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="deepset/roberta-base-squad2")
+
+# question_answer = pipeline("question-answering", model=model_path)
+
+
+
+
+def read_file_content(file_path):
+    """
+    Reads the content of a file given its file path and returns it as a string.
+    """
+    try:
+        with open(file_path, "r", encoding="utf-8") as file:
+            return file.read()
+    except Exception as e:
+        return f"Error reading file: {e}"
+
+def get_answer(file, question):
+    context = read_file_content(file)  # 'file' is a path string, not a file object
+    if context.startswith("Error"):
+        return context  # Return error message if file reading fails
+    answer = question_answer(question=question, context=context)
+    return answer["answer"]
+
+demo = gr.Interface(fn=get_answer,
+                    inputs=[gr.File(label="input your context"),gr.Textbox(label="input your question",lines=1)],
+                    outputs=[gr.Textbox(label="Summarized text",lines=1)],
+                    title="@Naseem GenAI Project 2: Question Answering based on context provided",
+                    description="THIS APPLICATION WILL PROVIDE ANSWER BASED ON CONTEXT PROVIDED")
+demo.launch()
+