fisrt commit

app.py

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+import torch
+from PIL import Image
+import gradio as gr
+import spaces
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+import os
+from threading import Thread
+
+MODEL_LIST = ["THUDM/glm-4v-9b"]
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
+MODEL_ID = os.environ.get("MODEL_ID")
+MODEL_NAME = MODEL_ID.split("/")[-1]
+
+TITLE = f'<br><center>🚀 Coin Generative Recognition</a></center>'
+
+DESCRIPTION = f"""
+<center>
+<p>
+A Space for Vision/Multimodal
+<br>
+<br>
+✨ Tips: Send messages or upload multiple IMAGES at a time.
+<br>
+✨ Tips: Please increase MAX LENGTH when dealing with files.
+<br>
+🤙 Supported Format: png, jpg, webp
+<br>
+🙇‍♂️ May be rebuilding from time to time.
+</p>
+</center>"""
+
+CSS = """
+h1 {
+    text-align: center;
+    display: block;
+}
+img {
+    max-width: 100%; /* Make sure images are not wider than their container */
+    height: auto; /* Maintain aspect ratio */
+    max-height: 300px; /* Limit the height of images */
+}
+"""
+import os
+# Directory where the model and tokenizer will be saved
+
+    
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    trust_remote_code=True
+).to(0)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+model.eval()
+
+
+def merge_images(paths):
+    images = [Image.open(path).convert('RGB') for path in paths]
+    widths, heights = zip(*(i.size for i in images))
+    total_width = sum(widths)
+    max_height = max(heights)
+    new_im = Image.new('RGB', (total_width, max_height))
+    x_offset = 0
+    for im in images:
+        new_im.paste(im, (x_offset,0))
+        x_offset += im.width
+    return new_im
+
+def mode_load(paths):
+    if all(path.lower().endswith(('png', 'jpg', 'jpeg', 'webp')) for path in paths):
+        content = merge_images(paths)
+        choice = "image"
+        return choice, content
+    else:
+        raise gr.Error("Unsupported file types. Please upload only images.")
+
+@spaces.GPU()
+def stream_chat(message, history: list, temperature: float, max_length: int, top_p: float, top_k: int, penalty: float):
+    conversation = []
+    if message["files"]:
+        choice, contents = mode_load(message["files"])
+        conversation.append({"role": "user", "image": contents, "content": message['text']})
+    elif message["files"] and len(message["files"]) == 1:
+        content = Image.open( message["files"][-1]).convert('RGB')
+        choice = "image"
+        conversation.append({"role": "user", "image": content, "content": message['text']})
+    else:
+        raise gr.Error("Please upload one or more images.")
+    input_ids = tokenizer.apply_chat_template(conversation, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)
+    streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        max_length=max_length,
+        streamer=streamer,
+        do_sample=True,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature,
+        repetition_penalty=penalty,
+        eos_token_id=[151329, 151336, 151338],
+    )
+    gen_kwargs = {**input_ids, **generate_kwargs}
+    with torch.no_grad():
+        thread = Thread(target=model.generate, kwargs=gen_kwargs)
+        thread.start()
+        buffer = ""
+        for new_text in streamer:
+            buffer += new_text
+            yield buffer
+
+chatbot = gr.Chatbot(label="Chatbox", height=600, placeholder=DESCRIPTION)
+chat_input = gr.MultimodalTextbox(
+    interactive=True,
+    placeholder="Enter message or upload images...",
+    show_label=False,
+    file_count="multiple",
+)
+
+EXAMPLES = [
+    [{"text": "Give me Country,Denomination and year as json format.", "files": ["./135_back.jpg", "./135_front.jpg"]}],
+    [{"text": "Give me Country,Denomination and year as json format.", "files": ["./141_back.jpg","./141_front.jpg"]}]
+]
+
+with gr.Blocks(css=CSS, theme="soft", fill_height=True) as demo:
+    gr.HTML(TITLE)
+    gr.ChatInterface(
+        fn=stream_chat,
+        multimodal=True,
+        textbox=chat_input,
+        chatbot=chatbot,
+        fill_height=True,
+        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
+        additional_inputs=[
+            gr.Slider(
+                minimum=0,
+                maximum=1,
+                step=0.1,
+                value=0.8,
+                label="Temperature",
+                render=False,
+            ),
+            gr.Slider(
+                minimum=1024,
+                maximum=8192,
+                step=1,
+                value=4096,
+                label="Max Length",
+                render=False,
+            ),
+            gr.Slider(
+                minimum=0.0,
+                maximum=1.0,
+                step=0.1,
+                value=1.0,
+                label="top_p",
+                render=False,
+            ),
+            gr.Slider(
+                minimum=1,
+                maximum=20,
+                step=1,
+                value=10,
+                label="top_k",
+                render=False,
+            ),
+            gr.Slider(
+                minimum=0.0,
+                maximum=2.0,
+                step=0.1,
+                value=1.0,
+                label="Repetition penalty",
+                render=False,
+            ),
+        ],
+    ),
+    gr.Examples(EXAMPLES, [chat_input])
+
+if __name__ == "__main__":
+    demo.queue(api_open=False).launch(show_api=False, share=False)

requirements.txt

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+Pillow
+torch
+torchvision
+transformers
+sentencepiece
+opencv-python
+accelerate
+tiktoken
+PyMuPDF
+python-docx
+python-pptx