Update app.py

app.py

@@ -48,29 +48,18 @@ def convert_to_casual_hindi(text):
 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
-
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-# Load the model and tokenizer
-model_name = "google/mt5-base"  # You can replace with another model name if needed
-tokenizer = AutoTokenizer.from_pretrained(model_name, legacy=True)  # Suppress legacy warning
+# Use the IndicTrans model
+model_name = "ai4bharat/indictrans-hin-eng"  # IndicTrans 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
+    # Prepare input for IndicTrans
+    prompt = f"Convert formal Hindi to casual Hindi: {input_text}"
     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