Create app.py

app.py

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+import torch
+import gradio as gr
+from transformers import pipeline
+import ast
+
+translation_task_names = {
+    'English to French': 'translation_en_to_fr',
+    'French to English': 'translation_fr_to_en',
+    'English to Spanish': 'translation_en_to_es',
+    'Spanish to English': 'translation_es_to_en',
+    'English to German': 'translation_en_to_de',
+    'German to English': 'translation_de_to_en',
+    'English to Italian': 'translation_en_to_it',
+    'Italian to English': 'translation_it_to_en',
+    'English to Dutch': 'translation_en_to_nl',
+    'Dutch to English': 'translation_nl_to_en',
+    'English to Portuguese': 'translation_en_to_pt',
+    'Portuguese to English': 'translation_pt_to_en',
+    'English to Russian': 'translation_en_to_ru',
+    'Russian to English': 'translation_ru_to_en',
+    'English to Chinese': 'translation_en_to_zh',
+    'Chinese to English': 'translation_zh_to_en',
+    'English to Japanese': 'translation_en_to_ja',
+    'Japanese to English': 'translation_ja_to_en',
+}
+
+# Create a dictionary to store loaded models
+loaded_models = {}
+
+# Simple translation function
+def translate_text(task_choice, text_input, load_in_8bit, device):
+    model_key = (task_choice, load_in_8bit)  # Create a tuple to represent the unique combination of task and 8bit loading
+
+    # Check if the model is already loaded
+    if model_key in loaded_models:
+        translator = loaded_models[model_key]
+    else:
+        model_kwargs = {"load_in_8bit": load_in_8bit} if load_in_8bit else {}
+        dtype = torch.float16 if load_in_8bit else torch.float32  # Set dtype based on the value of load_in_8bit
+        translator = pipeline(task=translation_task_names[task_choice],
+                            device=device,  # Use selected device
+                            model_kwargs=model_kwargs, 
+                            torch_dtype=dtype,  # Set the floating point
+                            use_fast=True
+                            )
+        # Store the loaded model
+        loaded_models[model_key] = translator
+
+    translation = translator(text_input)[0]['translation_text']
+    return str(translation).strip()
+
+def launch(task_choice, text_input, load_in_8bit, device):
+    return translate_text(task_choice, text_input, load_in_8bit, device)
+
+task_dropdown = gr.Dropdown(choices=list(translation_task_names.keys()), label='Select Translation Task')
+text_input = gr.Textbox(label="Input Text")  # Single line text input
+load_in_8bit = gr.Checkbox(label="Load model in 8bit")
+device = gr.Radio(['cpu', 'cuda'], label='Select device', default='cpu')
+
+iface = gr.Interface(launch, inputs=[task_dropdown, text_input, load_in_8bit, device], 
+                     outputs=gr.outputs.Textbox(type="text", label="Translation"))
+iface.launch()