resolving error

app.py

@@ -0,0 +1,131 @@
+from haystack.document_stores import FAISSDocumentStore
+from haystack.nodes import DensePassageRetriever, FARMReader
+from haystack.pipelines import ExtractiveQAPipeline, DocumentSearchPipeline
+from haystack.utils import clean_wiki_text, print_answers
+from sentence_transformers import SentenceTransformer
+from haystack.nodes import EmbeddingRetriever
+
+import streamlit as st
+import os
+import pandas as pd
+from datetime import datetime
+import json
+
+
+
+def intializeFAISS(docs):
+    try:        
+        db_file_name='faiss_document_store.db'
+        faiss_index_file='faiss_index'
+
+        if 'reader' not in st.session_state:
+            st.session_state.reader=FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=False)    
+
+        if  os.path.exists(faiss_index_file):
+            document_store=FAISSDocumentStore(
+                sql_url=f"sqlite:///{db_file_name}",
+                index=faiss_index_file,
+                embedding_dim=384
+            )  
+        else:       
+            document_store = FAISSDocumentStore(embedding_dim=384, faiss_index_factory_str="Flat")
+
+        if 'retriever' not in st.session_state:
+            st.session_state.retriever = EmbeddingRetriever(
+                            document_store=document_store,
+                            embedding_model="sentence-transformers/all-MiniLM-L6-v2",
+                            model_format="sentence_transformers",
+                            use_gpu=False
+                        ) 
+            
+        document_store.delete_all_documents()
+        document_store.write_documents(docs)      
+        document_store.update_embeddings(st.session_state.retriever)
+        document_store.save(index_path=faiss_index_file)  
+
+    except Exception as ex:
+        print('--error--')
+        print('--intialization method--')        
+        print(ex)
+        
+
+def prepare_doc(r):
+        _content=f'''{r['Salutation']} {r['Initial']} {r['Name']}, Employee Id is {r['Employee Id']} and user id is {r['User Id']},  is from country {r['Country']},  Supervisor / Manager name is {r['Supervisor / Manager']}  and Contracting Company is  {r['Contracting Company']},  Primary Industry is {r['Primary Industry']}  and Secondary Industry	is {r['Secondary Industry']}, there Sector is {r['Sector']}, they are expertise in {r['Expertise']}, there role is {r['Industry Role']}. There last Last Promotion Date is  {r['Last Promotion Date']} and {r['Last Promotional Level']}, There Job Title is {r['Job Title']}, they are working here since {r['Professional Since']}, there hired date is {r['Hired Date']}, there Relevant is {r['Relevant']}, Employee Sponser is {r['Employee Sponser']}, Job Description is {r['Job Description']},  Emergency Contact Name is {r['Emergency Contact Name']} and Emergency Contact Number  is {r['Emergency Contact Number']}, Regional Supervisor is {r['Regional Supervisor']}  Office Supervisor is {r['Office Supervisor']} Engagement Supervisor is {r['Engagement Supervisor']} '''
+        return {
+            'content':_content,
+            'meta':{
+                "Salutation":r["Salutation"],
+                "Initial":r["Initial"],
+                "User Id" :r["User Id"],
+                "Name" :r["Name"],
+                "Employee Id" :r["Employee Id"],
+                "Country":r["Country"],
+                "Supervisor / Manager" :r["Supervisor / Manager"],
+                "Contracting Company" :r["Contracting Company"],
+                "Primary Industry":r["Primary Industry"],
+                "Secondary Industry" :r["Secondary Industry"],
+                "Sector" :r["Sector"],
+                "Expertise":r["Expertise"],
+                "Industry Role":r["Industry Role"],
+                "Designation" :r["Designation"],
+                "Grade" :r["Grade"],
+                "Target Chargeability %" :r["Target Chargeability %"],
+                "Charge Out Rate":r["Charge Out Rate"],
+                "Last Promotion Date" :r["Last Promotion Date"],
+                "Last Promotional Level" :r["Last Promotional Level"],
+                "Job Title":r["Job Title"],
+                "Professional Since" :r["Professional Since"],
+                "Hired Date" :r["Hired Date"],
+                "Relevant":r["Relevant"],
+                "Employee Sponser":r["Employee Sponser"],
+                "Job Description" :r["Job Description"],
+                "Emergency Contact Name" :r["Emergency Contact Name"],
+                "Emergency Contact Number":r["Emergency Contact Number"],
+                "Regional Supervisor" :r["Regional Supervisor"],
+                "Office Supervisor" :r["Office Supervisor"],
+                "Engagement Supervisor":r["Engagement Supervisor"]
+            }
+        }
+
+
+def on_submission(question):
+    try:        
+        print('reader')
+        print(st.session_state.reader)
+        print('retriever')
+        print(st.session_state.retriever)
+        qa_pipeline=ExtractiveQAPipeline(reader=st.session_state.reader,retriever=st.session_state.retriever)
+        prediction=qa_pipeline.run(query=question,params={
+            "Retriever":{"top_k":5},
+            "Reader":{"top_k":1}
+                                            })
+        
+        if prediction:            
+            df=st.dataframe(prediction['answers'][0])
+            print(type(df))
+                
+
+    except Exception as ex:
+        print('--error--')
+        print('--on_submission method--')        
+        print(ex)
+
+uploaded_file=st.file_uploader(label='please upload your file',type=['csv'])
+
+if uploaded_file is not None:
+    df=pd.read_csv(uploaded_file)
+    df['formatted_content']=df.apply(prepare_doc,axis=1)
+    document=df['formatted_content'].to_numpy()
+    intializeFAISS(docs=document)
+
+#intialize form
+with st.form(key='workforce_management'):
+    st.title('workforce management')    
+    
+    question=st.text_input(label='please ask your question.')    
+    submitted=st.form_submit_button(label='submit')
+
+if submitted and question:
+    on_submission(question)
+
+

requirements.txt

@@ -0,0 +1,6 @@
+sentence_transformers
+transformers
+SQLAlchemy==1.4.46
+farm-haystack==1.26.3 #not installed
+faiss-cpu
+streamlit