Upload app.py

app.py

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+from transformers import AutoModel, AutoTokenizer, Qwen2VLForConditionalGeneration, AutoProcessor
+import streamlit as st
+import os
+from PIL import Image
+import torch
+import re
+
+@st.cache_resource
+def init_model():
+    tokenizer = AutoTokenizer.from_pretrained('srimanth-d/GOT_CPU', trust_remote_code=True)
+    model = AutoModel.from_pretrained('srimanth-d/GOT_CPU', trust_remote_code=True, use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
+    model = model.eval()
+    return model, tokenizer
+
+# def init_gpu_model():
+#     tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True)
+#     model = AutoModel.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
+#     model = model.eval().cuda()
+#     return model, tokenizer
+
+def init_qwen_model():
+    model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", device_map="cpu", torch_dtype=torch.float16)
+    processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
+    return model, processor
+
+def get_quen_op(image_file, model, processor):
+    try: 
+        image = Image.open(image_file).convert('RGB')
+        conversation = [
+            {
+                "role":"user",
+                "content":[
+                    {
+                        "type":"image",
+                    },
+                    {
+                        "type":"text",
+                        "text":"Extract text from this image."
+                    }
+                ]
+            }
+        ]
+        text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+        inputs = processor(text=[text_prompt], images=[image], padding=True, return_tensors="pt")
+        inputs = {k: v.to(torch.float32) if torch.is_floating_point(v) else v for k, v in inputs.items()}
+
+        generation_config = {
+            "max_new_tokens": 32,
+            "do_sample": False,
+            "top_k": 20,
+            "top_p": 0.90,
+            "temperature": 0.4,
+            "num_return_sequences": 1,
+            "pad_token_id": processor.tokenizer.pad_token_id,
+            "eos_token_id": processor.tokenizer.eos_token_id,
+        }
+
+        output_ids = model.generate(**inputs, **generation_config)
+        if 'input_ids' in inputs:
+                generated_ids = output_ids[:, inputs['input_ids'].shape[1]:]
+        else:
+            generated_ids = output_ids
+            
+        output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+            
+        return output_text[:] if output_text else "No text extracted from the image."
+    
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+@st.cache_data
+def get_text(image_file, _model, _tokenizer):
+    res = _model.chat(_tokenizer, image_file, ocr_type='ocr')
+    return res
+
+def highlight_text(text, search_term):
+    if not search_term:
+        return text
+    pattern = re.compile(re.escape(search_term), re.IGNORECASE)
+    return pattern.sub(lambda m: f'<span style="background-color: yellow;">{m.group()}</span>', text)
+
+st.title("GOT-OCR2.0")
+st.write("Upload an image")
+
+MODEL, PROCESSOR = init_model()
+
+image_file = st.file_uploader("Upload Image", type=['jpg', 'png', 'jpeg'])
+
+if image_file:
+    if not os.path.exists("images"):
+        os.makedirs("images")
+    with open(f"images/{image_file.name}", "wb") as f:
+        f.write(image_file.getbuffer())
+
+    image_file = f"images/{image_file.name}"
+
+    text = get_text(image_file, MODEL, PROCESSOR)
+
+    print(text)
+    
+    # Add search functionality
+    search_term = st.text_input("Enter a word or phrase to search:")
+    highlighted_text = highlight_text(text, search_term)
+    
+    st.markdown(highlighted_text, unsafe_allow_html=True)