A

app.py

@@ -5,8 +5,8 @@ import gradio as gr
 import torch
 
 from transformers import AutoModelForCausalLM
-from transformers import AutoTokenizer
 from transformers import AutoModelForSeq2SeqLM
+from transformers import AutoTokenizer
 
 from transformers import AutoProcessor
 
@@ -14,12 +14,16 @@ from transformers import pipeline
 
 from transformers import set_seed
 
+global ButtonIndex
+
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 big_processor = AutoProcessor.from_pretrained("microsoft/git-base-coco")
 big_model = AutoModelForCausalLM.from_pretrained("microsoft/git-base-coco")
 
-text_pipe = pipeline('text-generation', model='succinctly/text2image-prompt-generator')
+pipeline_01 = pipeline('text-generation', model='succinctly/text2image-prompt-generator')
+pipeline_02 = pipeline('text-generation', model='Gustavosta/MagicPrompt-Stable-Diffusion', tokenizer='gpt2')
+pipeline_03 = pipeline('text-generation', model='johnsu6616/ModelExport')
 
 zh2en_model = AutoModelForSeq2SeqLM.from_pretrained('Helsinki-NLP/opus-mt-zh-en').eval()
 zh2en_tokenizer = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-zh-en')
@@ -27,17 +31,14 @@ zh2en_tokenizer = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-zh-en')
 en2zh_model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-zh").eval()
 en2zh_tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-zh")
 
-
 def translate_zh2en(text):
     with torch.no_grad():
 
+        text = re.sub(r"[:\-–.!;?_#]", '', text)
         text = re.sub(r'([^\u4e00-\u9fa5])([\u4e00-\u9fa5])', r'\1\n\2', text)
         text = re.sub(r'([\u4e00-\u9fa5])([^\u4e00-\u9fa5])', r'\1\n\2', text)
-
         text = text.replace('\n', ',')
-
         text =re.sub(r'(?<![a-zA-Z])\s+|\s+(?![a-zA-Z])', '', text)
-
         text = re.sub(r',+', ',', text)
 
         encoded = zh2en_tokenizer([text], return_tensors='pt')
@@ -45,80 +46,68 @@ def translate_zh2en(text):
         result = zh2en_tokenizer.batch_decode(sequences, skip_special_tokens=True)[0]
 
         result = result.strip() 
-        if result != "No,no," :
+
+        if result == "No,no," :
             result = text
-        return result
 
+        return result
 
 def translate_en2zh(text):
     with torch.no_grad():
 
         encoded = en2zh_tokenizer([text], return_tensors="pt")
         sequences = en2zh_model.generate(**encoded)
-        return en2zh_tokenizer.batch_decode(sequences, skip_special_tokens=True)[0]
-
-def test05(text):
-
-    return text
+        result = en2zh_tokenizer.batch_decode(sequences, skip_special_tokens=True)[0]
 
-def test06(text):
+        result = re.sub(r'(\b\w+\b)(?:\W+\1\b)+', r'\1', result)
+        return result
 
-    return text
+def load_prompter():
+    prompter_model = AutoModelForCausalLM.from_pretrained("microsoft/Promptist")
+    tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.padding_side = "left"
+    return prompter_model, tokenizer
 
+prompter_model, prompter_tokenizer = load_prompter()
 
-def text_generate(text):
+def generate_prompter_pipeline_01(text):
     seed = random.randint(100, 1000000)
     set_seed(seed)
-
     text_in_english = translate_zh2en(text)
-    result = ""
-    for _ in range(6):
-        sequences = text_pipe(text_in_english, max_length=random.randint(60, 90), num_return_sequences=8)
-        list = []
-        for sequence in sequences:
-
-
-            line = sequence['generated_text'].strip()
-
-            if line != text_in_english and len(line) > (len(text_in_english) + 4):
+    response = pipeline_01(text_in_english, max_new_tokens=80, num_return_sequences=3)
+    response_list = []
+    for x in response:
+        resp = x['generated_text'].strip()
 
-                list.append(translate_en2zh(line)+"\n")
-                list.append(line+"\n")
-                list.append("\n")
+        if resp != text_in_english and len(resp) > (len(text_in_english) + 4):
 
-        result = "".join(list)
+            response_list.append(translate_en2zh(resp)+"\n")
+            response_list.append(resp+"\n")
+            response_list.append("\n")
 
-        result = re.sub('[^ ]+\.[^ ]+', '', result)
-
-        result = result.replace('<', '').replace('>', '')
-
-        if result != '':
-            break
-    return result
+    result = "".join(response_list)
+    result = re.sub('[^ ]+\.[^ ]+', '', result)
+    result = result.replace('<', '').replace('>', '')
 
+    if result != '':
+        return result
 
-def load_prompter():
-    prompter_model = AutoModelForCausalLM.from_pretrained("microsoft/Promptist")
-    tokenizer = AutoTokenizer.from_pretrained("gpt2")
-    tokenizer.pad_token = tokenizer.eos_token
-    tokenizer.padding_side = "left"
-    return prompter_model, tokenizer
+def generate_prompter_tokenizer_01(text):
 
