Create app.py

app.py

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+import torch
+import gradio as gr
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+# model_path = "../Models/models--deepset--roberta-base-squad2/snapshots/adc3b06f79f797d1c575d5479d6f5efe54a9e3b4"
+
+# question_answer = pipeline("question-answering", model=model_path)
+
+question_answer = pipeline("question-answering", model="deepset/roberta-base-squad2")
+
+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}"
+
+# context = ("Mark Elliot Zuckerberg (/ˈzʌkərbɜːrɡ/; born May 14, 1984) is an American businessman who co-founded the social media service Facebook"
+#            " and its parent company Meta Platforms, of which he is the chairman, chief executive officer, and controlling shareholder. "
+#            "Zuckerberg has been the subject of multiple lawsuits regarding the creation and ownership of the website as well as issues such "
+#            "as user privacy. Zuckerberg briefly attended Harvard College, where he launched Facebook in February 2004 with his roommates "
+#            "Eduardo Saverin, Andrew McCollum, Dustin Moskovitz and Chris Hughes. Zuckerberg took the company public in May 2012 with majority "
+#            "shares. He became the world's youngest self-made billionaire[a] in 2008, at age 23, and has consistently ranked among the world's "
+#            "wealthiest individuals. According to Forbes, as of March 2025, Zuckerberg's estimated net worth stood at US$214.1 billion, "
+#            "making him the second richest individual in the world,[2] behind Elon Musk and before Jeff Bezos.")
+#
+# question = "What is DOB of Mark?"
+
+# answer = question_answer(question=question, context=context)
+#
+# print(answer)
+
+def get_answer(file, question):
+    context = read_file_content(file)
+    answer = question_answer(question=question, context=context)
+    return answer["answer"]
+
+gr.close_all()
+
+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="Answered text",lines=4)],
+                    title="@GenAILearniverse Project 5: Document Q n A",
+                    description="THIS APPLICATION WILL BE USED TO ANSWER QUESTION BASED ON CONTEXT PROVIDED.")
+
+demo.launch()