Create app.py

app.py

@@ -0,0 +1,31 @@
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
+import gradio as gr
+
+model = "hosseinhimself/tara-roberta-base-fa-qa"
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model)
+model = AutoModelForQuestionAnswering.from_pretrained(model)
+
+# Create a QA pipeline
+qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)
+
+
+def answer_question(context, question):
+    response = qa_pipeline(question=question, context=context) 
+    return response['answer']
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=answer_question,
+    inputs=[
+        gr.inputs.Textbox(lines=10, placeholder="Enter context here..."),
+        gr.inputs.Textbox(lines=1, placeholder="Enter question here...")
+    ],
+    outputs="text",
+    title="Tara Question Answering Model",
+    description="This model answers questions based on the provided context."
+)
+
+# Launch the interface
+interface.launch()