Update app.py

app.py

@@ -1,125 +1,196 @@
-import os
-import openai
+import torch
 import gradio as gr
+import spaces
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import os
+import re
+from polyglot.detect import Detector
 
-# -------- OpenAI Key --------
-openai.api_key = os.environ.get('OPENAI_KEY')
-
-# -------- Supported Languages --------
-supportLanguages = [
-    ["auto", "auto"],
-    ["ar", "Arabic"],
-    ["en", "English"],
-    ["fr", "French"],
-    ["de", "German"],
-    ["es", "Spanish"],
-    ["it", "Italian"],
-    ["pt", "Portuguese"],
-    ["zh-CN", "Chinese (Simplified)"],
-    ["zh-TW", "Chinese (Traditional)"],
-    ["ja", "Japanese"],
-    ["ko", "Korean"],
-]
-
-prompt_template = (
-    "You are a translation engine that can only translate text "
-    "and cannot interpret it. Keep the indent of the original text."
-)
-
-# -------- Translation Function --------
-def submit_message(detectFrom, detectTo, user_token, prompt):
-    if user_token:
-        openai.api_key = user_token
-
-    if not prompt:
-        return ""
-
-    # map language name -> code
-    for code, name in supportLanguages:
-        if detectFrom == name:
-            detectFrom = code
-        if detectTo == name:
-            detectTo = code
-
-    systemInstruct = prompt_template
-    translateInstruct = f"translate from {detectFrom} to {detectTo}"
-
-    if detectFrom == "auto":
-        translateInstruct = f"translate to {detectTo}"
-
-    if detectFrom == detectTo:
-        systemInstruct = "You are a text embellisher."
-        translateInstruct = "polish this sentence"
-
-    messages = [
-        {"role": "system", "content": systemInstruct},
-        {"role": "user", "content": translateInstruct},
-        {"role": "user", "content": prompt},
-    ]
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
+MODEL = "LLaMAX/LLaMAX3-8B-Alpaca"
+RELATIVE_MODEL="LLaMAX/LLaMAX3-8B"
 
-    try:
-        response = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo",
-            messages=messages,
-            temperature=0,
-            max_tokens=1000,
-        )
-        return response["choices"][0]["message"]["content"]
+TITLE = "<h1><center>LLaMAX3-Translator</center></h1>"
+
+
+model = AutoModelForCausalLM.from_pretrained(
+        MODEL,
+        torch_dtype=torch.float16,
+        device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
 
-    except Exception as e:
-        return f"Error: {e}"
 
-# -------- CSS --------
-css = """
-#col-container { max-width: 80%; margin: auto; }
-#header { text-align: center; }
+def lang_detector(text):
+    min_chars = 5
+    if len(text) < min_chars:
+        return "Input text too short"
+    try:
+        detector = Detector(text).language
+        lang_info = str(detector)
+        code = re.search(r"name: (\w+)", lang_info).group(1)
+        return code
+    except Exception as e:
+        return f"ERROR：{str(e)}"
+
+def Prompt_template(inst, prompt, query, src_language, trg_language):
+    inst = inst.format(src_language=src_language, trg_language=trg_language)
+    instruction = f"`{inst}`"
+    prompt = (
+        f'{prompt}'
+        f'### Instruction:\n{instruction}\n'
+        f'### Input:\n{query}\n### Response:'
+    )
+    return prompt
+
+# Unfinished
+def chunk_text():
+    pass
+    
+@spaces.GPU(duration=60)
+def translate(
+    source_text: str, 
+    source_lang: str,
+    target_lang: str,
+    inst: str, 
+    prompt: str, 
+    max_length: int,
+    temperature: float,
+    top_p: float,
+    rp: float):
+    
+    print(f'Text is - {source_text}')
+    
+    prompt = Prompt_template(inst, prompt, source_text, source_lang, target_lang)
+    input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
+        
+    generate_kwargs = dict(
+        input_ids=input_ids,
+        max_length=max_length, 
+        do_sample=True, 
+        temperature=temperature,
+        top_p=top_p,
+        repetition_penalty=rp,    
+    )
+
+    outputs = model.generate(**generate_kwargs)
+    
+    resp = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
+    
+    yield resp[len(prompt):]
+
+CSS = """
+    h1 {
+        text-align: center;
+        display: block;
+        height: 10vh;
+        align-content: center;
+    }
+    footer {
+        visibility: hidden;
+    }
 """
 
-# -------- Gradio UI --------
-with gr.Blocks() as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown("## 🌍 OpenAI Translator", elem_id="header")
-
-        with gr.Row():
-            translateFrom = gr.Dropdown(
-                label="Translate From",
-                value="auto",
-                choices=[l[1] for l in supportLanguages],
-                scale=1,
-            )
+LICENSE = """
+Model: <a href="https://huggingface.co/LLaMAX/LLaMAX3-8B-Alpaca">LLaMAX3-8B-Alpaca</a>
+"""
 
