Update app.py

app.py

@@ -31,7 +31,6 @@ iface = gr.Interface(
 )
 
 iface.launch()
-"""
 from transformers import pipeline, MBartTokenizer
 import gradio as gr
 
@@ -48,3 +47,39 @@ def convert_to_casual_hindi(text):
 # Gradio interface for deployment in Hugging Face Spaces
 iface = gr.Interface(fn=convert_to_casual_hindi, inputs="text", outputs="text", title="Formal to Casual Hindi Converter")
 iface.launch()
+"""
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+import gradio as gr
+
+# Load a multilingual T5 model pre-trained for Hindi
+model_name = "google/mt5-base"  # mT5-base works well for Hindi
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+def formal_to_casual_hindi(input_text):
+    """
+    Converts formal Hindi text into conversational Hindi using mT5.
+    """
+    # Prepare the input for conversational reformulation
+    prompt = f"Convert the following formal Hindi text to casual spoken Hindi: {input_text}"
+    
+    # Tokenize input
+    input_ids = tokenizer.encode(prompt, return_tensors="pt")
+    
+    # Generate conversational text
+    outputs = model.generate(input_ids, max_length=128, num_beams=5, early_stopping=True)
+    
+    # Decode the output
+    casual_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return casual_text
+
+# Gradio interface for the deployment
+iface = gr.Interface(
+    fn=formal_to_casual_hindi,
+    inputs="text",
+    outputs="text",
+    title="Formal to Casual Hindi Converter",
+    description="Convert formal Hindi text into conversational Hindi using AI."
+)
+
+iface.launch()