Spaces:
Running
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Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
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@@ -8,25 +8,19 @@ from transformers import AutoProcessor, Gemma3ForConditionalGeneration, TextIter
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import spaces
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import time
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TITLE = "
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DESCRIPTION= """
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讘讻诇 驻注诐, 讬讜讜爪专 砖讬专 砖讜谞讛, 讗讝 讗诐 诇讗 讗讛讘转诐, 讗驻砖专 诇谞住讜转 砖讜讘 注诐 讗讜转讜 讛驻专讜诪驻讟
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[讛诪讜讚诇 讝诪讬谉 诇讛讜专讚讛](https://huggingface.co/Norod78/gemma-3_4b_hebrew-lyrics-finetune)
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讛诪讜讚诇 讻旨讜旨讬址旨讬诇 注状讬 [讚讜专讜谉 讗讚诇专](https://linktr.ee/Norod78)
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"""
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# model config
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device_map="auto",
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torch_dtype=torch.bfloat16
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).eval()
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processor_4b = AutoProcessor.from_pretrained(
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# I will add timestamp later
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def extract_video_frames(video_path, num_frames=8):
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cap = cv2.VideoCapture(video_path)
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@@ -90,31 +84,6 @@ def format_conversation_history(chat_history):
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@spaces.GPU(duration=120)
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def generate_response(input_data, chat_history, max_new_tokens, system_prompt, temperature, top_p, top_k, repetition_penalty):
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"""
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Creates silly song lyrics in Hebrew based on user input and conversation history.
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Args:
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input_data (dict or str):
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- If dict: must include 'text' (str) and optional 'files' (list of image/video file paths).
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- If str: treated as plain text input.
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chat_history (list of dict):
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Sequence of past messages, each with keys 'role' and 'content'.
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max_new_tokens (int):
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Maximum number of tokens to generate for the response.
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system_prompt (str):
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Optional system-level instruction to guide the style and content of the response.
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temperature (float):
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Sampling temperature; higher values yield more diverse outputs.
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top_p (float):
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Nucleus sampling threshold for cumulative probability selection.
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top_k (int):
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Limits sampling to the top_k most likely tokens at each step.
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repetition_penalty (float):
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Penalty factor to discourage the model from repeating the same tokens.
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Yields:
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str: Streaming chunks of the generated Hebrew song lyrics in real time.
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"""
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if isinstance(input_data, dict) and "text" in input_data:
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text = input_data["text"]
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@@ -132,7 +101,7 @@ def generate_response(input_data, chat_history, max_new_tokens, system_prompt, t
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messages[-1]["content"].extend(new_message["content"])
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else:
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messages.append(new_message)
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model =
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processor = processor_4b
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inputs = processor.apply_chat_template(
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messages,
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@@ -167,10 +136,10 @@ chat_interface = gr.ChatInterface(
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gr.Slider(label="Max new tokens", minimum=100, maximum=2000, step=1, value=512),
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gr.Textbox(
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label="System Prompt",
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value="
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lines=4,
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placeholder="
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text_align = '
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),
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gr.Slider(label="Temperature", minimum=0.1, maximum=2.0, step=0.1, value=0.2),
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gr.Slider(label="Top-p", minimum=0.05, maximum=1.0, step=0.05, value=0.4),
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@@ -178,20 +147,19 @@ chat_interface = gr.ChatInterface(
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gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.1),
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],
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examples=[
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[{"text": "
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[{"text": "转驻讜讞 讗讚诪讛 注诐 讞专讚讛 讞讘专转讬转"}]
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],
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textbox=gr.MultimodalTextbox(
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rtl=
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label="拽诇讟",
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file_types=["image", "video"],
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file_count="multiple",
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placeholder="
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),
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cache_examples=False,
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type="messages",
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fill_height=True,
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stop_btn="
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css_paths=["style.css"],
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multimodal=True,
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title=TITLE,
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import spaces
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import time
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TITLE = " google/gemma-3-270m-it "
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DESCRIPTION= """
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It's so small
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"""
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# model config
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model_270m_name = "google/gemma-3-270m-it"
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model_270m = Gemma3ForConditionalGeneration.from_pretrained(
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model_270m_name,
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device_map="auto",
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torch_dtype=torch.bfloat16
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).eval()
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processor_4b = AutoProcessor.from_pretrained(model_270m_name)
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# I will add timestamp later
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def extract_video_frames(video_path, num_frames=8):
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cap = cv2.VideoCapture(video_path)
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@spaces.GPU(duration=120)
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def generate_response(input_data, chat_history, max_new_tokens, system_prompt, temperature, top_p, top_k, repetition_penalty):
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if isinstance(input_data, dict) and "text" in input_data:
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text = input_data["text"]
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messages[-1]["content"].extend(new_message["content"])
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else:
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messages.append(new_message)
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model = model_270m
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processor = processor_4b
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inputs = processor.apply_chat_template(
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messages,
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gr.Slider(label="Max new tokens", minimum=100, maximum=2000, step=1, value=512),
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gr.Textbox(
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label="System Prompt",
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value="You are a very helpful multimodal assistant",
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lines=4,
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placeholder="Change the settings",
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text_align = 'left', rtl = False
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),
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gr.Slider(label="Temperature", minimum=0.1, maximum=2.0, step=0.1, value=0.2),
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gr.Slider(label="Top-p", minimum=0.05, maximum=1.0, step=0.05, value=0.4),
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gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.1),
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],
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examples=[
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[{"text": "Write a poem which describes this image", "files": ["examples/image1.jpg"]}],
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],
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textbox=gr.MultimodalTextbox(
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rtl=False,
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label="拽诇讟",
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file_types=["image", "video"],
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file_count="multiple",
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placeholder="Input text, image or video",
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),
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cache_examples=False,
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type="messages",
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fill_height=True,
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stop_btn="Stop",
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css_paths=["style.css"],
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multimodal=True,
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title=TITLE,
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