Create app.py

app.py

@@ -0,0 +1,28 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load the trained model
+model_path = "bart_QA"
+qa_pipeline = pipeline("question-answering", model=model_path, tokenizer=model_path)
+
+def answer_question(question, context):
+    if question.strip() == "" or context.strip() == "":
+        return "Please provide both a question and context."
+    
+    result = qa_pipeline(question=question, context=context)
+    return result['answer']
+
+# Define the Gradio interface
+input_question = gr.inputs.Textbox(label="Question", placeholder="Enter your question here")
+input_context = gr.inputs.Textbox(label="Context", placeholder="Enter the context here", lines=5)
+output_answer = gr.outputs.Textbox(label="Answer")
+
+interface = gr.Interface(
+    fn=answer_question,
+    inputs=[input_question, input_context],
+    outputs=output_answer,
+    title="BART QA System",
+    description="Provide a question and context, and get an answer based on the context."
+)
+
+interface.launch()