Delete pipeline_gradio.py

pipeline_gradio.py

@@ -1,165 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-#
-# Copyright @2023 RhapsodyAI
-#
-# @author: bokai xu <[email protected]>
-# @date: 2024/07/13
-#
-
-
-import tqdm
-from PIL import Image
-import hashlib
-import torch
-import fitz
-import threading
-import gradio as gr
-
-
-def get_image_md5(img: Image.Image):
-    img_byte_array = img.tobytes()
-    hash_md5 = hashlib.md5()
-    hash_md5.update(img_byte_array)
-    hex_digest = hash_md5.hexdigest()
-    return hex_digest
-
-def pdf_to_images(pdf_path, dpi=100):
-    doc = fitz.open(pdf_path)
-    images = []
-    for page in tqdm.tqdm(doc):
-        pix = page.get_pixmap(dpi=dpi)
-        img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
-        images.append(img)
-    return images
-
-def calculate_md5_from_binary(binary_data):
-    hash_md5 = hashlib.md5()
-    hash_md5.update(binary_data)
-    return hash_md5.hexdigest()
-
-class PDFVisualRetrieval:
-    def __init__(self, model, tokenizer):
-        self.tokenizer = tokenizer
-        self.model = model
-        self.reps = {}
-        self.images = {}
-        self.lock = threading.Lock()
-    
-    def retrieve(self, knowledge_base: str, query: str, topk: int):
-        doc_reps = list(self.reps[knowledge_base].values())
-        query_with_instruction = "Represent this query for retrieving relavant document: " + query
-        with torch.no_grad():
-            query_rep = self.model(text=[query_with_instruction], image=[None], tokenizer=self.tokenizer).reps.squeeze(0)
-        doc_reps_cat = torch.stack(doc_reps, dim=0)
-        similarities = torch.matmul(query_rep, doc_reps_cat.T)
-        topk_values, topk_doc_ids = torch.topk(similarities, k=topk)
-        topk_values_np = topk_values.cpu().numpy()
-        topk_doc_ids_np = topk_doc_ids.cpu().numpy()
-        similarities_np = similarities.cpu().numpy()
-        all_images_doc_list = list(self.images[knowledge_base].values())
-        images_topk = [all_images_doc_list[idx] for idx in topk_doc_ids_np]
-        return topk_doc_ids_np, topk_values_np, images_topk
-    
-    def add_pdf(self, knowledge_base_name: str, pdf_file_path: str, dpi: int = 100):
-        if knowledge_base_name not in self.reps:
-            self.reps[knowledge_base_name] = {}
-        if knowledge_base_name not in self.images:
-            self.images[knowledge_base_name] = {}
-        doc = fitz.open(pdf_file_path)
-        print("model encoding images..")
-        for page in tqdm.tqdm(doc):
-            pix = page.get_pixmap(dpi=dpi)
-            image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
-            image_md5 = get_image_md5(image)
-            with torch.no_grad():
-                reps = self.model(text=[''], image=[image], tokenizer=self.tokenizer).reps
-            self.reps[knowledge_base_name][image_md5] = reps.squeeze(0)
-            self.images[knowledge_base_name][image_md5] = image
-        return
-
-    def add_pdf_gradio(self, pdf_file_binary, progress=gr.Progress()):
-        knowledge_base_name = calculate_md5_from_binary(pdf_file_binary)
-        if knowledge_base_name not in self.reps:
-            self.reps[knowledge_base_name] = {}
-        else:
-            return knowledge_base_name
-        if knowledge_base_name not in self.images:
-            self.images[knowledge_base_name] = {}
-        dpi = 100
-        doc = fitz.open("pdf", pdf_file_binary)
-        for page in progress.tqdm(doc):
-            with self.lock: # because we hope one 16G gpu only process one image at the same time
-                pix = page.get_pixmap(dpi=dpi)
-                image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
-                image_md5 = get_image_md5(image)
-                with torch.no_grad():
-                    reps = self.model(text=[''], image=[image], tokenizer=self.tokenizer).reps
-                self.reps[knowledge_base_name][image_md5] = reps.squeeze(0)
-                self.images[knowledge_base_name][image_md5] = image
-        return knowledge_base_name
-
-    def retrieve_gradio(self, knowledge_base: str, query: str, topk: int):
-        doc_reps = list(self.reps[knowledge_base].values())
-        query_with_instruction = "Represent this query for retrieving relavant document: " + query
-        with torch.no_grad():
-            query_rep = self.model(text=[query_with_instruction], image=[None], tokenizer=self.tokenizer).reps.squeeze(0)
-        doc_reps_cat = torch.stack(doc_reps, dim=0)
-        similarities = torch.matmul(query_rep, doc_reps_cat.T)
-        topk_values, topk_doc_ids = torch.topk(similarities, k=topk)
-        topk_values_np = topk_values.cpu().numpy()
-        topk_doc_ids_np = topk_doc_ids.cpu().numpy()
-        similarities_np = similarities.cpu().numpy()
-        all_images_doc_list = list(self.images[knowledge_base].values())
-        images_topk = [all_images_doc_list[idx] for idx in topk_doc_ids_np]
-        return images_topk
-
-
-if __name__ == "__main__":
-    from transformers import AutoModel
-    from transformers import AutoTokenizer
-    from PIL import Image
-    import torch
-    
-    device = 'cuda:0'
-    
-    # Load model, be sure to substitute `model_path` by your model path 
-    model_path = '/home/jeeves/xubokai/minicpm-visual-embedding-v0' # replace with your local model path
-    # pdf_path = "/home/jeeves/xubokai/minicpm-visual-embedding-v0/2406.07422v1.pdf"
-    
-    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-    model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
-    model.to(device)
-    
-    retriever = PDFVisualRetrieval(model=model, tokenizer=tokenizer)
-    
-    # topk_doc_ids_np, topk_values_np, images_topk = retriever.retrieve(knowledge_base='test', query='what is the number of VQ of this kind of codec method?', topk=1)
-    # # 2
-    # topk_doc_ids_np, topk_values_np, images_topk = retriever.retrieve(knowledge_base='test', query='the training loss curve of this paper?', topk=1)
-    # # 3
-    # topk_doc_ids_np, topk_values_np, images_topk = retriever.retrieve(knowledge_base='test', query='the experiment table?', topk=1)
-    # # 2
-    
-    with gr.Blocks() as app:
-        gr.Markdown("# Memex: OCR-free Visual Document Retrieval @RhapsodyAI")
-        
-        with gr.Row():
-            file_input = gr.File(type="binary", label="Upload PDF")
-            file_result = gr.Text(label="Knowledge Base ID (remember this!)")
-            process_button = gr.Button("Process PDF")
-        
-        process_button.click(retriever.add_pdf_gradio, inputs=[file_input], outputs=file_result)
-
-        with gr.Row():
-            kb_id_input = gr.Text(label="Your Knowledge Base ID")
-            query_input = gr.Text(label="Your Queston")
-            topk_input = inputs=gr.Number(value=1, minimum=1, maximum=5, step=1, label="Top K")
-            retrieve_button = gr.Button("Retrieve")
-        
-        with gr.Row():
-            images_output = gr.Gallery(label="Retrieved Pages")
-        
-        retrieve_button.click(retriever.retrieve_gradio, inputs=[kb_id_input, query_input, topk_input], outputs=images_output)
-
-    app.launch()
-