Update app.py

app.py

@@ -15,6 +15,20 @@ tokenizer = AutoTokenizer.from_pretrained(CKPT)
 device = 0 if torch.cuda.is_available() else -1
 
 
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+import torch
+from langs import LANGS
+
+TASK = "translation"
+CKPT = "facebook/nllb-200-distilled-600M"
+
+model = AutoModelForSeq2SeqLM.from_pretrained(CKPT)
+tokenizer = AutoTokenizer.from_pretrained(CKPT)
+
+device = 0 if torch.cuda.is_available() else -1
+
+
 def translate(text):
     """
     Translate the text from source lang to target lang
@@ -34,25 +48,14 @@ def translate(text):
     result = translation_pipeline(text)
     return result[0]['translation_text']
 
-#prompt
-
-stable_diffusion = gr.Blocks.load(name="spaces/runwayml/stable-diffusion-v1-5")
-clip_interrogator_2 = gr.Blocks.load(name="spaces/fffiloni/CLIP-Interrogator-2")
 
-def get_images(prompt):
-    gallery_dir = stable_diffusion(prompt, fn_index=2)    
-    img_results = [os.path.join(gallery_dir, img) for img in os.listdir(gallery_dir)]  
-    return img_results[0]
-
-def get_new_prompt(img, mode):
-    interrogate = clip_interrogator_2(img, mode, 12, api_name="clipi2")
-    return interrogate
-
-def infer(input):
-    prompt = pipe1(input+',', num_return_sequences=1)[0]["generated_text"]
-    img = get_images(prompt) 
-    result = get_new_prompt(img, 'fast')    
-    return result[0]
+gr.Interface(
+    translate,
+    [
+        gr.components.Textbox(label="Text"),
+    ],
+    ["text"],
+).launch()
 
 with gr.Blocks() as demo:
     gr.Markdown(
@@ -70,13 +73,6 @@ with gr.Blocks() as demo:
     
     button.click(translate, [inp1], [out1])
     
-    with gr.Row().style(equal_height=True):
-        inp2 = out1
-        btn1 = gr.Button("让您的提示词更详细一下吧")
-        out2 = gr.Textbox(label = "翻译后的英文内容", lines=1)
-    
-    btn1.click(infer, [inp2], [out2])
-   
     gr.Markdown(
             """ ### <center>注意❗：请不要输入或生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及娱乐使用。用户输入或生成的内容与程序开发者无关，请自觉合法合规使用，违反者一切后果自负。</center>