Create app.py

app.py

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+import gradio as gr
+import spaces
+from transformers import AutoProcessor, LlavaForConditionalGeneration
+# from qwen_vl_utils import process_vision_info
+import torch
+from PIL import Image
+import subprocess
+from datetime import datetime
+import numpy as np
+import os
+
+os.environ["no_proxy"] = "localhost,127.0.0.1,::1"
+
+
+def array_to_image_path(image_array):
+    # Convert numpy array to PIL Image
+    img = Image.fromarray(np.uint8(image_array))
+    
+    # Generate a unique filename using timestamp
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    filename = f"image_{timestamp}.png"
+    
+    # Save the image
+    img.save(filename)
+    
+    # Get the full path of the saved image
+    full_path = os.path.abspath(filename)
+    
+    return full_path
+    
+
+cuda = 1
+model_id = "huangfx1020/human_llama3_8b"
+models = {
+    "HumanLlaVA-8B": LlavaForConditionalGeneration.from_pretrained("huangfx1020/human_llama3_8b", torch_dtype=torch.float16, low_cpu_mem_usage=True ).to(cuda).eval()
+}
+
+# processors = {
+#     "Qwen/Qwen2-VL-2B-Instruct": AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True)
+# }
+processors = {
+    "HumanLlaVA-8B": AutoProcessor.from_pretrained("huangfx1020/human_llama3_8b")
+}
+DESCRIPTION = "[HumanLlaVA Demo](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct)"
+
+kwargs = {}
+kwargs['torch_dtype'] = torch.bfloat16
+
+
+# @spaces.GPU
+def run_example(image, text_input=None, model_id="HumanLlaVA-8B"):
+    image_path = array_to_image_path(image)
+    
+    print(image_path)
+    model = models[model_id]
+    processor = processors[model_id]
+    raw_image = Image.open(image_path)
+    inputs = processor(images=raw_image, text=prompt, return_tensors='pt').to(cuda, torch.float16)
+
+    # generated_ids = model.generate(**inputs, max_new_tokens=128)
+    # generated_ids_trimmed = [
+    #     out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+    # ]
+    # output_text = processor.batch_decode(
+    #     generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+    # )
+    output = model.generate(**inputs, max_new_tokens=400, do_sample=False)
+    print(output)
+    predict = processor.decode(output[0][:], skip_special_tokens=False)
+    print(predict)
+        
+    return predict
+
+css = """
+  #output {
+    height: 500px; 
+    overflow: auto; 
+    border: 1px solid #ccc; 
+  }
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Tab(label="HumanLlaVA-8B Input"):
+        with gr.Row():
+            with gr.Column():
+                input_img = gr.Image(label="Input Picture")
+                model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value="HumanLlaVA-8B")
+                text_input = gr.Textbox(label="Question")
+                submit_btn = gr.Button(value="Submit")
+            with gr.Column():
+                output_text = gr.Textbox(label="Output Text")
+
+        submit_btn.click(run_example, [input_img, text_input, model_selector], [output_text])
+
+demo.queue(api_open=False)
+demo.launch(debug=True)