Upload app.py

app.py

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+import sys
+import time
+import warnings
+from pathlib import Path
+
+
+# 配置hugface环境
+from huggingface_hub import hf_hub_download
+import gradio as gr
+import os
+import glob
+import json
+
+# os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
+# torch.set_float32_matmul_precision("high")
+
+
+
+def instruct_generate(
+    img_path: str = " ",
+    prompt: str = "What food do lamas eat?",
+    input: str = "",
+    max_new_tokens: int = 100,
+    top_k: int = 200,
+    temperature: float = 0.8,
+) -> None:
+    """Generates a response based on a given instruction and an optional input.
+    This script will only work with checkpoints from the instruction-tuned LLaMA-Adapter model.
+    See `finetune_adapter.py`.
+
+    Args:
+        prompt: The prompt/instruction (Alpaca style).
+        adapter_path: Path to the checkpoint with trained adapter weights, which are the output of
+            `finetune_adapter.py`.
+        input: Optional input (Alpaca style).
+        pretrained_path: The path to the checkpoint with pretrained LLaMA weights.
+        tokenizer_path: The tokenizer path to load.
+        quantize: Whether to quantize the model and using which method:
+            ``"llm.int8"``: LLM.int8() mode,
+            ``"gptq.int4"``: GPTQ 4-bit mode.
+        max_new_tokens: The number of generation steps to take.
+        top_k: The number of top most probable tokens to consider in the sampling process.
+        temperature: A value controlling the randomness of the sampling process. Higher values result in more random
+    """
+    output = [prompt, input, max_new_tokens, top_k, temperature]
+    print(output)
+    return output
+
+# 配置具体参数
+
+example_path = "example.json"
+# 1024如果不够, 调整为512
+max_seq_len = 1024
+max_batch_size = 1
+
+with open(example_path, 'r') as f:
+    content = f.read()
+    example_dict = json.loads(content)
+
+
+def create_instruct_demo():
+    with gr.Blocks() as instruct_demo:
+        with gr.Row():
+            with gr.Column():
+                scene_img = gr.Image(label='Scene', type='filepath')
+                object_list = gr.Textbox(
+                    lines=2, label="Input")
+
+                instruction = gr.Textbox(
+                    lines=2, label="Instruction")
+                max_len = gr.Slider(minimum=1, maximum=512,
+                                    value=128, label="Max length")
+                with gr.Accordion(label='Advanced options', open=False):
+                    temp = gr.Slider(minimum=0, maximum=1,
+                                     value=0.8, label="Temperature")
+                    top_k = gr.Slider(minimum=100, maximum=300,
+                                      value=200, label="Top k")
+
+                run_botton = gr.Button("Run")
+
+            with gr.Column():
+                outputs = gr.Textbox(lines=10, label="Output")
+
+        inputs = [instruction, object_list, max_len, top_k, temp]
+
+        # 接下来设定具体的example格式
+        examples_img_list = glob.glob("caption_demo/*.png")
+        examples = []
+        for example_img_one in examples_img_list:
+            scene_name = os.path.basename(example_img_one).split(".")[0]
+            example_object_list = example_dict[scene_name]["input"]
+            example_instruction = example_dict[scene_name]["instruction"]
+            example_one = [example_img_one, example_object_list, example_instruction, 512, 0.8, 200]
+            examples.append(example_one)
+
+        gr.Examples(
+            examples=examples,
+            inputs=inputs,
+            outputs=outputs,
+            fn=instruct_generate,
+            cache_examples=os.getenv('SYSTEM') == 'spaces'
+        )
+        run_botton.click(fn=instruct_generate, inputs=inputs, outputs=outputs)
+    return instruct_demo
+
+
+# Please refer to our [arXiv paper](https://arxiv.org/abs/2303.16199) and [github](https://github.com/ZrrSkywalker/LLaMA-Adapter) for more details.
+description = """
+# TaPA
+The official demo for **Embodied Task Planning with Large Language Models**.
+"""
+
+with gr.Blocks(css='style.css') as demo:
+    gr.Markdown(description)
+    with gr.TabItem("Instruction-Following"):
+        create_instruct_demo()
+
+demo.queue(api_open=True, concurrency_count=1).launch()
+
+