr.Label(
-                    label="Résultats",
-                ),
             )
         )
     logging.log(level=logging.INFO, msg=f"Interfaces ready\n")
-    return interface_list
-generated_interface = infertace_builder()
 iface_generated = gr.TabbedInterface(
     interface_list=generated_interface,
     tab_names=tasks_name,
-    title=title,
 )
 if __name__ == "__main__":
     logging.log(level=logging.INFO, msg="Starting the Gradio server...")
     iface_generated.launch(
-        server_name="0.0.0.0"
     )
     logging.log(level=logging.INFO, msg="Stopping the Gradio server...")

 import os
 import gradio as gr
 import sys
 sys.path.append(".")
 from config_parser import *
 from inferencer import *
 gr.close_all()
+def generate_parralel_interface(task_number, product):
+    logging.log(level=logging.INFO, msg=f"Entering generate_parralel_interface")
+    generate_parralel_interface_list = []
+    number_of_model = len(config["tasks"][f"task{task_number+1}"]["models"][product])
+    logging.log(level=logging.INFO, msg=f"Real Number of model: {number_of_model}")
+    for model_number in range(0, number_of_model):
+        logging.log(level=logging.INFO, msg=f"Building Interface for {product}")
+        logging.log(
+            level=logging.INFO, msg=f"Building Interface for sub-model n°{model_number}"
+        )
+        generate_parralel_interface_list.append(
+            create_interface(task_number, product, model_number)
+        )
+    generated_parralel_interface = gr.Parallel(
+        *generate_parralel_interface_list,
+        title=tasks[task_number],
+        description=tasks_description[task_number],
+    )
+    logging.log(level=logging.INFO, msg=f"Leaving generate_parralel_interface")
+    return generated_parralel_interface
+def create_interface(task_number, product, model_number):
+    logging.log(level=logging.INFO, msg=f"Entering create_interface")
+    title = tasks[task_number]
+    description = tasks_description[task_number]
+    product_lists = tasks_products[task_number]
+    logging.log(level=logging.INFO, msg=f"Title : {title}")
+    logging.log(level=logging.INFO, msg=f"Description : {description}")
+    logging.log(level=logging.INFO, msg=f"Product list : {product_lists}")
+    logging.log(level=logging.INFO, msg=f"Model Number: {model_number}")
+    created_interface = gr.Interface(
+        title=title,
+        description=description,
+        fn=inferencer_arr[f"task{task_number+1}"][product][str(model_number)][
+            "function"
+        ],
+        # fn=lambda x: x,
+        allow_flagging="never",
+        inputs=[
+            gr.Textbox(
+                value=f"task{task_number+1}",
+                label="Tasks",
+                visible=False,
+                interactive=False,
+            ),
+            gr.Dropdown(
+                tasks_products[task_number],
+                type="value",
+                value=product,
+                label="Choix",
+                # visible=True if len(tasks_products[task_number]) > 1 else False,
+                visible=False,
+                info="Sur quel type de produit, voulez vous lancer l'analyse ?",
+            ),
+            gr.Image(
+                label="Image à analyser",
+                shape=None,
+                image_mode="RGB",
+                invert_colors=False,
+                source="upload",
+                tool="editor",
+                type="numpy",
+            ),
+        ],
+        outputs=gr.Label(
+            label="Résultats",
+        ),
+    )
+    logging.log(level=logging.INFO, msg=f"Leaving create_interface")
+    return created_interface
+def interface_builder():
+    interface_builder_list = []
+    interface_builder_dict = {}
     logging.log(level=logging.INFO, msg=f"Building Interfaces")
+    logging.log(level=logging.INFO, msg=f"Number of task(s) : {len(tasks)}")
     for task_number in range(0, len(tasks)):
+        logging.log(level=logging.INFO, msg=f"Treating task n°{task_number+1}")
+        logging.log(level=logging.INFO, msg=f"Building Interface n°{task_number+1}")
+        interface_builder_dict[tasks[task_number]] = {}
+        product_list = list(config["tasks"][f"task{task_number+1}"]["models"].keys())
+        logging.log(level=logging.DEBUG, msg=f"Products : {product_list}")
+        interface_builder_product_level_list = []
+        for product in product_list:
+            logging.log(level=logging.INFO, msg=f"Product : {product}")
+            interface_builder_dict[tasks[task_number]][product] = []
+            if len(config["tasks"][f"task{task_number+1}"]["models"][product]) > 1:
+                generated_parralel_interface = generate_parralel_interface(
+                    task_number, product
+                )
+                interface_builder_dict[tasks[task_number]][product].append(
+                    generated_parralel_interface
+                )
+                interface_builder_product_level_list.append(
+                    generated_parralel_interface
+                )
+            else:
+                generated_interface = create_interface(
+                    task_number=task_number, product=product, model_number=0
+                )
+                interface_builder_dict[tasks[task_number]][product].append(
+                    generated_interface
+                )
+                interface_builder_product_level_list.append(generated_interface)
+        interface_builder_list.append(
+            gr.TabbedInterface(
+                interface_list=interface_builder_product_level_list,
+                tab_names=product_list,
             )
         )
     logging.log(level=logging.INFO, msg=f"Interfaces ready\n")
+    # logging.log(level=logging.INFO, msg=f"Interfaces Dict {interface_dict}")
+    logging.log(level=logging.DEBUG, msg=f"Interfaces List {interface_builder_list}")
+    return interface_builder_list
+generated_interface = interface_builder()
 iface_generated = gr.TabbedInterface(
     interface_list=generated_interface,
     tab_names=tasks_name,
+    title=f"{title} - {description}",
 )
 if __name__ == "__main__":
     logging.log(level=logging.INFO, msg="Starting the Gradio server...")
     iface_generated.launch(
+        server_name="0.0.0.0")
     )
     logging.log(level=logging.INFO, msg="Stopping the Gradio server...")

