Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""Translation_APP.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1EVFldoVPoPgAsak48hRkL_D_jhCo76r_
+"""
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from gtts import gTTS
+import torch
+import gradio as gr
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+language_model_name = "Qwen/Qwen2-1.5B-Instruct"
+language_model = AutoModelForCausalLM.from_pretrained(
+    language_model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(language_model_name)
+
+def process_input(input_text, action):
+    if action == "Translate to English":
+        prompt = f"Please translate the following text into English：{input_text}"
+        lang = "en"
+    elif action == "Translate to Chinese":
+        prompt = f"Please translate the following text into Chinese：{input_text}"
+        lang = "zh-cn"
+    elif action == "Translate to Japanese":
+        prompt = f"Please translate the following text into Japanese：{input_text}"
+        lang = "ja"
+    else:
+        prompt = input_text
+        lang = "en"
+
+    messages = [
+        {"role": "system", "content": "You are a helpful AI assistant."},
+        {"role": "user", "content": prompt}
+    ]
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    model_inputs = tokenizer([text], return_tensors="pt").to(device)
+
+    generated_ids = language_model.generate(
+        model_inputs.input_ids,
+        max_new_tokens=512
+    )
+    generated_ids = [
+        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+
+    output_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    return output_text, lang
+
+def text_to_speech(text, lang):
+    tts = gTTS(text=text, lang=lang)
+    filename = "output_audio.mp3"
+    tts.save(filename)
+    return filename
+
+def handle_interaction(input_text, action):
+    output_text, lang = process_input(input_text, action)
+    audio_filename = text_to_speech(output_text, lang)
+    return output_text, audio_filename
+
+action_options = ["Translate to English", "Translate to Chinese", "Translate to Japanese", "Chat"]
+
+iface = gr.Interface(
+    fn=handle_interaction,
+    inputs=[
+        gr.Textbox(label="input text"),
+        gr.Dropdown(action_options, label="select action")
+    ],
+    outputs=[
+        gr.Textbox(label="output text"),
+        gr.Audio(label="output audio")
+    ],
+    title="Translation and Chat App using AI",
+    description="Translate input text or chat based on the selected action.",
+    theme= "gradio/soft"
+)
+
+if __name__ == "__main__":
+    iface.launch(share=True)