darinanina dina no

app.py

@@ -1,12 +1,38 @@
+from transformers import MBartForConditionalGeneration, MBart50Tokenizer
 import gradio as gr
 
-def greet(name, intensity):
-    return "Hello, " + name + "!" * int(intensity)
+# 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
 
 demo = gr.Interface(
-    fn=greet,
-    inputs=["text", "slider"],
-    outputs=["text"],
+  fn=answer_question,
+  inputs=["text", "text"],
+  outputs=["text"]
 )
 
-demo.launch()
+
+demo.launch()