Feature Get the Output.csv

.gitignore

@@ -1 +1,2 @@
-test_images/*
+test_images/*
+*.csv

app.py

@@ -6,29 +6,79 @@ from tkinter import Tk, filedialog
 import gradio as gr
 from pathlib import Path
 from transformers import AutoImageProcessor, AutoModelForImageClassification
+from io import BytesIO
 import torch
 
+
 # Load model
-processor = AutoImageProcessor.from_pretrained("victor/animals-classifier")
-model = AutoModelForImageClassification.from_pretrained("victor/animals-classifier")
-model.eval()
+# processor = AutoImageProcessor.from_pretrained("victor/animals-classifier")
+# model = AutoModelForImageClassification.from_pretrained("victor/animals-classifier")
+# model.eval()
+
+
+def get_file_names(files_):
+    """
+    Get a list of the name of files splitted to get only the proper name
+    Input: Uploaded files
+    Output: ['name of file 1', 'name of file 2']"""
+    return [file.name.split("/")[-1] for file in files_]
 
 
-def upload_file(files):
-    dir_list = [file.name for file in files]
+def get_annotation(files_):
+    """
+    Get the label and accuracy from pretrained (or futur custom model)
+    Input: Uploaded files
+    Output: Df that contains: file_name | label | accuracy
+    """
     df = pd.DataFrame(columns=["file_name", "label", "accuracy"])
-    df["file_name"] = dir_list
-    df["label"] = "incoming"
-    df["accuracy"] = 100
+    df["file_name"] = get_file_names(files_)
+    df["label"] = "incoming"  # TODO
+    df["accuracy"] = 100  # TODO
     return df
 
 
-with gr.Blocks() as demo:
-    upload_button = gr.UploadButton(
-        "Click to Upload a File", file_types=["image", "video"], file_count="multiple"
-    )
+def df_to_csv(df_):
+    """
+    Get the df and convert it as an gradio file output ready for download
+    Input: DF created
+    Output: gr.File()
+    """
+    df_.to_csv("output.csv", index=False)
+    return gr.File(value="output.csv", visible=True)
+
+
+def process_files(files_):
+    """
+    Main function
+    - Get uploaded files
+    - Get annotations # TODO
+    - Get the corresponding df
+    - Get the csv output
+    """
+    df = get_annotation(files_)
+    output_csv = df_to_csv(df)
+    print(df)
+    print(output_csv)
+    print("test")
+    return [df, output_csv]
+
+
+title = "Demo: zero-shot depth estimation with DPT + 3D Point Cloud"
+description = "This demo is a variation from the original <a href='https://huggingface.co/spaces/nielsr/dpt-depth-estimation' target='_blank'>DPT Demo</a>. It uses the DPT model to predict the depth of an image and then uses 3D Point Cloud to create a 3D object."
+
+
+with gr.Blocks() as interface:
+    with gr.Row():
+        upload_btn = gr.UploadButton(
+            "Click to Upload a File",
+            file_types=["image", "video"],
+            file_count="multiple",
+        )
     # file_output = gr.File()
-    output_df = gr.DataFrame()
-    upload_button.upload(upload_file, upload_button, output_df)
+    # output_df = gr.DataFrame()
+    upload_btn.upload(
+        fn=process_files, inputs=upload_btn, outputs=[gr.DataFrame(), gr.Files()]
+    )
+
 
-demo.launch()
+interface.launch(debug=True)