upload application

app.py

@@ -0,0 +1,40 @@
+import gradio as gr
+import os
+from utils import extraire_informations_carte,make_prediction
+
+def predict(img):
+    proba, document_type = make_prediction(img)
+    if proba < .98:
+        return "Ce document ne fait pas partir de ceux pris en charge actuelement (Nouvelle et Ancienne CNI, Permis de conduire)"
+    else :
+        doc_type = document_type +1
+        result = extraire_informations_carte(img,doc_type)
+        return result
+
+image = gr.components.Image(type = "filepath")
+#type_document = gr.components.Dropdown(["Nouvelle_CNI","ANCIENNE_CNI","PERMIS_DE_CONDUITE"])
+out_lab = gr.components.Textbox()
+
+### 4. Gradio app ###
+# Create title, description and article strings
+title = "OCR FOR IMAGES ANALYSIS"
+description = "WE USE OCR TO EXTRACT INFORMATIONS FROM DIFFERENT TYPES OF DOCUMENTS AND FORMALIZE THE RESULT INTO JSON."
+article = "Created by data354."
+
+# Create examples list from "examples/" directory
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+print(example_list)
+#[gr.Label(label="Predictions"), # what are the outputs?
+#gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
+# Create examples list from "examples/" directory
+# Create the Gradio demo
+demo = gr.Interface(fn=predict, # mapping function from input to output
+                    inputs= image, #gr.Image(type="pil"), # what are the inputs?
+                    outputs=out_lab, #[list of outputs]
+                    examples=example_list, 
+                    title=title,
+                    description=description,
+                    article=article
+                   )
+# Launch the demo!
+demo.launch(debug = True)