-prompter_model, prompter_tokenizer = load_prompter()
+    text_in_english = translate_zh2en(text)
 
-def generate_prompter(text):
-    text = translate_zh2en(text)
+    input_ids = prompter_tokenizer(text_in_english.strip()+" Rephrase:", return_tensors="pt").input_ids
 
-    input_ids = prompter_tokenizer(text.strip()+" Rephrase:", return_tensors="pt").input_ids
-    eos_id = prompter_tokenizer.eos_token_id
+    eos_id = 50256
     outputs = prompter_model.generate(
         input_ids,
         do_sample=False,
-        max_new_tokens=75,
+        max_new_tokens=80,
         num_beams=3,
         num_return_sequences=3,
-        eos_token_id=eos_id,
         pad_token_id=eos_id,
+        eos_token_id=eos_id,
         length_penalty=-1.0
     )
     output_texts = prompter_tokenizer.batch_decode(outputs, skip_special_tokens=True)
@@ -134,69 +123,143 @@ def generate_prompter(text):
         result.append("\n")
     return "".join(result)
 
-def combine_text(text):
-    text01 = generate_prompter(text)
-    text02 = text_generate(text)
-    return text01,text02
 
-def get_prompt_from_image(input_image):
+def generate_prompter_pipeline_02(text):
+    seed = random.randint(100, 1000000)
+    set_seed(seed)
+    text_in_english = translate_zh2en(text)
+    response = pipeline_02(text_in_english, max_new_tokens=80, num_return_sequences=3)
+    response_list = []
+    for x in response:
+        resp = x['generated_text'].strip()
+        if resp != text_in_english and len(resp) > (len(text_in_english) + 4):
+
+            response_list.append(translate_en2zh(resp)+"\n")
+            response_list.append(resp+"\n")
+            response_list.append("\n")
+
+    result = "".join(response_list)
+    result = re.sub('[^ ]+\.[^ ]+','', result)
+    result = result.replace("<", "").replace(">", "")
+
+    if result != "":
+        return result
+
+def generate_prompter_pipeline_03(text):
+    seed = random.randint(100, 1000000)
+    set_seed(seed)
+    text_in_english = translate_zh2en(text)
+    response = pipeline_03(text_in_english, max_new_tokens=80, num_return_sequences=3)
+    response_list = []
+    for x in response:
+        resp = x['generated_text'].strip()
+        if resp != text_in_english and len(resp) > (len(text_in_english) + 4):
+
+
+            response_list.append(translate_en2zh(resp)+"\n")
+            response_list.append(resp+"\n")
+            response_list.append("\n")
+
+    result = "".join(response_list)
+    result = re.sub('[^ ]+\.[^ ]+','', result)
+    result = result.replace("<", "").replace(">", "")
+
+    if result != "":
+        return result
+
+def generate_render(text,choice):
+    if choice == '★pipeline模式(succinctly)':
+        outputs = generate_prompter_pipeline_01(text)
+        return outputs,choice
+    elif choice == '★★tokenizer模式':
+        outputs = generate_prompter_tokenizer_01(text)
+        return outputs,choice
+    elif choice == '★★★pipeline模型(Gustavosta)':
+        outputs = generate_prompter_pipeline_02(text)
+        return outputs,choice
+    elif choice == 'pipeline模型(John)_自訓測試，資料不穩定':
+        outputs = generate_prompter_pipeline_03(text)
+        return outputs,choice
+
+def get_prompt_from_image(input_image,choice):
     image = input_image.convert('RGB')
     pixel_values = big_processor(images=image, return_tensors="pt").to(device).pixel_values
-    generated_ids = big_model.to(device).generate(pixel_values=pixel_values, max_length=50)
+    generated_ids = big_model.to(device).generate(pixel_values=pixel_values, max_new_tokens=80)
     generated_caption = big_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-    result01 = generate_prompter(generated_caption)
-    result02 = text_generate(generated_caption)
-    return result01,result02
+    text = re.sub(r"[:\-–.!;?_#]", '', generated_caption)
+
+    if choice == '★pipeline模式(succinctly)':
+        outputs = generate_prompter_pipeline_01(text)
+        return outputs
+    elif choice == '★★tokenizer模式':
+        outputs = generate_prompter_tokenizer_01(text)
+        return outputs
+    elif choice == '★★★pipeline模型(Gustavosta)':
+        outputs = generate_prompter_pipeline_02(text)
+        return outputs
+    elif choice == 'pipeline模型(John)_自訓測試，資料不穩定':
+        outputs = generate_prompter_pipeline_03(text)
+        return outputs
 
