Update app.py

app.py

@@ -1,7 +1,218 @@
+import tqdm
+from PIL import Image
+import hashlib
+import torch
+import fitz
+import threading
 import gradio as gr
+import spaces
+import os
+from transformers import AutoModel
+from transformers import AutoTokenizer
+from PIL import Image
+import torch
+import os
+import numpy as np
+import json
 
-def greet(name):
-    return "Hello " + name + "!!"
+cache_dir = '/home/user/data'
+os.makedirs(cache_dir, exist_ok=True)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+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 calculate_md5_from_binary(binary_data):
+    hash_md5 = hashlib.md5()
+    hash_md5.update(binary_data)
+    return hash_md5.hexdigest()
+
+@spaces.GPU(duration=100)
+def add_pdf_gradio(pdf_file_binary, progress=gr.Progress()):
+    global model, tokenizer
+
+    knowledge_base_name = calculate_md5_from_binary(pdf_file_binary)
+    
+    this_cache_dir = os.path.join(cache_dir, knowledge_base_name)
+    os.makedirs(this_cache_dir, exist_ok=True)
+
+    with open(os.path.join(this_cache_dir, f"src.pdf"), 'wb') as file:
+        file.write(pdf_file_binary)
+
+    dpi = 200
+    doc = fitz.open("pdf", pdf_file_binary)
+    
+    reps_list = []
+    images = []
+    image_md5s = []
+
+    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)
+        image_md5s.append(image_md5)
+        with torch.no_grad():
+            reps = model(text=[''], image=[image], tokenizer=tokenizer).reps
+        reps_list.append(reps.squeeze(0).cpu().numpy())
+        images.append(image)
+
+    for idx in range(len(images)):
+        image = images[idx]
+        image_md5 = image_md5s[idx]
+        cache_image_path = os.path.join(this_cache_dir, f"{image_md5}.png")
+        image.save(cache_image_path)
+
+    np.save(os.path.join(this_cache_dir, f"reps.npy"), reps_list)
+
+    with open(os.path.join(this_cache_dir, f"md5s.txt"), 'w') as f:
+        for item in image_md5s:
+            f.write(item+'\n')
+    
+    return knowledge_base_name
+
+# @spaces.GPU
+def retrieve_gradio(knowledge_base: str, query: str, topk: int):
+    global model, tokenizer
+
+    target_cache_dir = os.path.join(cache_dir, knowledge_base)
+
+    if not os.path.exists(target_cache_dir):
+        return None
+    
+    md5s = []
+    with open(os.path.join(target_cache_dir, f"md5s.txt"), 'r') as f:
+        for line in f:
+            md5s.append(line.rstrip('\n'))
+    
+    doc_reps = np.load(os.path.join(target_cache_dir, f"reps.npy"))
+
+    query_with_instruction = "Represent this query for retrieving relevant document: " + query
+    with torch.no_grad():
+        query_rep = model(text=[query_with_instruction], image=[None], tokenizer=tokenizer).reps.squeeze(0).cpu()
+
+    query_md5 = hashlib.md5(query.encode()).hexdigest()
+
+    doc_reps_cat = torch.stack([torch.Tensor(i) for i in 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()
+
+    images_topk = [Image.open(os.path.join(target_cache_dir, f"{md5s[idx]}.png")) for idx in topk_doc_ids_np]
+
+    with open(os.path.join(target_cache_dir, f"q-{query_md5}.json"), 'w') as f:
+        f.write(json.dumps(
+            {
+                "knowledge_base": knowledge_base,
+                "query": query,
+                "retrived_docs": [os.path.join(target_cache_dir, f"{md5s[idx]}.png") for idx in topk_doc_ids_np]
+            }, indent=4, ensure_ascii=False
+        ))
+
+    return images_topk
+
+
+def upvote(knowledge_base, query):
+    global model, tokenizer
+
+    target_cache_dir = os.path.join(cache_dir, knowledge_base)
+
+    query_md5 = hashlib.md5(query.encode()).hexdigest()
+
+    with open(os.path.join(target_cache_dir, f"q-{query_md5}.json"), 'r') as f:
+        data = json.loads(f.read())
+
+    data["user_preference"] = "upvote"