configtest.json → config_file/configtest.json RENAMED Viewed

File without changes

config_file/demo_full_yvesrocher.json ADDED Viewed

	@@ -0,0 +1,209 @@

+{
+    "title": "YVES ROCHER",
+    "description": "Démonstration des algos de reconnaissance d'étiquetage/bouchage correct",
+    "tasks": {
+        "task1": {
+            "shortname": "Étiquetage",
+            "name": {
+                "en": "Quality Control of Labels",
+                "fr": "Contrôle de l'Étiquetage"
+            },
+            "description": {
+                "en": "Is the label in the right position ?",
+                "fr": "Est-ce que l'étiquette est bien positionnée ?"
+            },
+            "products": [
+                "497 Pure Algue 200ml",
+                "505 Pure Calmille 200ml",
+                "614 Eco Douche 100ml",
+                "648 Hair Care 300ml"
+            ],
+            "models": {
+                "497 Pure Algue 200ml": [
+                    {
+                        "type": "classification",
+                        "path": "https://automi-test-models.s3.eu-west-3.amazonaws.com/inference-pipeline/po2xfZzK0KtWZUpXrFjZ/model.onnx",
+                        "input_shape": 224,
+                        "mean": [
+                            0.485,
+                            0.456,
+                            0.406
+                        ],
+                        "std": [
+                            0.229,
+                            0.224,
+                            0.225
+                        ],
+                        "class_names": [
+                            "Étiquetage correct",
+                            "Étiquetage incorrect"
+                        ]
+                    },
+                    {
+                        "type": "classification",
+                        "path": "https://automi-test-models.s3.eu-west-3.amazonaws.com/inference-pipeline/po2xfZzK0KtWZUpXrFjZ/model.onnx",
+                        "input_shape": 224,
+                        "mean": [
+                            0.485,
+                            0.456,
+                            0.406
+                        ],
+                        "std": [
+                            0.229,
+                            0.224,
+                            0.225
+                        ],
+                        "class_names": [
+                            "Étiquetage correct",
+                            "Étiquetage incorrect"
+                        ]
+                    }
+                ],
+                "505 Pure Calmille 200ml": [
+                    {
+                        "type": "classification",
+                        "path": "https://automi-test-models.s3.eu-west-3.amazonaws.com/inference-pipeline/po2xfZzK0KtWZUpXrFjZ/model.onnx",
+                        "input_shape": 224,
+                        "mean": [
+                            0.485,
+                            0.456,
+                            0.406
+                        ],
+                        "std": [
+                            0.229,
+                            0.224,
+                            0.225
+                        ],
+                        "class_names": [
+                            "Étiquetage correct",
+                            "Étiquetage incorrect"
+                        ]
+                    }
+                ],
+                "614 Eco Douche 100ml": [
+                    {
+                        "type": "classification",
+                        "path": "https://automi-test-models.s3.eu-west-3.amazonaws.com/inference-pipeline/po2xfZzK0KtWZUpXrFjZ/model.onnx",
+                        "input_shape": 224,
+                        "mean": [
+                            0.485,
+                            0.456,
+                            0.406
+                        ],
+                        "std": [
+                            0.229,
+                            0.224,
+                            0.225
+                        ],
+                        "class_names": [
+                            "Étiquetage correct",
+                            "Étiquetage incorrect"
+                        ]
+                    }
+                ],
+                "648 Hair Care 300ml": [
+                    {
+                        "type": "classification",
+                        "path": "https://automi-test-models.s3.eu-west-3.amazonaws.com/inference-pipeline/J5dAmDWboJNDouVkMVIL/model.onnx",
+                        "input_shape": 224,
+                        "mean": [
+                            0.485,
+                            0.456,
+                            0.406
+                        ],
+                        "std": [
+                            0.229,
+                            0.224,
+                            0.225
+                        ],
+                        "class_names": [
+                            "Étiquetage correct",
+                            "Étiquetage incorrect"
+                        ]
+                    }
+                ]
+            }
+        },
+        "task2": {
+            "shortname": "Bouchage",
+            "name": {
+                "en": "Quality Control of Corck Screwing",
+                "fr": "Contrôle du Bouchage"
+            },
+            "description": {
+                "en": "Is the corck in the right position ?",
+                "fr": "Est-ce que le bouchon est bien positionné et entièrement vissé ?"
+            },
+            "products": [
+                "187 Hamamelis 300ml",
+                "550 SVC 300ml",
+                "600 PN 500 ml"
+            ],
+            "models": {
+                "187 Hamamelis 300ml": [
+                    {
+                        "type": "classification",
+                        "path": "https://automi-test-models.s3.eu-west-3.amazonaws.com/inference-pipeline/Qp6BRHBcLq7KKxqCWqmV/model.onnx",
+                        "input_shape": 224,
+                        "mean": [
+                            0.485,
+                            0.456,
+                            0.406
+                        ],
+                        "std": [
+                            0.229,
+                            0.224,
+                            0.225
+                        ],
+                        "class_names": [
+                            "Bouchage correct",
+                            "Bouchage incorrect"
+                        ]
+                    }
+                ],
+                "550 SVC 300ml": [
+                    {
+                        "type": "anomaly_detection-classification",
+                        "path": "/Users/bastien/Downloads/model_corck_screwing.onnx",
+                        "input_shape": 256,
+                        "mean": [
+                            0.485,
+                            0.456,
+                            0.406
+                        ],
+                        "std": [
+                            0.229,
+                            0.224,
+                            0.225
+                        ],
+                        "class_names": [
+                            "Bouchage correct",
+                            "Bouchage incorrect"
+                        ]
+                    }
+                ],
+                "600 PN 500 ml": [
+                    {
+                        "type": "anomaly_detection-classification",
+                        "path": "/Users/bastien/Downloads/model_corck_screwing.onnx",
+                        "input_shape": 256,
+                        "mean": [
+                            0.485,
+                            0.456,
+                            0.406
+                        ],
+                        "std": [
+                            0.229,
+                            0.224,
+                            0.225
+                        ],
+                        "class_names": [
+                            "Bouchage correct",
+                            "Bouchage incorrect"
+                        ]
+                    }
+                ]
+            }
+        }
+    }
+}

