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Upload app.py
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app.py
CHANGED
@@ -42,6 +42,11 @@ def instruct_generate(
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top_k: The number of top most probable tokens to consider in the sampling process.
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temperature: A value controlling the randomness of the sampling process. Higher values result in more random
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"""
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output = [prompt, input, max_new_tokens, top_k, temperature]
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print(output)
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return output
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@@ -56,7 +61,9 @@ max_batch_size = 1
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with open(example_path, 'r') as f:
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content = f.read()
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example_dict = json.loads(content)
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def create_instruct_demo():
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with gr.Blocks() as instruct_demo:
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@@ -87,7 +94,7 @@ def create_instruct_demo():
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examples = []
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for example_img_one in examples_img_list:
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scene_name = os.path.basename(example_img_one).split(".")[0]
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example_object_list = example_dict[scene_name]["
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example_instruction = example_dict[scene_name]["instruction"]
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example_one = [example_img_one, example_instruction, example_object_list, 512, 0.8, 200]
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examples.append(example_one)
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top_k: The number of top most probable tokens to consider in the sampling process.
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temperature: A value controlling the randomness of the sampling process. Higher values result in more random
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"""
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scene_name = os.path.basename(img_path).split(".")[0]
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if input in input_value_list:
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input = example_dict[scene_name]["input"]
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if "..." in input:
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input = input.replace("...", "")
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output = [prompt, input, max_new_tokens, top_k, temperature]
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print(output)
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return output
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with open(example_path, 'r') as f:
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content = f.read()
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example_dict = json.loads(content)
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input_value_list = []
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for scene_id, scene_dict in example_dict.items():
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input_value_list.append(scene_dict["input_display"])
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def create_instruct_demo():
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with gr.Blocks() as instruct_demo:
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examples = []
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for example_img_one in examples_img_list:
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scene_name = os.path.basename(example_img_one).split(".")[0]
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example_object_list = example_dict[scene_name]["input_display"]
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example_instruction = example_dict[scene_name]["instruction"]
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example_one = [example_img_one, example_instruction, example_object_list, 512, 0.8, 200]
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examples.append(example_one)
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