Create app.py

app.py

@@ -0,0 +1,55 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline
+import torch
+
+MODEL_NAME = "openbmb/MiniCPM-V-4"
+
+@gr.cache(allow_output_mutation=True)
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_NAME,
+        torch_dtype=torch.float16,
+        device_map="auto",
+        trust_remote_code=True
+    )
+    pipeline = TextGenerationPipeline(
+        model=model,
+        tokenizer=tokenizer,
+        device=model.device.index if torch.cuda.is_available() else -1
+    )
+    return pipeline
+
+def generate(prompt: str, max_length: int = 100, top_k: int = 50, top_p: float = 0.95):
+    pipe = load_model()
+    output = pipe(
+        prompt,
+        max_length=max_length,
+        do_sample=True,
+        top_k=top_k,
+        top_p=top_p,
+        num_return_sequences=1
+    )
+    return output[0]["generated_text"]
+
+with gr.Blocks() as demo:
+    gr.Markdown("# MiniCPM-V-4 Text Generation Demo")
+
+    with gr.Row():
+        prompt_input = gr.Textbox(label="Prompt", placeholder="여기에 입력하세요...", lines=2)
+
+    with gr.Row():
+        max_len = gr.Slider(10, 512, value=100, step=10, label="Max Length")
+        topk = gr.Slider(1, 100, value=50, step=1, label="Top-k")
+        topp = gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p")
+
+    generate_btn = gr.Button("Generate")
+    output_box = gr.Textbox(label="Generated Text", lines=5)
+
+    generate_btn.click(
+        fn=generate,
+        inputs=[prompt_input, max_len, topk, topp],
+        outputs=output_box
+    )
+
+demo.launch()