config_file/demo_yvesrocher.json ADDED Viewed

	@@ -0,0 +1,139 @@

+{
+    "title": "YVES ROCHER",
+    "description": "Démonstration des algos de reconnaissance d'étiquetage/bouchage correct",
+    "tasks": {
+        "task1": {
+            "shortname": "Étiquetage",
+            "name": {
+                "en": "Quality Control of Labels",
+                "fr": "Contrôle de l'Étiquetage"
+            },
+            "description": {
+                "en": "Is the label in the right position ?",
+                "fr": "Est-ce que l'étiquette est bien positionnée ?"
+            },
+            "products": [
+                "505 Pure Calmille 200ml"
+            ],
+            "models": {
+                "505 Pure Calmille 200ml": [
+                    {
+                        "type": "classification",
+                        "path": "https://automi-test-models.s3.eu-west-3.amazonaws.com/inference-pipeline/dmeQ6Mae0HKDwkHBTdh2/model.onnx",
+                        "input_shape": 224,
+                        "mean": [
+                            0.485,
+                            0.456,
+                            0.406
+                        ],
+                        "std": [
+                            0.229,
+                            0.224,
+                            0.225
+                        ],
+                        "class_names": [
+                            "Étiquetage correct",
+                            "Étiquetage incorrect"
+                        ]
+                    }
+                ],
+                "648 Haire Care 300 ml": [
+                    {
+                        "type": "classification",
+                        "path": "https://automi-test-models.s3.eu-west-3.amazonaws.com/inference-pipeline/uDFdeazjnSzTnIE0JVSf/model.onnx",
+                        "input_shape": 224,
+                        "mean": [
+                            0.485,
+                            0.456,
+                            0.406
+                        ],
+                        "std": [
+                            0.229,
+                            0.224,
+                            0.225
+                        ],
+                        "class_names": [
+                            "Étiquetage correct",
+                            "Étiquetage incorrect"
+                        ]
+                    },
+                    {
+                        "type": "classification",
+                        "path": "https://automi-test-models.s3.eu-west-3.amazonaws.com/inference-pipeline/J5dAmDWboJNDouVkMVIL/model.onnx",
+                        "input_shape": 224,
+                        "mean": [
+                            0.485,
+                            0.456,
+                            0.406
+                        ],
+                        "std": [
+                            0.229,
+                            0.224,
+                            0.225
+                        ],
+                        "class_names": [
+                            "Étiquetage correct",
+                            "Étiquetage incorrect"
+                        ]
+                    }
+                ]
+            }
+        },
+        "task2": {
+            "shortname": "Bouchage",
+            "name": {
+                "en": "Quality Control of Corck Screwing",
+                "fr": "Contrôle du Bouchage"
+            },
+            "description": {
+                "en": "Is the corck in the right position ?",
+                "fr": "Est-ce que le bouchon est bien positionné et entièrement vissé ?"
+            },
+            "products": [
+                "187 Hamamelis 300ml"
+            ],
+            "models": {
+                "187 Hamamelis 300ml": [
+                    {
+                        "type": "classification",
+                        "path": "https://automi-test-models.s3.eu-west-3.amazonaws.com/inference-pipeline/3nkoTiBxDNBzqyGtE5w8/model.onnx",
+                        "input_shape": 224,
+                        "mean": [
+                            0.485,
+                            0.456,
+                            0.406
+                        ],
+                        "std": [
+                            0.229,
+                            0.224,
+                            0.225
+                        ],
+                        "class_names": [
+                            "Bouchage correct",
+                            "Bouchage incorrect"
+                        ]
+                    },
+                    {
+                        "type": "classification",
+                        "path": "https://automi-test-models.s3.eu-west-3.amazonaws.com/inference-pipeline/Qp6BRHBcLq7KKxqCWqmV/model.onnx",
+                        "input_shape": 224,
+                        "mean": [
+                            0.485,
+                            0.456,
+                            0.406
+                        ],
+                        "std": [
+                            0.229,
+                            0.224,
+                            0.225
+                        ],
+                        "class_names": [
+                            "Bouchage correct",
+                            "Bouchage incorrect"
+                        ]
+                    }
+                ]
+            }
+        }
+    }
+}