 with gr.Blocks() as block:
     with gr.Column():
         with gr.Tab('工作區'):
             with gr.Row():
                 input_text = gr.Textbox(lines=12, label='輸入文字', placeholder='在此输入文字...')
-                input_image = gr.Image(type='pil')
+                input_image = gr.Image(type='pil', label="選擇圖片(辨識度不佳)")
             with gr.Row():
                 txt_prompter_btn = gr.Button('文生文')
                 pic_prompter_btn = gr.Button('圖生文')
             with gr.Row():
-                Textbox_1 = gr.Textbox(lines=6, label='生成方式A')
+                radio_btn = gr.Radio(
+                    label="請選擇產出方式",
+                    choices=['★pipeline模式(succinctly)', '★★tokenizer模式', '★★★pipeline模型(Gustavosta)',
+                            'pipeline模型(John)_自訓測試，資料不穩定'], 
+
+                    value='★pipeline模式(succinctly)'
+                )
+
+            with gr.Row():
+                Textbox_1 = gr.Textbox(lines=6, label='提示詞生成')
             with gr.Row():
-                Textbox_2 = gr.Textbox(lines=6, label='生成方式B')
+                Textbox_2 = gr.Textbox(lines=6, label='測試資訊')
+
         with gr.Tab('測試區'):
             with gr.Row():
                 input_test01 = gr.Textbox(lines=2, label='中英翻譯', placeholder='在此输入文字...')
                 test01_btn = gr.Button('執行')
                 Textbox_test01 = gr.Textbox(lines=2, label='輸出結果')
             with gr.Row():
-                input_test02 = gr.Textbox(lines=2, label='英中翻譯', placeholder='在此输入文字...')
+                input_test02 = gr.Textbox(lines=2, label='英中翻譯(不精準)', placeholder='在此输入文字...')
                 test02_btn = gr.Button('執行')
                 Textbox_test02 = gr.Textbox(lines=2, label='輸出結果')
             with gr.Row():
-                input_test03 = gr.Textbox(lines=2, label='SD模式', placeholder='在此输入文字...')
+                input_test03 = gr.Textbox(lines=2, label='★pipeline模式(succinctly)', placeholder='在此输入文字...')
                 test03_btn = gr.Button('執行')
                 Textbox_test03 = gr.Textbox(lines=2, label='輸出結果')
             with gr.Row():
-                input_test04 = gr.Textbox(lines=2, label='瞎掰模式', placeholder='在此输入文字...')
+                input_test04 = gr.Textbox(lines=2, label='★★tokenizer模式', placeholder='在此输入文字...')
                 test04_btn = gr.Button('執行')
                 Textbox_test04 = gr.Textbox(lines=2, label='輸出結果')
             with gr.Row():
-                input_test05 = gr.Textbox(lines=2, label='沒作用', placeholder='在此输入文字...')
+                input_test05 = gr.Textbox(lines=2, label='★★★pipeline模型(Gustavosta)', placeholder='在此输入文字...')
                 test05_btn = gr.Button('執行')
                 Textbox_test05 = gr.Textbox(lines=2, label='輸出結果')
             with gr.Row():
-                input_test06 = gr.Textbox(lines=2, label='沒作用', placeholder='在此输入文字...')
+                input_test06 = gr.Textbox(lines=2, label='pipeline模型(John)_自訓測試，資料不穩定', placeholder='在此输入文字...')
                 test06_btn = gr.Button('執行')
                 Textbox_test06 = gr.Textbox(lines=2, label='輸出結果')
 
-    txt_prompter_btn.click(
-        fn=combine_text,
-        inputs=input_text, 
+    txt_prompter_btn.click (
+        fn=generate_render,
+        inputs=[input_text,radio_btn],
         outputs=[Textbox_1,Textbox_2]
-    )
-
+      )
+    
     pic_prompter_btn.click(
         fn=get_prompt_from_image,
-        inputs=input_image,
-        outputs=[Textbox_1,Textbox_2]
+        inputs=[input_image,radio_btn],
+        outputs=Textbox_1
     )
 
     test01_btn.click(
@@ -212,25 +275,28 @@ with gr.Blocks() as block:
     )
 
     test03_btn.click(
-        fn=generate_prompter,
+        fn= generate_prompter_pipeline_01,
         inputs=input_test03,
         outputs=Textbox_test03
     )
 
     test04_btn.click(
-        fn=text_generate,
+        fn= generate_prompter_tokenizer_01,
         inputs=input_test04,
         outputs=Textbox_test04
     )
+
     test05_btn.click(
-        fn=test05,
+        fn= generate_prompter_pipeline_02,
         inputs=input_test05,
         outputs=Textbox_test05
     )
+
     test06_btn.click(
-        fn=test06,
-        inputs=input_test06,
-        outputs=Textbox_test06
+        fn= generate_prompter_pipeline_03,
+        inputs= input_test06,
+        outputs= Textbox_test06
     )
 
 block.queue(max_size=64).launch(show_api=False, enable_queue=True, debug=True, share=False, server_name='0.0.0.0')
+

requirements.txt

@@ -1,6 +1,6 @@
-transformers==4.29.1
+transformers==4.29.2
 torch==2.0.0
-pytorch_lightning==1.7.7
+pytorch_lightning==2.0.2
 gradio==3.30.0
-sentencepiece==0.1.97
+sentencepiece==0.1.99
 sacremoses==0.0.53