Update app.py

app.py

@@ -3,8 +3,8 @@ import pandas as pd
 
 from haystack.schema import Answer
 from haystack.document_stores import InMemoryDocumentStore
-from haystack.pipelines import FAQPipeline
-from haystack.retriever.dense import EmbeddingRetriever
+from haystack.pipelines import FAQPipeline, ExtractiveQAPipeline
+from haystack.nodes import EmbeddingRetriever, TfidfRetriever, FARMReader 
 from haystack.utils import print_answers
 import logging
 
@@ -15,7 +15,7 @@ def start_haystack():
     retriever = EmbeddingRetriever(document_store=document_store, embedding_model='sentence-transformers/all-MiniLM-L6-v2', use_gpu=True, top_k=1)
     load_data_to_store(document_store,retriever)
     pipeline = FAQPipeline(retriever=retriever)
-    return pipeline
+    return pipeline, document_store
 
 def load_data_to_store(document_store, retriever):
     df = pd.read_csv('monopoly_qa-v1.csv')
@@ -26,11 +26,11 @@ def load_data_to_store(document_store, retriever):
     
     dicts = df.to_dict(orient="records")
     document_store.write_documents(dicts)
+    
+faq_pipeline, doc_store = start_haystack()
  
-pipeline = start_haystack()
- 
-def predict(question):
-    prediction = pipeline.run(question)
+def predict_faq(question):
+    prediction = faq_pipeline.run(question)
     answer =  prediction["answers"][0].meta
     faq_response = "FAQ Question: " + answer["query"] + "\n"+"Answer: " + answer["answer"]
     return faq_response
@@ -39,10 +39,14 @@ input_question =gr.inputs.Textbox(label="enter your monopoly question here")
 response = "text"
 examples = ["how much cash do we get to start with?", "at what point can I buy houses?", "what happens when I land on free parking?"]
 
-gr.Interface(
-    fn=predict,
-    inputs=input_question,
-    outputs=response,
-    examples=examples,
-    title="Monopoly FAQ Semantic Search",
-).launch()
+mon_faq = gr.Interface(
+            fn=predict_faq,
+            inputs=input_question,
+            outputs=response,
+            examples=examples,
+            title="Monopoly FAQ Semantic Search")
+
+feedback_answer = 
+
+def return_feedback(input_question,feedback_answer):
+