config_parser.py CHANGED Viewed

@@ -15,12 +15,12 @@ import os
 from functools import reduce
 logging.basicConfig(
-    format="%(asctime)s.%(msecs)03d %(levelname)-8s %(message)s",
     level=logging.INFO,
     datefmt="%Y%m%d-%H%M%S",
 )
-with open("demo_yvesrocher.json") as config_file:
     config = json.load(config_file)
 logging.log(level=logging.DEBUG, msg=f"Loaded config file : {json.dumps(config)}")

 from functools import reduce
 logging.basicConfig(
+    format="%(asctime)s.%(msecs)03d %(levelname)-8s [%(filename)s:%(lineno)s - %(funcName)20s() ] - %(message)s",
     level=logging.INFO,
     datefmt="%Y%m%d-%H%M%S",
 )
+with open("config_file/demo_yvesrocher.json") as config_file:
     config = json.load(config_file)
 logging.log(level=logging.DEBUG, msg=f"Loaded config file : {json.dumps(config)}")

demo_full_yvesrocher.json DELETED Viewed

@@ -1,100 +0,0 @@
-{
-    "title": "YVES ROCHER",
-    "description": "Démonstration des algos de reconnaissance d'étiquetage/bouchage correct",
-    "tasks": {
-        "task1": {
-            "shortname": "Étiquetage",
-            "name": {
-                "en": "Quality Control of Labels",
-                "fr": "Contrôle de l'Étiquetage"
-            },
-            "description": {
-                "en": "Is the label in the right position ?",
-                "fr": "Est-ce que l'étiquette est bien positionnée ?"
-            },
-            "products":[
-                "497 Pure Algue 200ml",
-                "505 Pure Calmille 200ml",
-                "614 Eco Douche 100ml",
-                "648 Hair Care 300ml"
-            ],
-            "models": {
-                "497 Pure Algue 200ml": {
-                    "type": "classification",
-                    "path":  "https://automi-test-models.s3.eu-west-3.amazonaws.com/inference-pipeline/po2xfZzK0KtWZUpXrFjZ/model.onnx",
-                    "input_shape": 224,
-                    "mean": [0.485, 0.456, 0.406],
-                    "std": [0.229, 0.224, 0.225],
-                    "class_names": ["Étiquetage correct", "Étiquetage incorrect"]
-                },
-                "505 Pure Calmille 200ml": {
-                    "type": "classification",
-                    "path": "https://automi-test-models.s3.eu-west-3.amazonaws.com/inference-pipeline/po2xfZzK0KtWZUpXrFjZ/model.onnx",
-                    "input_shape": 224,
-                    "mean": [0.485, 0.456, 0.406],
-                    "std": [0.229, 0.224, 0.225],
-                    "class_names": ["Étiquetage correct", "Étiquetage incorrect"]
-                },
-                "614 Eco Douche 100ml": {
-                    "type": "classification",
-                    "path": "https://automi-test-models.s3.eu-west-3.amazonaws.com/inference-pipeline/po2xfZzK0KtWZUpXrFjZ/model.onnx",
-                    "input_shape": 224,
-                    "mean": [0.485, 0.456, 0.406],
-                    "std": [0.229, 0.224, 0.225],
-                    "class_names": ["Étiquetage correct", "Étiquetage incorrect"]
-                },
-                "648 Hair Care 300ml": {
-                    "type": "classification",
-                    "path": "https://automi-test-models.s3.eu-west-3.amazonaws.com/inference-pipeline/po2xfZzK0KtWZUpXrFjZ/model.onnx",
-                    "input_shape": 224,
-                    "mean": [0.485, 0.456, 0.406],
-                    "std": [0.229, 0.224, 0.225],
-                    "class_names": ["Étiquetage correct", "Étiquetage incorrect"]
-                }
-            }
-        },
-        "task2": {
-            "shortname": "Bouchage",
-            "name": {
-                "en": "Quality Control of Corck Screwing",
-                "fr": "Contrôle du Bouchage"
-            },
-            "description": {
-                "en": "Is the corck in the right position ?",
-                "fr": "Est-ce que le bouchon est bien positionné et entièrement vissé ?"
-            },
-            "products":[
-                "187 Hamamelis 300ml",
-                "550 SVC 300ml",
-                "600 PN 500 ml"
-            ],
-            "models": {
-                "187 Hamamelis 300ml":{
-                    "type": "classification",
-                    "path": "https://automi-test-models.s3.eu-west-3.amazonaws.com/inference-pipeline/Qp6BRHBcLq7KKxqCWqmV/model.onnx",
-                    "input_shape": 224,
-                    "mean": [0.485, 0.456, 0.406],
-                    "std": [0.229, 0.224, 0.225],
-                    "class_names": ["Bouchage correct", "Bouchage incorrect"]
-                },
-                "550 SVC 300ml": {
-                    "type": "classification",
-                    "path": "/Users/bastien/Downloads/model_corck_screwing.onnx",
-                    "input_shape": 256,
-                    "mean": [0.485, 0.456, 0.406],
-                    "std": [0.229, 0.224, 0.225],
-                    "class_names": ["Bouchage correct", "Bouchage incorrect"]
-                },
-                "600 PN 500 ml": {
-                    "type": "classification",
-                    "path": "/Users/bastien/Downloads/model_corck_screwing.onnx",
-                    "input_shape": 256,
-                    "mean": [0.485, 0.456, 0.406],
-                    "std": [0.229, 0.224, 0.225],
-                    "class_names": ["Bouchage correct", "Bouchage incorrect"]
-                }
-            }
-        }
-    }
-}

