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Update app.py

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  1. app.py +125 -46
app.py CHANGED
@@ -1,64 +1,143 @@
1
  import gradio as gr
2
- from huggingface_hub import InferenceClient
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3
 
4
- """
5
- For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
6
- """
7
- client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
8
 
 
 
 
 
 
 
 
 
 
9
 
10
- def respond(
11
- message,
12
- history: list[tuple[str, str]],
13
- system_message,
14
- max_tokens,
15
- temperature,
16
- top_p,
17
- ):
18
- messages = [{"role": "system", "content": system_message}]
19
 
20
- for val in history:
21
- if val[0]:
22
- messages.append({"role": "user", "content": val[0]})
23
- if val[1]:
24
- messages.append({"role": "assistant", "content": val[1]})
25
 
26
- messages.append({"role": "user", "content": message})
27
 
28
- response = ""
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
29
 
30
- for message in client.chat_completion(
31
- messages,
32
- max_tokens=max_tokens,
33
- stream=True,
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
34
  temperature=temperature,
35
- top_p=top_p,
36
- ):
37
- token = message.choices[0].delta.content
 
 
 
 
 
38
 
39
- response += token
40
- yield response
 
 
 
 
41
 
42
 
43
- """
44
- For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
45
- """
46
  demo = gr.ChatInterface(
47
- respond,
 
 
 
 
48
  additional_inputs=[
49
- gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
50
- gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
51
- gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
52
  gr.Slider(
53
- minimum=0.1,
54
- maximum=1.0,
55
- value=0.95,
56
- step=0.05,
57
- label="Top-p (nucleus sampling)",
 
 
 
 
 
58
  ),
59
  ],
 
 
 
60
  )
61
-
62
-
63
- if __name__ == "__main__":
64
- demo.launch()
 
1
  import gradio as gr
2
+ import torch
3
+ from transformers import (
4
+ AutoModelForCausalLM,
5
+ AutoTokenizer,
6
+ TextIteratorStreamer,
7
+ )
8
+ import os
9
+ from threading import Thread
10
+ import spaces
11
+ import time
12
+ import subprocess
13
+
14
+ subprocess.run(
15
+ "pip install flash-attn --no-build-isolation",
16
+ env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
17
+ shell=True,
18
+ )
19
 
20
+ token = os.environ["HF_TOKEN"]
 
 
 
21
 
22
+ model = AutoModelForCausalLM.from_pretrained(
23
+ "microsoft/Phi-3-mini-128k-instruct",
24
+ token=token,
25
+ trust_remote_code=True,
26
+ )
27
+ tok = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct", token=token)
28
+ terminators = [
29
+ tok.eos_token_id,
30
+ ]
31
 
32
+ if torch.cuda.is_available():
33
+ device = torch.device("cuda")
34
+ print(f"Using GPU: {torch.cuda.get_device_name(device)}")
35
+ else:
36
+ device = torch.device("cpu")
37
+ print("Using CPU")
 
 
 
38
 
39
+ model = model.to(device)
 
 
 
