Update app.py

app.py

@@ -1,304 +1,18 @@
-import os
-import requests
-import json
-import time
-
 import gradio as gr
-from transformers import AutoTokenizer
-
-
-import socket
-hostname=socket.gethostname()
-IPAddr=socket.gethostbyname(hostname)
-print("Your Computer Name is:" + hostname)
-print("Your Computer IP Address is:" + IPAddr)
-
-
-DESCRIPTION = """
-# MediaTek Research Breeze-7B
-
-MediaTek Research Breeze-7B (hereinafter referred to as Breeze-7B) is a language model family that builds on top of [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1), specifically intended for Traditional Chinese use.
-
-[Breeze-7B-Base](https://huggingface.co/MediaTek-Research/Breeze-7B-Base-v1_0) is the base model for the Breeze-7B series. 
-It is suitable for use if you have substantial fine-tuning data to tune it for your specific use case.
-
-[Breeze-7B-Instruct](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v1_0) derives from the base model Breeze-7B-Base, making the resulting model amenable to be used as-is for commonly seen tasks.
-
-
-The current release version of Breeze-7B is v1.0.
-
-*A project by the members (in alphabetical order): Chan-Jan Hsu 許湛然, Chang-Le Liu 劉昶樂, Feng-Ting Liao 廖峰挺, Po-Chun Hsu 許博竣, Yi-Chang Chen 陳宜昌, and the supervisor Da-Shan Shiu 許大山.*
-
-**免責聲明: MediaTek Research Breeze-7B 並未針對問答進行安全保護，因此語言模型的任何回應不代表 MediaTek Research 立場。**
-"""
-
-LICENSE = """
-"""
-
-DEFAULT_SYSTEM_PROMPT = "You are a helpful AI assistant built by MediaTek Research. The user you are helping speaks Traditional Chinese and comes from Taiwan."
-
-API_URL = os.environ.get("API_URL")
-TOKEN = os.environ.get("TOKEN")
-TOKENIZER_REPO = "MediaTek-Research/Breeze-7B-Instruct-v1_0"
-API_MODEL_TYPE = "breeze-7b-instruct-v10"
-
-HEADERS = {
-    "Authorization": f"Bearer {TOKEN}", 
-    "Content-Type": "application/json",
-    "accept": "application/json"
-}
-
-MAX_SEC = 30
-MAX_INPUT_LENGTH = 5000
-
-tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_REPO, use_auth_token=os.environ.get("HF_TOKEN"))
-
-
-def refusal_condition(query):
-    # 不要再問這些問題啦！
-
-    query_remove_space = query.replace(' ', '').lower()
-    is_including_tw = False
-    for x in ['台灣', '台湾', 'taiwan', 'tw', '中華民國', '中华民国']:
-        if x in query_remove_space:
-            is_including_tw = True
-    is_including_cn = False
-    for x in ['中國', '中国', 'cn', 'china', '大陸', '內地', '大陆', '内地', '中華人民共和國', '中华人民共和国']:
-        if x in query_remove_space:
-            is_including_cn = True
-    if is_including_tw and is_including_cn:
-        return True
-
-    for x in ['一個中國', '兩岸', '一中原則', '一中政策', '一个中国', '两岸', '一中原则']:
-        if x in query_remove_space:
-            return True
-
-    return False
-
-with gr.Blocks() as demo:
-    gr.Markdown(DESCRIPTION)
-
-    system_prompt = gr.Textbox(label='System prompt',
-                               value=DEFAULT_SYSTEM_PROMPT,
-                               lines=1)
-    
-    with gr.Accordion(label='Advanced options', open=False):
-
-        max_new_tokens = gr.Slider(
-            label='Max new tokens',
-            minimum=32,
-            maximum=2048,
-            step=1,
-            value=1024,
-        )
-        temperature = gr.Slider(
-            label='Temperature',
-            minimum=0.01,
-            maximum=0.5,
-            step=0.01,
-            value=0.01,
-        )
-        top_p = gr.Slider(
-            label='Top-p (nucleus sampling)',
-            minimum=0.01,
-            maximum=0.99,
-            step=0.01,
-            value=0.01,
-        )
-
-    chatbot = gr.Chatbot(show_copy_button=True, show_share_button=True, )
-    with gr.Row():
-        msg = gr.Textbox(
-            container=False,
-            show_label=False,
-            placeholder='Type a message...',
-            scale=10,
-            lines=6
-        )
-        submit_button = gr.Button('Submit',
-                                  variant='primary',
-                                  scale=1,
-                                  min_width=0)
-
-    with gr.Row():
-        retry_button = gr.Button('🔄  Retry', variant='secondary')
-        undo_button = gr.Button('↩️ Undo', variant='secondary')
-        clear = gr.Button('🗑️  Clear', variant='secondary')
-
-    saved_input = gr.State()
-
-    def user(user_message, history):
-        return "", history + [[user_message, None]]
-
-
-    def connect_server(data):
-        for _ in range(3):
-            s = requests.Session()
-            r = s.post(API_URL, headers=HEADERS, json=data, stream=True, timeout=30)
-            time.sleep(1)
-            if r.status_code == 200:
-                return r
-        return None
-
-
-    def stream_response_from_server(r):
-        # start_time = time.time()
-        keep_streaming = True
-        for line in r.iter_lines():
-            # if time.time() - start_time > MAX_SEC:
-            #     keep_streaming = False
-            #     break
-            
-            if line and keep_streaming:
-                if r.status_code != 200:
-                    continue