demo_yvesrocher.json DELETED Viewed

@@ -1,55 +0,0 @@
-{
-    "title": "YVES ROCHER",
-    "description": "Démonstration des algos de reconnaissance d'étiquetage/bouchage correct",
-    "tasks": {
-        "task1": {
-            "shortname": "Étiquetage",
-            "name": {
-                "en": "Quality Control of Labels",
-                "fr": "Contrôle de l'Étiquetage"
-            },
-            "description": {
-                "en": "Is the label in the right position ?",
-                "fr": "Est-ce que l'étiquette est bien positionnée ?"
-            },
-            "products":[
-                "505 Pure Calmille 200ml"
-            ],
-            "models": {
-                "505 Pure Calmille 200ml": {
-                    "type": "classification",
-                    "path": "https://automi-test-models.s3.eu-west-3.amazonaws.com/inference-pipeline/dmeQ6Mae0HKDwkHBTdh2/model.onnx",
-                    "input_shape": 224,
-                    "mean": [0.485, 0.456, 0.406],
-                    "std": [0.229, 0.224, 0.225],
-                    "class_names": ["Étiquetage correct", "Étiquetage incorrect"]
-                }
-            }
-        },
-        "task2": {
-            "shortname": "Bouchage",
-            "name": {
-                "en": "Quality Control of Corck Screwing",
-                "fr": "Contrôle du Bouchage"
-            },
-            "description": {
-                "en": "Is the corck in the right position ?",
-                "fr": "Est-ce que le bouchon est bien positionné et entièrement vissé ?"
-            },
-            "products":[
-                "187 Hamamelis 300ml"
-            ],
-            "models": {
-                "187 Hamamelis 300ml":{
-                    "type": "classification",
-                    "path": "https://automi-test-models.s3.eu-west-3.amazonaws.com/inference-pipeline/3nkoTiBxDNBzqyGtE5w8/model.onnx",
-                    "input_shape": 224,
-                    "mean": [0.485, 0.456, 0.406],
-                    "std": [0.229, 0.224, 0.225],
-                    "class_names": ["Bouchage correct", "Bouchage incorrect"]
-                }
-            }
-        }
-    }
-}

