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Browse files- app.py +95 -0
- requirements.txt +4 -0
    	
        app.py
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            import gradio as gr
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            from easyocr import Reader
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            from PIL import Image
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            import io
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            import json
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            import csv
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            import openai
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            openai.api_key = "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"
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            reader = Reader(["tr"])
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            def get_text(input_img):
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                result = reader.readtext(input_img, detail=0)
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                return " ".join(result)
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            def save_csv(mahalle, il, sokak, apartman):
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                adres_full = [mahalle, il, sokak, apartman]
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                with open("adress_book.csv", "a", encoding="utf-8") as f:
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                    write = csv.writer(f)
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                    write.writerow(adres_full)
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                return adres_full
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            def get_json(mahalle, il, sokak, apartman):
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                adres = {"mahalle": mahalle, "il": il, "sokak": sokak, "apartman": apartman}
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                dump = json.dumps(adres, indent=4, ensure_ascii=False)
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                return dump
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            def openai_response(ocr_input):
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                prompt = f"""Address Extractor
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                You are a highly intelligent and accurate address  extractor from plain text input and especially from emergency text that carries address information, your inputs can be text of arbitrary size, but the output should be in [{{'tabular':{{'entity_type': 'entity'}} }}] JSON format
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                Examples:
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                Input: Deprem sırasında evimizde yer alan adresimiz: İstanbul, Beşiktaş, Yıldız Mahallesi, Cumhuriyet Caddesi No: 35, cep telefonu numaram 5551231256, adim Ahmet Yilmaz
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                Output: 'Sehir:İstanbul', 'Ilce:Beşiktaş', 'Mahalle:Yıldız Mahallesi', 'Cadde: Cumhuriyet Caddesi', 'Apartman:no:35', 'Telefon: 5551231256', 'isim:Ahmet Yılmaz'
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                Input: {ocr_input}
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                Output:"""
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                response = openai.Completion.create(
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                model="text-davinci-003",
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                prompt=prompt,
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                temperature=0,
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                max_tokens=100,
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                top_p=1,
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                frequency_penalty=0.0,
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                presence_penalty=0.0,
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                stop=["\n"],
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            )
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                return response["choices"][0]['text']
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            with gr.Blocks() as demo:
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                gr.Markdown(""" # Image to Text - Adres""")
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                with gr.Row():
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                    img_area = gr.Image()
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                    ocr_result = gr.Textbox(label="OCR")
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                    open_api_text = gr.Textbox(label="OPENAI")
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                submit_button = gr.Button()
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                submit_button.click(get_text, img_area, ocr_result)
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                ocr_result.change(openai_response, ocr_result, open_api_text)
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                with gr.Column():
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                    with gr.Row():
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                        mahalle = gr.Textbox(label="mahalle")
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                        sokak = gr.Textbox(label="sokak")
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                    with gr.Row():
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                        apartman = gr.Textbox(label="apartman")
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                        il = gr.Textbox(label="il")
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                    tarif = gr.Textbox(label="Tarif")
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                json_out = gr.Textbox()
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                csv_out = gr.Textbox()
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                adres_submit = gr.Button()
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                adres_submit.click(get_json, [mahalle, il, sokak, apartman], json_out)
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                adres_submit.click(save_csv, [mahalle, il, sokak, apartman], csv_out)
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            if __name__ == "__main__":
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                demo.launch()
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        requirements.txt
    ADDED
    
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            +
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            Pillow
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            easyocr
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            gradio
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