Create app.py

app.py

@@ -0,0 +1,29 @@
+import torch
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+import gradio as gr
+
+# Load the model and tokenizer
+model_name = "deepset/roberta-base-squad2"
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Setup the pipeline
+nlp = pipeline('question-answering', model=model, tokenizer=tokenizer)
+
+def answer_question(context, question):
+    """
+    Takes a context and a question, and returns the answer based on the context.
+    """
+    result = nlp(question=question, context=context)
+    return result['answer']
+
+# Define the Gradio interface
+iface = gr.Interface(fn=answer_question,
+                     inputs=[gr.inputs.Textbox(label="Context", placeholder="Enter the text here..."), 
+                             gr.inputs.Textbox(label="Question", placeholder="Enter your question here...")],
+                     outputs=gr.outputs.Textbox(label="Answer"),
+                     title="Question and Answer Assistant",
+                     description="Provide a context and ask a question based on that context. The assistant will find the answer for you.")
+
+if __name__ == "__main__":
+    iface.launch()