First commit.

app.py

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+# Reference:
+# https://huggingface.co/spaces/Sagar23p/mistralAI_chatBoat
+
+import gradio as gr
+from paddleocr import PaddleOCR, draw_ocr
+import asyncio
+import requests
+from huggingface_hub import InferenceClient
+import os
+
+API_TOKEN = os.environ.get('HUGGINGFACE_API_KEY')
+
+API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
+headers = {"Authorization": "Bearer " +API_TOKEN}
+
+
+def query(question):
+    client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct", headers=headers)
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a helpful and honest assistant. Please, respond concisely and truthfully.",
+        },
+        {
+            "role": "user",
+            "content": question,
+        },
+    ]
+    output = client.chat_completion(messages, model="meta-llama/Meta-Llama-3-8B-Instruct", max_tokens=1000)  
+    if output.choices[0].message['content'].find('Yes')>=0:
+        messages+=[output.choices[0].message]
+        messages+=[{"role": "user",
+            "content": "What is the mistake and what is the correct sentence?"}]
+        output = client.chat_completion(messages, model="meta-llama/Meta-Llama-3-8B-Instruct", max_tokens=1000)  
+    return output.choices[0].message['content']
+
+def image2Text(image:str, langChoice:str):
+    ocr = PaddleOCR(use_angle_cls=True, lang=langChoice)  # need to run only once to download and load model into memory
+    img_path = image
+    result = ocr.ocr(img_path, cls=True)
+    text = ""
+    for idx in range(len(result)):
+        res = result[idx]
+        for line in res:
+            import re
+            # remove pinyin if it's Chinese
+            if langChoice=="ch":
+                #t = re.sub('[a-z0-9.]', '', line[1][0])
+                t = re.sub('[a-z]', '', line[1][0])
+                t = re.sub('[0-9]\.', '', t)
+                t = t.replace(" ", "")
+                t = t.replace("（）", "")
+                t = t.replace("（)", "")
+                t = t.replace("( )", "")
+                t = t.replace("()", "")            
+                if t!="":
+                    text +=((t) + "\n")             
+            else:
+                print(line)
+                t = line[1][0]
+                t = re.sub('Term [0-9] Spelling', '', t)
+                t = re.sub('Page [0-9]', '', t)
+                if t!="":
+                    text += (t + "\n")
+    
+    text = text.replace("\n"," ").replace(".",".\n")
+                            
+    return text
+
+def text2PrevMistake(recognized_text, langChoice:str, current_line, session_data):
+    if len(session_data) == 0 or session_data[0] == 0 or session_data[0] == 1:
+        session_data = []
+    else:
+        session_data = [session_data[0]-2]
+
+    return text2NextMistake(recognized_text, langChoice, current_line, session_data)
+    
+def text2NextMistake(recognized_text, langChoice:str, current_line, session_data):
+
+    lines = recognized_text.split("\n")
+    while 1:
+        if len(lines) == 0:
+            return current_line, "No mistake. Empty text.", session_data
+        elif len(session_data) == 0:
+            session_data = [0]
+            current_line = lines[session_data[0]]
+        elif session_data[0] + 1 >= len(lines):
+            session_data = []
+            return current_line, "No more mistake. End of text", session_data
+        else:
+            session_data = [session_data[0]+1]
+            current_line = lines[session_data[0]]
+            
+        question = f"Only answer Yes or No. Is there grammatical or logical mistake in the sentence: {current_line}"
+        correction_text = query(question)
+
+        if correction_text.find("No") == 0:
+            continue
+        else:
+            break
+            
+    return current_line, correction_text, session_data
+    
+with gr.Blocks() as demo:
+    gr.HTML("""<h1 align="center">Composition Corrector</h1>""")
+    session_data = gr.State([])
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            upload_image = gr.Image(height=400,width=400, value = "compo.jpg")
+            langChoice = gr.Radio(["en", "ch"], value="en", label="Select lanaguage: 'ch' for Chinese, 'en' for English", info="")
+        with gr.Column(scale=3):
+            recognized_text = gr.Textbox(show_label=False, placeholder="composition", lines=15)
+    toText = gr.Button("Convert image to text")
+
+    current_line = gr.Textbox(show_label=False, placeholder="current line", lines=1)
+    correction_text = gr.Textbox(show_label=False, placeholder="corrections...", lines=15)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            toPrevMistake = gr.Button("Find prev mistake", variant="primary")
+        with gr.Column(scale=1):
+            toNextMistake = gr.Button("Find next mistake", variant="primary")
+    
+
+    
+    toText.click(
+        image2Text,
+        [upload_image, langChoice],
+        [recognized_text],
+        #show_progress=True,
+
+    )
+    toNextMistake.click(text2NextMistake , [recognized_text, langChoice, current_line, session_data], [current_line, correction_text, session_data])
+    toPrevMistake.click(text2PrevMistake , [recognized_text, langChoice, current_line, session_data], [current_line, correction_text, session_data])
+
+demo.queue().launch(share=False, inbrowser=True)

requirements.txt

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+gradio
+paddlepaddle
+paddleocr