files added

Pipfile

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+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+pytesseract = "*"
+fastapi = "*"
+opencv-python = "*"
+mltu = "*"
+python-multipart = "*"
+uvicorn = "*"
+symspellpy = "*"
+textblob = "*"
+swig = "*"
+happytransformer = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3.11"

configs.yaml

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+batch_size: 32
+height: 96
+learning_rate: 0.0005
+max_text_length: 73
+model_path: ./models/model.onnx
+train_epochs: 1000
+train_workers: 20
+vocab: '''3.FR20JWIe8CyBowxTV5rgOYQ,ipPcqDGnMAK(Eb6)fH:"9LlUt;jsz m4&1#kZ-adNhvu7!S?'
+width: 1408

dockerfile

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+FROM python:3.11
+
+COPY . .
+
+WORKDIR /
+
+RUN pip install --no-cache-dir --upgrade -r /requirements.txt
+
+
+CMD ["uvicorn", "handwritting_fastapi:app", "--host", "0.0.0.0", "--port", "7860"]

handwritting_fastapi.py

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+import cv2
+import io
+import numpy as np
+from PIL import Image
+
+import pytesseract
+
+from fastapi import FastAPI, UploadFile, File
+from fastapi.middleware.cors import CORSMiddleware
+
+from mltu.inferenceModel import OnnxInferenceModel
+from mltu.utils.text_utils import ctc_decoder
+from mltu.transformers import ImageResizer
+from mltu.configs import BaseModelConfigs
+
+from textblob import TextBlob
+from happytransformer import HappyTextToText, TTSettings
+
+configs = BaseModelConfigs.load("./configs.yaml")
+
+happy_tt = HappyTextToText("T5", "vennify/t5-base-grammar-correction")
+
+beam_settings = TTSettings(num_beams=5, min_length=1, max_length=100)
+
+app = FastAPI()
+
+origins = ["*"]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+class ImageToWordModel(OnnxInferenceModel):
+    def __init__(self, char_list, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.char_list = char_list
+
+    def predict(self, image: np.ndarray):
+        image = ImageResizer.resize_maintaining_aspect_ratio(
+            image, *self.input_shape[:2][::-1]
+        )
+
+        image_pred = np.expand_dims(image, axis=0).astype(np.float32)
+
+        preds = self.model.run(None, {self.input_name: image_pred})[0]
+
+        text = ctc_decoder(preds, self.char_list)[0]
+
+        return text
+
+
+model = ImageToWordModel(model_path=configs.model_path, char_list=configs.vocab)
+extracted_text = ""
+
+@app.post("/extract_handwritten_text/")
+async def predict_text(image: UploadFile):
+    global extracted_text
+    # Read the uploaded image
+    img = await image.read()
+    nparr = np.frombuffer(img, np.uint8)
+    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+    # Make a prediction
+    extracted_text = model.predict(img)
+    corrected_text = happy_tt.generate_text(extracted_text, beam_settings)
+
+    return {"text": extracted_text, "corrected_text": corrected_text}
+
+
+@app.post("/extract_text/")
+async def extract_text_from_image(image: UploadFile):
+    global extracted_text
+    # Check if the uploaded file is an image
+    if image.content_type.startswith("image/"):
+        # Read the image from the uploaded file
+        image_bytes = await image.read()
+        img = Image.open(io.BytesIO(image_bytes))
+
+        # Perform OCR on the image
+        extracted_text = pytesseract.image_to_string(img)
+        corrected_text = happy_tt.generate_text(extracted_text, beam_settings)
+
+        return {"text": extracted_text, "corrected_text": corrected_text}
+    else:
+        return {"error": "Invalid file format. Please upload an image."}
+
+from transformers import AutoTokenizer, T5ForConditionalGeneration
+from pydantic import BaseModel
+
+tokenizer = AutoTokenizer.from_pretrained("grammarly/coedit-large")
+chatModel = T5ForConditionalGeneration.from_pretrained("grammarly/coedit-large")
+
+class ChatPrompt(BaseModel):
+    prompt: str
+
+@app.post("/chat_prompt/")
+async def chat_prompt(request: ChatPrompt):
+    global extracted_text
+    input_text = request.prompt + ": " + extracted_text
+    print(input_text)
+    input_ids = tokenizer(input_text, return_tensors="pt").input_ids
+    outputs = chatModel.generate(input_ids, max_length=256)
+    edited_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    return {"edited_text": edited_text}

models/mnist_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e92accaee40ef1e16c8822fb60686fd5c26183b3cf833446c135701bd61344c2
+size 7435768

models/model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3447f46751b438e16020536605597a8508d7cb6f98118b62d3f21258e4be83aa
+size 9718812