 
40
 
41
+ # Dispatch Errors
42
 
43
+ @spaces.GPU(duration=60)
44
+ def chat(message, history, temperature, do_sample, max_tokens):
45
+ # ๅฎš็พฉ PTZ ๆŽงๅˆถๅŠฉๆ‰‹็š„ prompt
46
+ prompt = (
47
+ "You are an assistant for controlling PTZ cameras.\n"
48
+ "When the user gives you a clear command, please JUST respond in the following format:\n"
49
+ "Camera:<camera_id>. Tracking_Target:<target_name> placement:<position>.\n"
50
+ "If multiple cameras are specified, provide separate lines for each camera.\n"
51
+ "The available placements are: top_left, top_middle, top_right, "
52
+ "center_left, center_middle, center_right, bottom_left, bottom_middle, bottom_right.\n"
53
+ "Default Values:\n"
54
+ "- camera_id: default\n"
55
+ "- tracking_target: default\n"
56
+ "- placement: center_middle\n"
57
+ "Rules for Defaults:\n"
58
+ "1. If the camera_id is not specified, use the default value `default`.\n"
59
+ "2. If the tracking_target is not specified, use the default value `default`.\n"
60
+ "3. If the position information is incomplete or not specified, default the placement to the middle position.\n"
61
+ "For example, if the user specifies 'top', interpret it as 'top_middle'.\n\n"
62
+ "Examples:\n"
63
+ "User: Please set camera 1 to track target A at bottom_right.\n"
64
+ "Assistant: Camera:1. Tracking_Target:A placement:bottom_right.\n\n"
65
+ "User: Please set camera 2 to track target B at top.\n"
66
+ "Assistant: Camera:2. Tracking_Target:B placement:top_middle.\n\n"
67
+ "User: Please set camera 3 to track target C.\n"
68
+ "Assistant: Camera:3. Tracking_Target:C placement:center_middle.\n\n"
69
+ "User: Please track target D at left.\n"
70
+ "Assistant: Camera:default. Tracking_Target:D placement:center_left.\n\n"
71
+ "User: Please control camera 4.\n"
72
+ "Assistant: Camera:4. Tracking_Target:default placement:center_middle.\n\n"
73
+ "User: Please start recording.\n"
74
+ "Assistant: Camera:default. Tracking_Target:default placement:center_middle.\n\n"
75
+ "User: Please set camera 2 and camera 3 to track target Kyle at bottom_right.\n"
76
+ "Assistant:\n"
77
+ "Camera:2. Tracking_Target:Kyle placement:bottom_right.\n"
78
+ "Camera:3. Tracking_Target:Kyle placement:bottom_right.\n\n"
79
+ "Now, respond to the following command:\n"
80
+ )
81
 
82
+ chat = []
83
+ for item in history:
84
+ chat.append({"role": "user", "content": item[0]})
85
+ if item[1] is not None:
86
+ chat.append({"role": "assistant", "content": item[1]})
87
+ chat.append({"role": "user", "content": message})
88
+
89
+ # ๅฐ‡ prompt ๆทปๅŠ ๅˆฐๆถˆๆฏ็š„้–‹้ ญ
90
+ full_message = prompt + "\n" + tok.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
91
+
92
+ model_inputs = tok([full_message], return_tensors="pt").to(device)
93
+ streamer = TextIteratorStreamer(
94
+ tok, timeout=20.0, skip_prompt=True, skip_special_tokens=True
95
+ )
96
+ generate_kwargs = dict(
97
+ model_inputs,
98
+ streamer=streamer,
99
+ max_new_tokens=max_tokens,
100
+ do_sample=True,
101
  temperature=temperature,
102
+ eos_token_id=terminators,
103
+ )
104
+
105
+ if temperature == 0:
106
+ generate_kwargs["do_sample"] = False
107
+
108
+ t = Thread(target=model.generate, kwargs=generate_kwargs)
109
+ t.start()
110
 
111
+ partial_text = ""
112
+ for new_text in streamer:
113
+ partial_text += new_text
114
+ yield partial_text
115
+
116
+ yield partial_text
117
 
118
 
 
 
 
119
  demo = gr.ChatInterface(
120
+ fn=chat,
121
+ examples=[["Please set camera 2 to track target A at top."]],
122
+ additional_inputs_accordion=gr.Accordion(
123
+ label="โš™๏ธ Parameters", open=False, render=False
124
+ ),
125
  additional_inputs=[
 
 
 
126
  gr.Slider(
127
+ minimum=0, maximum=1, step=0.1, value=0.9, label="Temperature", render=False
128
+ ),
129
+ gr.Checkbox(label="Sampling", value=True),
130
+ gr.Slider(
131
+ minimum=128,
132
+ maximum=4096,
133
+ step=1,
134
+ value=512,
135
+ label="Max new tokens",
136
+ render=False,
137
  ),
138
  ],
139
+ stop_btn="Stop Generation",
140
+ title="PTZ Camera Control Chat",
141
+ description="Now Running [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) for PTZ camera control.",
142
  )
143
+ demo.launch()