upload app file and requirements

app.py

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+import gradio as gr
+from PIL import Image
+import pytesseract
+
+import yolov5
+
+# Pytesseract init
+choices = pytesseract.get_languages(config='')
+
+def text_inference(filepath, languages):
+
+    return pytesseract.image_to_string(Image.open(filepath), lang=', '.join(languages))
+
+# YOLOv5 Init
+model = yolov5.load('nickgambirasi/yolov5s-recycling')
+
+model.conf = 0.2
+model.iou = 0.45
+model.agnostic = False
+model.multi_label = False
+model.max_det = 1000
+
+def yolo_inference(image):
+
+    results = model(Image.open(image), size=640)
+    predictions = results.pred[0]
+
+    scores = predictions[:, 4]
+
+    return scores
+
+def full_inference(image, languages):
+
+    text_infer = text_inference(image, languages)
+    symbol_infer = yolo_inference(image)
+
+    output = {}
+
+    if text_infer:
+
+        output.update({'text': 'all_pass'})
+
+    if any(symbol_infer > model.conf):
+
+        output.update({'symbols': 'all_pass'})
+
+    return output
+
+title = "Hyperintelligent Label Parser"
+description = "Gradio deployment of models for text and symbol detection on product labels, powered by Tesseract and YOLOv5"
+gr.Interface(
+    full_inference,
+    [gr.inputs.Image(type="filepath", label="Upload Your Label"), gr.inputs.CheckboxGroup(choices, type="value", default=['eng'], label="Language of Text")],
+    'text',
+    title=title,
+    description=description,
+    article=None,
+    examples=None
+).launch(enable_queue=True)

packages.txt

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+tesseract-ocr-all

requirements.txt

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+gradio
+jinja2
+pytesseract