-                json_response = json.loads(line)
-
-                if "fragment" not in json_response["result"]:
-                    keep_streaming = False
-                    break
-
-                delta = json_response["result"]["fragment"]["data"]["text"]
-                yield delta
-
-            # start_time = time.time()
-
-    
-    def bot(history, max_new_tokens, temperature, top_p, system_prompt):
-        chat_data = []
-        system_prompt = system_prompt.strip()
-        if system_prompt:
-            chat_data.append({"role": "system", "content": system_prompt})
-        for user_msg, assistant_msg in history:
-            chat_data.append({"role": "user", "content": user_msg if user_msg is not None else ''})
-            chat_data.append({"role": "assistant", "content": assistant_msg if assistant_msg is not None else ''})
-
-        message = tokenizer.apply_chat_template(chat_data, tokenize=False)
-        message = message[3:]  # remove SOT token
-
-        if len(message) > MAX_INPUT_LENGTH:
-            raise Exception()
-
-        response = '[ERROR]'
-        if refusal_condition(history[-1][0]):
-            history = [['[安全拒答啟動]', '[安全拒答啟動] 請清除再開啟對話']]
-            response = '[REFUSAL]'
-            yield history
-        else:
-            data = {
-                "model_type": API_MODEL_TYPE,
-                "prompt": str(message),
-                "parameters": {
-                    "temperature": float(temperature),
-                    "top_p": float(top_p),
-                    "max_new_tokens": int(max_new_tokens),
-                    "repetition_penalty": 1.1
-                }
-            }
-            
-            r = connect_server(data)
-            if r is not None:
-                for delta in stream_response_from_server(r):
-                    if history[-1][1] is None:
-                        history[-1][1] = ''
-                    history[-1][1] += delta
-                    yield history
-
-                if history[-1][1].endswith('</s>'):
-                    history[-1][1] = history[-1][1][:-4]
-                    yield history
-    
-                response = history[-1][1]
-    
-                if refusal_condition(history[-1][1]):
-                    history[-1][1] = history[-1][1] + '\n\n**[免責聲明: 此模型並未針對問答進行安全保護，因此語言模型的任何回應不代表 MediaTek Research 立場。]**'
-                    yield history
-            else:
-                del history[-1]
-                yield history
-        print('== Record ==\nQuery: {query}\nResponse: {response}'.format(query=repr(message), response=repr(history[-1][1])))
-        
-    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
-        fn=bot,
-        inputs=[
-            chatbot,
-            max_new_tokens,
-            temperature,
-            top_p,
-            system_prompt,
-        ],
-        outputs=chatbot
-    )
-    submit_button.click(
-        user, [msg, chatbot], [msg, chatbot], queue=False
-    ).then(
-        fn=bot,
-        inputs=[
-            chatbot,
-            max_new_tokens,
-            temperature,
-            top_p,
-            system_prompt,
-        ],
-        outputs=chatbot
-    )
-
-
-    def delete_prev_fn(
-            history: list[tuple[str, str]]) -> tuple[list[tuple[str, str]], str]:
-        try:
-            message, _ = history.pop()
-        except IndexError:
-            message = ''
-        return history, message or ''
-
-
-    def display_input(message: str,
-                      history: list[tuple[str, str]]) -> list[tuple[str, str]]:
-        history.append((message, ''))
-        return history
-
-    retry_button.click(
-        fn=delete_prev_fn,
-        inputs=chatbot,
-        outputs=[chatbot, saved_input],
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=display_input,
-        inputs=[saved_input, chatbot],
-        outputs=chatbot,
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=bot,
-        inputs=[
-            chatbot,
-            max_new_tokens,
-            temperature,
-            top_p,
-            system_prompt,
-        ],
-        outputs=chatbot,
-    )
+from transformers import pipeline
 
-    undo_button.click(
-        fn=delete_prev_fn,
-        inputs=chatbot,
-        outputs=[chatbot, saved_input],
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=lambda x: x,
-        inputs=[saved_input],
-        outputs=msg,
-        api_name=False,
-        queue=False,
-    )
+pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-    clear.click(lambda: None, None, chatbot, queue=False)
+def predict(input_img):
+    predictions = pipeline(input_img)
+    return input_img, {p["label"]: p["score"] for p in predictions} 
 
-    gr.Markdown(LICENSE)
+gradio_app = gr.Interface(
+    predict,
+    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
+    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
+    title="Hot Dog? Or Not?",
+)
 
-demo.queue(concurrency_count=4, max_size=128)
-demo.launch()
+if __name__ == "__main__":
+    gradio_app.launch()