Update app.py

app.py

@@ -1,42 +1,38 @@
+import torch
 import gradio as gr
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-
-# Load pre-trained model and tokenizer
-model_name = "t5-small"
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-# Function to translate text
-def translate_text(text, source_lang, target_lang):
-    input_text = f"translate {source_lang} to {target_lang}: {text}"
-    inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
-    outputs = model.generate(**inputs)
-    translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return translation
-
-# List of Indian languages
-indian_languages = [
-    "as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te", "ur"
-]
-
-# Supported languages
-languages = ["en", "fr", "de", "es", "it"] + indian_languages
-
-# Create Gradio interface
-def translate_interface(text, source_lang, target_lang):
-    return translate_text(text, source_lang, target_lang)
-
-iface = gr.Interface(
-    fn=translate_interface,
-    inputs=[
-        gr.Textbox(lines=2, placeholder="Enter text to translate"),
-        gr.Dropdown(choices=languages, label="Source Language"),
-        gr.Dropdown(choices=languages, label="Target Language")
-    ],
-    outputs="text",
-    title="Hugging Face Translation App",
-    description="Translate text from one language to another using a T5 model."
-)
-
-if __name__ == "__main__":
-    iface.launch()
+import json
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+text_translator = pipeline("translation", model="facebook/nllb-200-distilled-600M",
+                           torch_dtype=torch.bfloat16)
+
+# Load the JSON data from the file
+with open('language.json', 'r') as file:
+    language_data = json.load(file)
+
+def get_FLORES_code_from_language(language):
+    for entry in language_data:
+        if entry['Language'].lower() == language.lower():
+            return entry['FLORES-200 code']
+    return None
+
+
+def translate_text(text, destination_language):
+    # text = "Hello Friends, How are you?"
+    dest_code= get_FLORES_code_from_language(destination_language)
+    translation = text_translator(text,
+                                  src_lang="eng_Latn",
+                                  tgt_lang=dest_code)
+    return translation[0]["translation_text"]
+
+gr.close_all()
+
+# demo = gr.Interface(fn=summary, inputs="text",outputs="text")
+demo = gr.Interface(fn=translate_text,
+                    inputs=[gr.Textbox(label="Input text to translate",lines=6), gr.Dropdown(["German","French", "Hindi", "Romanian	"], label="Select Destination Language")],
+                    outputs=[gr.Textbox(label="Translated text",lines=4)],
+                    title="@GenAILearniverse Project 4: Multi language translator",
+                    description="THIS APPLICATION WILL BE USED TO TRNSLATE ANY ENGLIST TEXT TO MULTIPLE LANGUAGES.")
+demo.launch()