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Running
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Zero
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Parent(s):
0afabcf
Fix dat english (#17)
Browse files- Fix dat english (c511212ab3c600c05d6772b2299dd22fbb82baa2)
Co-authored-by: Noa Roggendorff <nroggendorff@users.noreply.huggingface.co>
app.py
CHANGED
@@ -149,34 +149,34 @@ examples_path = os.path.dirname(__file__)
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EXAMPLES = [
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[
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{
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-
"text": "Hi, who are you",
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}
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],
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[
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{
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"text": "Create a Photorealistic image of Eiffel Tower",
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}
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],
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[
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{
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"text": "Read what's written on the paper",
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"files": [f"{examples_path}/example_images/paper_with_text.png"],
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}
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],
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[
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{
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"text": "Identify
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"files": [f"{examples_path}/example_images/elon_smoking.jpg", f"{examples_path}/example_images/steve_jobs.jpg",]
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}
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],
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[
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{
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"text": "Create
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}
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],
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[
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{
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"text": "What is 900
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}
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],
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[
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@@ -187,13 +187,13 @@ EXAMPLES = [
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],
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[
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{
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"text": "
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"files": [f"{examples_path}/example_images/shampoo.jpg"],
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}
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],
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[
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{
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-
"text": "What is formed by the deposition of
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"files": [f"{examples_path}/example_images/ai2d_example.jpeg"],
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}
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],
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@@ -234,8 +234,7 @@ def format_user_prompt_with_im_history_and_system_conditioning(
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user_prompt, chat_history
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) -> List[Dict[str, Union[List, str]]]:
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"""
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-
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-
It handles the potential image(s), the history and the system conditionning.
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"""
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resulting_messages = copy.deepcopy(SYSTEM_PROMPT)
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resulting_images = []
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@@ -316,10 +315,10 @@ def model_inference(
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top_p,
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):
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if user_prompt["text"].strip() == "" and not user_prompt["files"]:
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gr.Error("Please input a query and optionally image(s).")
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if user_prompt["text"].strip() == "" and user_prompt["files"]:
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gr.Error("Please input a text query along the image(s).")
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streamer = TextIteratorStreamer(
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PROCESSOR.tokenizer,
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@@ -417,7 +416,7 @@ decoding_strategy = gr.Radio(
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value="Top P Sampling",
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label="Decoding strategy",
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interactive=True,
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-
info="Higher values
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)
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temperature = gr.Slider(
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minimum=0.0,
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@@ -437,7 +436,7 @@ top_p = gr.Slider(
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visible=True,
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interactive=True,
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label="Top P",
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info="Higher values
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)
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EXAMPLES = [
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[
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{
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+
"text": "Hi, who are you?",
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}
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],
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[
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{
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+
"text": "Create a Photorealistic image of the Eiffel Tower.",
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}
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],
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[
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{
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+
"text": "Read what's written on the paper.",
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"files": [f"{examples_path}/example_images/paper_with_text.png"],
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}
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],
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[
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{
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+
"text": "Identify two famous people in the modern world.",
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"files": [f"{examples_path}/example_images/elon_smoking.jpg", f"{examples_path}/example_images/steve_jobs.jpg",]
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}
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],
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[
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{
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+
"text": "Create five images of supercars, each in a different color.",
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}
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],
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[
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{
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+
"text": "What is 900 multiplied by 900?",
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}
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],
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[
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],
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[
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{
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+
"text": "Create an online ad for this product.",
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"files": [f"{examples_path}/example_images/shampoo.jpg"],
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}
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],
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[
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{
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+
"text": "What is formed by the deposition of the weathered remains of other rocks?",
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"files": [f"{examples_path}/example_images/ai2d_example.jpeg"],
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}
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],
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user_prompt, chat_history
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) -> List[Dict[str, Union[List, str]]]:
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"""
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+
Produce the resulting list that needs to go inside the processor. It handles the potential image(s), the history, and the system conditioning.
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"""
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resulting_messages = copy.deepcopy(SYSTEM_PROMPT)
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resulting_images = []
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top_p,
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):
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if user_prompt["text"].strip() == "" and not user_prompt["files"]:
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+
gr.Error("Please input a query and optionally an image(s).")
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if user_prompt["text"].strip() == "" and user_prompt["files"]:
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+
gr.Error("Please input a text query along with the image(s).")
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streamer = TextIteratorStreamer(
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PROCESSOR.tokenizer,
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value="Top P Sampling",
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label="Decoding strategy",
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interactive=True,
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info="Higher values are equivalent to sampling more low-probability tokens.",
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)
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temperature = gr.Slider(
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minimum=0.0,
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visible=True,
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interactive=True,
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label="Top P",
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info="Higher values are equivalent to sampling more low-probability tokens.",
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)
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