inferencer.py CHANGED Viewed

@@ -15,6 +15,26 @@ import requests
 from config_parser import *
 from torchvision import transforms
 corck_screwing_metadata = {
     "image_threshold": 0.9247307181358337,
     "pixel_threshold": 0.9247307181358337,
@@ -27,26 +47,35 @@ logging.log(level=logging.INFO, msg="Loading models...")
 for task in config["tasks"].keys():
     inferencer_arr[task] = {}
     r = None
-    for model in config["tasks"][task]["models"]:
-        product_name = model
-        model_path = config["tasks"][task]["models"][product_name]["path"]
-        logging.log(
-            level=logging.INFO,
-            msg=f"Loading labeling model for product {product_name}",
-        )
-        inferencer_arr[task][product_name] = {}
-        if model_path.startswith("http"):
-            r = requests.get(model_path, stream=True).content
-        inferencer_arr[task][product_name][product_name] = ort.InferenceSession(
-            r if model_path.startswith("http") else model_path
-        )
-        inferencer_arr[task][product_name]["input_name"] = (
-            inferencer_arr[task][product_name][product_name].get_inputs()[0].name
-        )
-        inferencer_arr[task][product_name]["output_name"] = (
-            inferencer_arr[task][product_name][product_name].get_outputs()[0].name
-        )
-        logging.log(level=logging.INFO, msg=f"Model for {product_name} loaded\n")
 logging.log(level=logging.INFO, msg="All models loaded...\n\n")
 logging.log(level=logging.DEBUG, msg=f"Inferencer Array : {inferencer_arr}")
@@ -56,40 +85,42 @@ def softmax(x):
     return e_x / e_x.sum(axis=0)
-def is_anomalous_classification(prediction, meta_data):
     pred_label = None
     pred_score = prediction.reshape(-1).max()
     if "image_threshold" in meta_data:
         pred_label = (
-            config["tasks"][task]["models"][model]["class_names"][0]
             if (pred_score >= meta_data["image_threshold"])
-            else config["tasks"][task]["models"][model]["class_names"][1]
         )
     return pred_label, pred_score
-def analysis(task, use_case, image):
-    """
-    Main function that process inference and return results strings
-    Args:
-        - task
-        - use case
-        - image
-    Returns:
-        - String including label and confidence of the model
-    """
-    input_image = pre_process_all(task, use_case, image)
-    result = inference(task, use_case, input_image)
-    logging.log(level=logging.DEBUG, msg=result)
-    return result
-def pre_process_all(task, use_case, image):
     preprocessed_image = []
-    input_shape = config["tasks"][task]["models"][use_case]["input_shape"]
-    mean = config["tasks"][task]["models"][use_case]["mean"]
-    std = config["tasks"][task]["models"][use_case]["std"]
     logging.log(level=logging.DEBUG, msg=f"Shape {input_shape}")
     logging.log(level=logging.DEBUG, msg=f"Mean {mean}")
     logging.log(level=logging.DEBUG, msg=f"Std {std}")
@@ -108,29 +139,47 @@ def pre_process_all(task, use_case, image):
     return preprocessed_image
-def inference(task, model, input_image):
     """
     Process inference for bottle labels
     Args:
         - task
-        - use_case (product)
         - image
     Returns:
         - String including label and confidence of the model
     """
-    prediction = inferencer_arr[task][model][model].run(
-        [inferencer_arr[task][model]["output_name"]],
-        {inferencer_arr[task][model]["input_name"]: input_image},
     )
     prediction = prediction[0].squeeze()
-    logging.log(level=logging.INFO, msg=f"Softmaxed prediction {softmax(prediction)}")
-    if config["tasks"][task]["models"][model]["type"] == "classification":
-        result = f"{config['tasks'][task]['models'][model]['class_names'][np.argmax(prediction)]} avec une confiance de {str(round(softmax(prediction)[np.argmax(prediction)]*100))} %"
-    elif task == "task2":
-        label, score = is_anomalous_classification(prediction, corck_screwing_metadata)
         result = f"{label} avec une confiance de {str(round(score*100))} %"
     else:
-        result = "Houston, we've got a problem"
     logging.log(level=logging.DEBUG, msg=result)
     return result

