add inference class

app.py

@@ -1,7 +1,13 @@
 import gradio as gr
+from inference import  Question_and_Answer_System
 
-def greet(name):
-    return "Hello " + name + "!!"
+qa = Question_and_Answer_System() 
+
+
+
+def greet(question):
+    prediction = qa.answer_question(question)
+    return prediction
 
 iface = gr.Interface(fn=greet, inputs="text", outputs="text")
 iface.launch()

inference.py

@@ -0,0 +1,64 @@
+import os
+import logging
+from haystack.pipelines.standard_pipelines import TextIndexingPipeline
+from haystack.document_stores import InMemoryDocumentStore
+from haystack.nodes import BM25Retriever
+from haystack.nodes import FARMReader
+from haystack.pipelines import ExtractiveQAPipeline
+from haystack.utils import print_answers
+
+
+class Question_and_Answer_System:
+    def __init__(self):
+        self.pipe_line = None
+        doc_dir = "data"
+        document_store = self.prepare_documents(doc_dir)
+        self.reader = self.create_reader(document_store)
+        self.retriever = self.create_retriever(document_store)
+        self.pipe_line = self.create_pipeline(self.reader, self.retriever)
+
+    def setup_logging(self):
+        logging.basicConfig(
+            format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING
+        )
+        logging.getLogger("haystack").setLevel(logging.INFO)
+
+    def prepare_documents(self, doc_dir):
+        document_store = InMemoryDocumentStore(use_bm25=True)
+        doc_dir = "data"
+        files_to_index = [doc_dir + "/" + f for f in os.listdir(doc_dir)]
+        indexing_pipeline = TextIndexingPipeline(document_store)
+        indexing_pipeline.run_batch(file_paths=files_to_index)
+        return document_store
+
+    def create_retriever(self, document_store):
+        retriever = BM25Retriever(document_store=document_store)
+        return retriever
+
+    def create_reader(self, document_store):
+        reader = FARMReader(
+            model_name_or_path="deepset/roberta-base-squad2", use_gpu=False
+        )
+        return reader
+
+    def create_pipeline(self, reader, retriever):
+        self.pipe_line = ExtractiveQAPipeline(reader, retriever)
+        return self.pipe_line
+
+    def answer_question(self, question: str):
+        prediction = self.pipe_line.run(
+            query=question,
+            params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}},
+        )
+
+        return prediction
+
+    def format_answers(self, prediction):
+        print_answers(
+            prediction, details="minimum"  ## Choose from `minimum`, `medium`, and `all`
+        )
+
+    
+
+
+