darinanina dina no

app.py

@@ -1,38 +1,12 @@
-from transformers import MBartForConditionalGeneration, MBart50Tokenizer
 import gradio as gr
 
-# Load the model and tokenizer
-model_name = "LocalDoc/mbart_large_qa_azerbaijan"
-tokenizer = MBart50Tokenizer.from_pretrained(model_name, src_lang="en_XX", tgt_lang="az_AZ")
-model = MBartForConditionalGeneration.from_pretrained(model_name)
-
-
-
-
-
-def answer_question(text, question):
-    # Prepare input text
-    input_text = f"context: {text} question: {question}"
-    inputs = tokenizer(input_text, return_tensors="pt", max_length=1280000, truncation=False, padding="max_length")
-    
-    # Generate answer
-    outputs = model.generate(
-        input_ids=inputs["input_ids"],
-        attention_mask=inputs["attention_mask"],
-        max_length=1280000,
-        num_beams=5,
-        early_stopping=True
-    )
-    
-    # Decode the answer
-    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return answer
+def greet(name, intensity):
+    return "Hello, " + name + "!" * int(intensity)
 
 demo = gr.Interface(
-  fn=answer_question,
-  inputs=["text", "question"],
-  outputs=["text"]
+    fn=greet,
+    inputs=["text", "slider"],
+    outputs=["text"],
 )
 
-
-demo.launch()
+demo.launch()