+
+    with open(os.path.join(target_cache_dir, f"q-{query_md5}-withpref.json"), 'w') as f:
+        f.write(json.dumps(data, indent=4, ensure_ascii=False))
+
+    print("up", os.path.join(target_cache_dir, f"q-{query_md5}-withpref.json"))
+
+    gr.Info('Received, babe! Thank you!')
+
+    return
+
+
+def downvote(knowledge_base, query):
+    global model, tokenizer
+
+    target_cache_dir = os.path.join(cache_dir, knowledge_base)
+
+    query_md5 = hashlib.md5(query.encode()).hexdigest()
+
+    with open(os.path.join(target_cache_dir, f"q-{query_md5}.json"), 'r') as f:
+        data = json.loads(f.read())
+
+    data["user_preference"] = "downvote"
+
+    with open(os.path.join(target_cache_dir, f"q-{query_md5}-withpref.json"), 'w') as f:
+        f.write(json.dumps(data, indent=4, ensure_ascii=False))
+
+    print("down", os.path.join(target_cache_dir, f"q-{query_md5}-withpref.json"))
+
+    gr.Info('Received, babe! Thank you!')
+
+    return
+
+
+device = 'cuda' 
+model_path = 'RhapsodyAI/minicpm-visual-embedding-v0' # replace with your local model path
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
+model.to(device)
+
+
+with gr.Blocks() as app:
+    gr.Markdown("# Memex: OCR-free Visual Document Embedding Model as Your Personal Librarian")
+    gr.Markdown("""The model only takes images as document-side inputs and produce vectors representing document pages. Memex is trained with over 200k query-visual document pairs, including textual document, visual document, arxiv figures, plots, charts, industry documents, textbooks, ebooks, and openly-available PDFs, etc. Its performance is on a par with our ablation text embedding model on text-oriented documents, and an advantages on visually-intensive documents.
+Our model is capable of:
+- Help you read a long visually-intensive or text-oriented PDF document and find the pages that answer your question.
+- Help you build a personal library and retireve book pages from a large collection of books.
+- It works like human: read and comprehend with vision and remember multimodal information in hippocampus.""")
+
+    gr.Markdown("- Our model is proudly based on MiniCPM-V series [MiniCPM-V-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6) [MiniCPM-V-2](https://huggingface.co/openbmb/MiniCPM-V-2).")
+    
+    gr.Markdown("- We open-sourced our model at [RhapsodyAI/minicpm-visual-embedding-v0](https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0)")
+    
+    gr.Markdown("- Currently we support PDF document with less than 50 pages, PDF over 50 pages will reach GPU time limit.")
+    
+    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 (Don't click until PDF upload success)")
+    
+    process_button.click(add_pdf_gradio, inputs=[file_input], outputs=file_result)
+
+    with gr.Row():
+        kb_id_input = gr.Text(label="Your Knowledge Base ID (paste your Knowledge Base ID here:)")
+        query_input = gr.Text(label="Your Queston")
+        topk_input = inputs=gr.Number(value=5, minimum=1, maximum=10, step=1, label="Number of pages to retrieve")
+        retrieve_button = gr.Button("Step 1: Retrieve")
+    
+    with gr.Row():
+        downvote_button = gr.Button("🤣Downvote")
+        upvote_button = gr.Button("🤗Upvote")
+    
+    with gr.Row():
+        images_output = gr.Gallery(label="Step 2: Retrieved Pages")
+    
+    retrieve_button.click(retrieve_gradio, inputs=[kb_id_input, query_input, topk_input], outputs=images_output)
+
+    upvote_button.click(upvote, inputs=[kb_id_input, query_input], outputs=None)
+    downvote_button.click(downvote, inputs=[kb_id_input, query_input], outputs=None)
+
+    gr.Markdown("By using this demo, you agree to share your use data with us for research purpose, to help improve user experience.")
+
+
+app.launch()