-            translateTo = gr.Dropdown(
-                label="Translate To",
+LANG_LIST = ['Akrikaans', 'Amharic', 'Arabic', 'Armenian', 'Assamese', 'Asturian', 'Azerbaijani', \
+             'Belarusian', 'Bengali', 'Bosnian', 'Bulgarian', 'Burmese', \
+             'Catalan', 'Cebuano', 'Simplified Chinese', 'Traditional Chinese', 'Croatian', 'Czech', \
+             'Danish', 'Dutch', 'English', 'Estonian', 'Filipino', 'Finnish', 'French', 'Fulah', \
+             'Galician', 'Ganda', 'Georgian', 'German', 'Greek', 'Gujarati', \
+             'Hausa', 'Hebrew', 'Hindi', 'Hungarian', \
+             'Icelandic', 'Igbo', 'Indonesian', 'Irish', 'Italian', \
+             'Japanese', 'Javanese', \
+             'Kabuverdianu', 'Kamba', 'Kannada', 'Kazakh', 'Khmer', 'Korean', 'Kyrgyz', \
+             'Lao', 'Latvian', 'Lingala', 'Lithuanian', 'Luo', 'Luxembourgish', \
+             'Macedonian', 'Malay', 'Malayalam', 'Maltese', 'Maori', 'Marathi', 'Mongolian', \
+             'Nepali', 'Northern', 'Norwegian', 'Nyanja', \
+             'Occitan', 'Oriya', 'Oromo', \
+             'Pashto', 'Persian', 'Polish', 'Portuguese', 'Punjabi', \
+             'Romanian', 'Russian', \
+             'Serbian', 'Shona', 'Sindhi', 'Slovak', 'Slovenian', 'Somali', 'Sorani', 'Spanish', 'Swahili', 'Swedish', \
+             'Tajik', 'Tamil', 'Telugu', 'Thai', 'Turkish', \
+             'Ukrainian', 'Umbundu', 'Urdu', 'Uzbek', \
+             'Vietnamese', 'Welsh', 'Wolof', 'Xhosa', 'Yoruba', 'Zulu']
+
+chatbot = gr.Chatbot(height=600)
+
+with gr.Blocks(theme="soft", css=CSS) as demo:
+    gr.Markdown(TITLE)
+    with gr.Row():
+        with gr.Column(scale=1):
+            source_lang = gr.Textbox(
+                label="Source Lang(Auto-Detect)",
                 value="English",
-                choices=[l[1] for l in supportLanguages if l[0] != "auto"],
-                scale=1,
             )
+            target_lang = gr.Dropdown(
+                label="Target Lang",
+                value="Spanish",
+                choices=LANG_LIST,
+            )
+            max_length = gr.Slider(
+                label="Max Length",
+                minimum=512,
+                maximum=8192,
+                value=4096,
+                step=8,
+            )
+            temperature = gr.Slider(
+                label="Temperature",
+                minimum=0,
+                maximum=1,
+                value=0.3,
+                step=0.1,
+            )
+            top_p = gr.Slider(
+                minimum=0.0,
+                maximum=1.0,
+                step=0.1,
+                value=1.0,
+                label="top_p",
+            )
+            rp = gr.Slider(
+                minimum=0.0,
+                maximum=2.0,
+                step=0.1,
+                value=1.2,
+                label="Repetition penalty",
+            )
+            with gr.Accordion("Advanced Options", open=False):
+                inst = gr.Textbox(
+                    label="Instruction",
+                    value="Translate the following sentences from {src_language} to {trg_language}.",
+                    lines=3,
+                )
+                prompt = gr.Textbox(
+                    label="Prompt",
+                    value=""" 'Below is an instruction that describes a task, paired with an input that provides further context. '
+                    'Write a response that appropriately completes the request.\n' """,
+                    lines=8,
+                )
+                
+        with gr.Column(scale=4):
+            source_text = gr.Textbox(
+                label="Source Text",
+                value="LLaMAX is a language model with powerful multilingual capabilities without loss instruction-following capabilities. "+\
+                "LLaMAX supports translation between more than 100 languages, "+\
+                "surpassing the performance of similarly scaled LLMs.",
+                lines=10,
+            )
+            output_text = gr.Textbox(
+                label="Output Text",
+                lines=10,
+                show_copy_button=True,
+            )
+    with gr.Row():
+        submit = gr.Button(value="Submit")
+        clear = gr.ClearButton([source_text, output_text])
+    gr.Markdown(LICENSE)
+    
+    source_text.change(lang_detector, source_text, source_lang)
+    submit.click(fn=translate, inputs=[source_text, source_lang, target_lang, inst, prompt, max_length, temperature, top_p, rp], outputs=[output_text])
 
-        input_message = gr.Textbox(
-            lines=8,
-            placeholder="Enter text here...",
-            label="Input",
-        )
-
-        output = gr.Textbox(
-            lines=8,
-            label="Translation",
-        )
-
-        user_token = gr.Textbox(
-            placeholder="Optional: OpenAI API Key",
-            type="password",
-            label="API Key",
-        )
-
-        btn_submit = gr.Button("Translate")
-
-        btn_submit.click(
-            submit_message,
-            inputs=[translateFrom, translateTo, user_token, input_message],
-            outputs=output,
-        )
-
-# -------- Launch --------
 
-demo.launch(css=css, height="800px")
+if __name__ == "__main__":
+    demo.launch()