 from config_parser import *
 from torchvision import transforms
+def make_func(model_number):
+    def _analysis(task, product, image):
+        """
+        Main function that process inference and return results strings
+        Args:
+            - task
+            - use case
+            - image
+        Returns:
+            - String including label and confidence of the model
+        """
+        input_image = pre_process_all(task, product, image, model_number=model_number)
+        result = inference(task, product, input_image, model_number=model_number)
+        logging.log(level=logging.DEBUG, msg=result)
+        return result
+    return _analysis
 corck_screwing_metadata = {
     "image_threshold": 0.9247307181358337,
     "pixel_threshold": 0.9247307181358337,
 for task in config["tasks"].keys():
     inferencer_arr[task] = {}
     r = None
+    for product in config["tasks"][task]["models"]:
+        inferencer_arr[task][product] = {}
+        for model_number in range(len(config["tasks"][task]["models"][product])):
+            model = config["tasks"][task]["models"][product][model_number]
+            model_path = model["path"]
+            logging.log(
+                level=logging.INFO,
+                msg=f"Loading model for product {product}, version {model_number}",
+            )
+            inferencer_arr[task][product][str(model_number)] = {}
+            if model_path.startswith("http"):
+                r = requests.get(model_path, stream=True).content
+            inferencer_arr[task][product][str(model_number)][
+                "model"
+            ] = ort.InferenceSession(r if model_path.startswith("http") else model_path)
+            inferencer_arr[task][product][str(model_number)]["function"] = make_func(
+                model_number
+            )
+            inferencer_arr[task][product][str(model_number)]["input_name"] = (
+                inferencer_arr[task][product][str(model_number)]["model"]
+                .get_inputs()[0]
+                .name
+            )
+            inferencer_arr[task][product][str(model_number)]["output_name"] = (
+                inferencer_arr[task][product][str(model_number)]["model"]
+                .get_outputs()[0]
+                .name
+            )
+            logging.log(level=logging.INFO, msg=f"Model for {product} loaded\n")
 logging.log(level=logging.INFO, msg="All models loaded...\n\n")
 logging.log(level=logging.DEBUG, msg=f"Inferencer Array : {inferencer_arr}")
     return e_x / e_x.sum(axis=0)
+def is_anomalous_classification(task, product, model_number, prediction, meta_data):
     pred_label = None
     pred_score = prediction.reshape(-1).max()
+    logging.log(level=logging.INFO, msg=f"Task {task}")
+    logging.log(level=logging.INFO, msg=f"Product {product}")
+    class_names = config["tasks"][task]["models"][product][model_number]["class_names"]
     if "image_threshold" in meta_data:
         pred_label = (
+            class_names[0]
             if (pred_score >= meta_data["image_threshold"])
+            else class_names[1]
         )
+    logging.log(
+        level=logging.INFO,
+        msg=f"Predicted label {pred_label} with a confidence of {pred_score}",
+    )
     return pred_label, pred_score
+def pre_process_all(task, product, image, model_number):
+    # model_number = model_number-1
+    logging.log(level=logging.INFO, msg=f"Task {task}")
+    logging.log(level=logging.INFO, msg=f"Product {product}")
+    logging.log(level=logging.INFO, msg=f"Model Number {model_number}")
+    logging.log(
+        level=logging.DEBUG,
+        msg=f'Product Array  {config["tasks"][task]["models"][product]}',
+    )
+    logging.log(
+        level=logging.DEBUG,
+        msg=f'Model Array {config["tasks"][task]["models"][product][model_number]}',
+    )
     preprocessed_image = []
+    input_shape = config["tasks"][task]["models"][product][model_number]["input_shape"]
+    mean = config["tasks"][task]["models"][product][int(model_number)]["mean"]
+    std = config["tasks"][task]["models"][product][int(model_number)]["std"]
     logging.log(level=logging.DEBUG, msg=f"Shape {input_shape}")
     logging.log(level=logging.DEBUG, msg=f"Mean {mean}")
     logging.log(level=logging.DEBUG, msg=f"Std {std}")
     return preprocessed_image
+def inference(task, product, input_image, model_number):
     """
     Process inference for bottle labels
     Args:
         - task
+        - product
+        - model to use (number)
         - image
     Returns:
         - String including label and confidence of the model
     """
+    # model_number = model_number-1
+    logging.log(level=logging.DEBUG, msg=f"Inferencer {inferencer_arr}")
+    logging.log(level=logging.INFO, msg=f"Task {task}")
+    logging.log(level=logging.INFO, msg=f"Product {product}")
+    logging.log(level=logging.INFO, msg=f"Model {model_number}")
+    result = "Algorithm not yet supported"
+    prediction = inferencer_arr[task][product][str(model_number)]["model"].run(
+        [inferencer_arr[task][product][str(model_number)]["output_name"]],
+        {inferencer_arr[task][product][str(model_number)]["input_name"]: input_image},
     )
     prediction = prediction[0].squeeze()
+    model_type = config["tasks"][task]["models"][product][int(model_number)]["type"]
+    class_names = config["tasks"][task]["models"][product][int(model_number)][
+        "class_names"
+    ]
+    if model_type == "classification":
+        logging.log(
+            level=logging.INFO, msg=f"Softmaxed prediction {softmax(prediction)}"
+        )
+        result = f"{class_names[np.argmax(prediction)]} avec une confiance de {str(round(softmax(prediction)[np.argmax(prediction)]*100))} %"
+    elif model_type == "anomaly_detection-classification":
+        label, score = is_anomalous_classification(
+            task=task,
+            product=product,
+            model_number=int(model_number),
+            prediction=prediction,
+            meta_data=corck_screwing_metadata,
+        )
         result = f"{label} avec une confiance de {str(round(score*100))} %"
     else:
+        pass
     logging.log(level=logging.DEBUG, msg=result)
     return result

poetry.lock CHANGED Viewed

@@ -630,14 +630,14 @@ files = [
 [[package]]
 name = "filelock"
-version = "3.10.3"
 description = "A platform independent file lock."
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "filelock-3.10.3-py3-none-any.whl", hash = "sha256:99d6282f732410d44242ca02aa49835cf5473e2dd4d6734a2a785c8889dc191e"},
-    {file = "filelock-3.10.3.tar.gz", hash = "sha256:a26bfa34d26293e04886dff13fa8dd0c8c6e1a786b723c689755fe8939297410"},
 ]
 [package.extras]
@@ -800,17 +800,6 @@ smb = ["smbprotocol"]
 ssh = ["paramiko"]
 tqdm = ["tqdm"]
-[[package]]
-name = "functools"
-version = "0.5"
-description = "Fast tools for functional programming"
-category = "main"
-optional = false
-python-versions = "*"
-files = [
-    {file = "functools-0.5.tar.gz", hash = "sha256:596ed8999dee419c0749a41bfdd82e4697e80ea27ee01c716003ef55be9a54c5"},
-]
 [[package]]
 name = "gradio"
 version = "3.23.0"
@@ -1229,17 +1218,6 @@ files = [
     {file = "lit-16.0.0.tar.gz", hash = "sha256:3c4ac372122a1de4a88deb277b956f91b7209420a0bef683b1ab2d2b16dabe11"},
 ]
-[[package]]
-name = "logging"
-version = "0.4.9.6"
-description = "A logging module for Python"
-category = "main"
-optional = false
-python-versions = "*"
-files = [
-    {file = "logging-0.4.9.6.tar.gz", hash = "sha256:26f6b50773f085042d301085bd1bf5d9f3735704db9f37c1ce6d8b85c38f2417"},
-]
 [[package]]
 name = "markdown-it-py"
 version = "2.2.0"
@@ -3061,4 +3039,4 @@ testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8, <3.11"
-content-hash = "ec96f879cbd998b3226a4afe86ba3fc3aa6058ce2bdcba847eeeb14abebb70b4"

