Create app.py

app.py

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+import torch
+import gradio as gr
+from transformers import pipeline
+
+# Use a Hebrew question-answering model
+model_name = "avichr/heBERT"
+
+question_answer = pipeline("question-answering", model=model_name, tokenizer=model_name)
+
+def read_file_content(file_obj):
+    """
+    Reads the content of a file object and returns it.
+    Parameters:
+    file_obj (file object): The file object to read from.
+    Returns:
+    str: The content of the file.
+    """
+    try:
+        with open(file_obj.name, 'r', encoding='utf-8') as file:
+            context = file.read()
+            return context
+    except Exception as e:
+        return f"An error occurred: {e}"
+
+def get_answer(file, question):
+    context = read_file_content(file)
+    answer = question_answer(question=question, context=context)
+    return answer["answer"]
+
+demo = gr.Interface(
+    fn=get_answer,
+    inputs=[gr.File(label="Upload your file"), gr.Textbox(label="Input your question", lines=1)],
+    outputs=[gr.Textbox(label="Answer text", lines=1)],
+    title="Document Q & A - Hebrew",
+    description="This application will be used to answer questions based on the context provided."
+)
+
+demo.launch()