Create app.py

app.py

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+import torch
+import gradio as gr
+from transformers import pipeline
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+# model_path = ("../Models/models--Helsinki-NLP--opus-mt-en-de/snapshots"
+#               "/6183067f769a302e3861815543b9f312c71b0ca4")
+
+pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-de")
+# text_translator = pipeline("translation", model=model_path, tokenizer=model_path)
+
+def translate_text(text, destination_language):
+    # German uses your preloaded local snapshot
+    if destination_language == "German":
+        out = text_translator(text)
+        return out[0]["translation_text"]
+
+    # For other targets we load the correct Marian model on demand (EN -> target)
+    if destination_language == "French":
+        p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr")
+    elif destination_language == "Hindi":
+        p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-hi")
+    elif destination_language == "Romanian":
+        p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ro")
+    elif destination_language == "Spanish":
+        p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")
+    elif destination_language == "Italian":
+        p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-it")
+    elif destination_language == "Portuguese":
+        p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-pt")
+    elif destination_language == "Russian":
+        p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ru")
+    elif destination_language == "Japanese":
+        p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ja")
+    elif destination_language == "Korean":
+        p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ko")
+    elif destination_language == "Chinese":
+        p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-zh")
+    elif destination_language == "Arabic":
+        p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ar")
+    elif destination_language == "Turkish":
+        p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-tr")
+    elif destination_language == "Dutch":
+        p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-nl")
+    elif destination_language == "Polish":
+        p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-pl")
+    elif destination_language == "Ukrainian":
+        p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-uk")
+    elif destination_language == "Czech":
+        p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-cs")
+    elif destination_language == "Swedish":
+        p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-sv")
+    else:
+        return "Unsupported language. Please choose a listed destination."
+
+    out = p(text)  # Marian pipeline returns a list of dicts
+    return out[0]["translation_text"]
+
+gr.close_all()
+demo = gr.Interface(
+    fn=translate_text,
+    inputs=[
+        gr.Textbox(label="Input text to translate", lines=6),
+        gr.Dropdown(
+            [
+                "German", "French", "Hindi", "Romanian", "Spanish", "Italian",
+                "Portuguese", "Russian", "Japanese", "Korean", "Chinese",
+                "Arabic", "Turkish", "Dutch", "Polish", "Ukrainian", "Czech", "Swedish"
+            ],
+            label="Select Destination Language"
+        )
+    ],
+    outputs=[gr.Textbox(label="Translated text", lines=4)],
+    title="@SahibhimGenAI Project 4: Multi language translator",
+    description="Translate English text to your selected language (loads the appropriate MarianMT model per language)."
+)
+demo.launch()