Create app.py

app.py

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+# def translate_and_speak(text):
+#     input_text = "en " + text
+#     encoded = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True).to(device)
+#     generated_tokens = model.generate(**encoded, max_length=128, num_beams=5, early_stopping=True)
+#     output = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
+
+#     for tag in ["__en__", "__sa__", "en", "sa"]:
+#         output = output.replace(tag, "")
+#     sanskrit_text = output.strip()
+
+#     # Convert to speech
+#     tts = gTTS(sanskrit_text, lang='hi')
+#     with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
+#         tts.save(fp.name)
+#         audio_path = fp.name
+
+#     return sanskrit_text, audio_path
+from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
+import torch
+from gtts import gTTS
+import gradio as gr
+import tempfile
+
+
+
+# Load model and tokenizer
+model__name = "Helsinki-NLP/opus-mt-en-hi"
+
+
+# Use GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
+
+model_name = "SweUmaVarsh/m2m100-en-sa-translation"
+tokenizer = M2M100Tokenizer.from_pretrained(model_name)
+model = M2M100ForConditionalGeneration.from_pretrained(model_name)
+
+
+
+def translate_and_speak(text):
+    input_text = "en " + text
+    encoded = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True).to(device)
+    generated_tokens = model.generate(**encoded, max_length=128, num_beams=5, early_stopping=True)
+    output = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
+
+    for tag in ["__en__", "__sa__", "en", "sa"]:
+        output = output.replace(tag, "")
+    sanskrit_text = output.strip()
+
+    # Convert to speech
+    tts = gTTS(sanskrit_text, lang='hi')
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
+        tts.save(fp.name)
+        audio_path = fp.name
+
+    return sanskrit_text, audio_path
+
+iface = gr.Interface(
+    fn=translate_and_speak,
+    inputs=gr.Textbox(label="Enter English Text"),
+    outputs=[gr.Textbox(label="Sanskrit Translation"), gr.Audio(label="Sanskrit Speech")],
+    title="Final Year Project: English to Sanskrit Translator (IT 'A' 2021–2025)",
+    description="Enter a sentence in English to get its Sanskrit translation and audio output."
+)
+
+iface.launch()