 [[package]]
 name = "filelock"
+version = "3.10.4"
 description = "A platform independent file lock."
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
+    {file = "filelock-3.10.4-py3-none-any.whl", hash = "sha256:6d332dc5c896f18ba93a21d987155e97c434a96d3fe4042ca70d0b3b46e3b470"},
+    {file = "filelock-3.10.4.tar.gz", hash = "sha256:9fc1734dbddcdcd4aaa02c160dd94db5272b92dfa859b44ec8df28e160b751f0"},
 ]
 [package.extras]
 ssh = ["paramiko"]
 tqdm = ["tqdm"]
 [[package]]
 name = "gradio"
 version = "3.23.0"
     {file = "lit-16.0.0.tar.gz", hash = "sha256:3c4ac372122a1de4a88deb277b956f91b7209420a0bef683b1ab2d2b16dabe11"},
 ]
 [[package]]
 name = "markdown-it-py"
 version = "2.2.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8, <3.11"
+content-hash = "a4f33fc4dbfcec1cb8b7c92cd33de17926044e93e0f1f11a57edfadf4406d6ca"

pyproject.toml CHANGED Viewed

@@ -7,7 +7,6 @@ authors = ["[email protected]"]
 [tool.poetry.dependencies]
 python = "^3.8, <3.11"
 gradio = "^3.23.0"
-logging = "^0.4.9.6"
 numpy = "^1.24.2"
 onnxruntime = "^1.14.1"
 requests = "^2.28.2"

 [tool.poetry.dependencies]
 python = "^3.8, <3.11"
 gradio = "^3.23.0"
 numpy = "^1.24.2"
 onnxruntime = "^1.14.1"
 requests = "^2.28.2"

requirements.txt CHANGED Viewed

The diff for this file is too large to render. See raw diff

requirements_test.txt ADDED Viewed

	@@ -0,0 +1,92 @@

+aiofiles==23.1.0
+aiohttp==3.8.4
+aiosignal==1.3.1
+altair==4.2.2
+anyio==3.6.2
+astroid==2.15.0
+async-timeout==4.0.2
+attrs==22.2.0
+black==23.1.0
+certifi==2022.12.7
+charset-normalizer==3.1.0
+click==8.1.3
+coloredlogs==15.0.1
+configparser==5.3.0
+contourpy==1.0.7
+cycler==0.11.0
+dill==0.3.6
+entrypoints==0.4
+exceptiongroup==1.1.1
+fastapi==0.95.0
+ffmpy==0.3.0
+filelock==3.10.4
+flatbuffers==23.3.3
+fonttools==4.39.2
+frozenlist==1.3.3
+fsspec==2023.3.0
+gradio==3.23.0
+h11==0.14.0
+httpcore==0.16.3
+httpx==0.23.3
+huggingface-hub==0.13.3
+humanfriendly==10.0
+idna==3.4
+iniconfig==2.0.0
+isort==5.12.0
+Jinja2==3.1.2
+jsonschema==4.17.3
+kiwisolver==1.4.4
+lazy-object-proxy==1.9.0
+liccheck==0.8.3
+linkify-it-py==2.0.0
+markdown-it-py==2.2.0
+MarkupSafe==2.1.2
+matplotlib==3.7.1
+mccabe==0.7.0
+mdit-py-plugins==0.3.3
+mdurl==0.1.2
+mpmath==1.3.0
+multidict==6.0.4
+mypy-extensions==1.0.0
+networkx==3.0
+numpy==1.24.2
+onnxruntime==1.14.1
+orjson==3.8.8
+packaging==23.0
+pandas==1.5.3
+pathspec==0.11.1
+Pillow==9.4.0
+platformdirs==3.1.1
+pluggy==1.0.0
+protobuf==4.22.1
+pydantic==1.10.7
+pydub==0.25.1
+pylint==2.17.1
+pyparsing==3.0.9
+pyrsistent==0.19.3
+pytest==7.2.2
+python-dateutil==2.8.2
+python-multipart==0.0.6
+pytz==2022.7.1
+PyYAML==6.0
+requests==2.28.2
+rfc3986==1.5.0
+semantic-version==2.10.0
+six==1.16.0
+sniffio==1.3.0
+starlette==0.26.1
+sympy==1.11.1
+toml==0.10.2
+tomli==2.0.1
+tomlkit==0.11.6
+toolz==0.12.0
+torch==2.0.0
+torchvision==0.15.1
+tqdm==4.65.0
+typing_extensions==4.5.0
+uc-micro-py==1.0.1
+urllib3==1.26.15
+uvicorn==0.21.1
+websockets==10.4
+wrapt==1.15.0
+yarl==1.8.2