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Update app.py
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app.py
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@@ -1,6 +1,6 @@
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import gradio as gr
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from transformers import
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from
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import torch
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import pandas as pd
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import pytesseract
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@@ -9,19 +9,16 @@ import cv2
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# Set Tesseract command (only works if Tesseract is already installed on the hosting server)
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pytesseract.pytesseract_cmd = r'/usr/bin/tesseract'
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#
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processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct-AWQ")
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except Exception as e:
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print(f"Error loading model: {e}")
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# Preprocessing image for OCR
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def preprocess_image(image_path):
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@@ -53,11 +50,10 @@ def process_image(image_path):
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}]
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# Process the vision info and prepare inputs
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text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt")
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inputs = inputs.to(device)
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generated_ids = model.generate(**inputs, max_new_tokens=128)
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output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
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import gradio as gr
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from transformers import AutoModelForConditionalGeneration, AutoProcessor
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from huggingface_hub import hf_api
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import torch
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import pandas as pd
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import pytesseract
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# Set Tesseract command (only works if Tesseract is already installed on the hosting server)
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pytesseract.pytesseract_cmd = r'/usr/bin/tesseract'
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# Initialize the model and processor from Hugging Face Hub
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model_name = "Qwen/Qwen2-VL-2B-Instruct-AWQ"
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model = AutoModelForConditionalGeneration.from_pretrained(
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model_name,
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torch_dtype="auto"
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)
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model.to("cpu")
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processor = AutoProcessor.from_pretrained(model_name)
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# Preprocessing image for OCR
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def preprocess_image(image_path):
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]
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}]
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text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt")
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inputs = inputs.to(model.device)
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generated_ids = model.generate(**inputs, max_new_tokens=128)
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output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
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