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pages/Create_ML_Model.py

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+import streamlit as st
+from pages.admin_utils import *
+from sklearn.svm import SVC
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+import joblib
+
+from pages.admin_utils import *
+
+
+if 'cleaned_data' not in st.session_state:
+    st.session_state['cleaned_data'] =''
+if 'sentences_train' not in st.session_state:
+    st.session_state['sentences_train'] =''
+if 'sentences_test' not in st.session_state:
+    st.session_state['sentences_test'] =''
+if 'labels_train' not in st.session_state:
+    st.session_state['labels_train'] =''
+if 'labels_test' not in st.session_state:
+    st.session_state['labels_test'] =''
+if 'svm_classifier' not in st.session_state:
+    st.session_state['svm_classifier'] =''
+
+ 
+st.title("Let's build our Model...")
+ 
+# Create tabs
+tab_titles = ['Data Preprocessing', 'Model Training', 'Model Evaluation',"Save Model"]
+tabs = st.tabs(tab_titles)
+
+# Adding content to each tab
+
+#Data Preprocessing TAB
+with tabs[0]:
+    st.header('Data Preprocessing')
+    st.write('Here we preprocess the data...')
+
+    # Capture the CSV file
+    data = st.file_uploader("Upload CSV file",type="csv")
+
+    button = st.button("Load data",key="data")
+
+    if button:
+        with st.spinner('Wait for it...'):
+            our_data=read_data(data)
+            embeddings=get_embeddings()
+            st.session_state['cleaned_data'] = create_embeddings(our_data,embeddings)
+        st.success('Done!')
+
+
+#Model Training TAB
+with tabs[1]:
+    st.header('Model Training')
+    st.write('Here we train the model...')
+    button = st.button("Train model",key="model")
+    
+    if button:
+            with st.spinner('Wait for it...'):
+                st.session_state['sentences_train'], st.session_state['sentences_test'], st.session_state['labels_train'], st.session_state['labels_test']=split_train_test__data(st.session_state['cleaned_data'])
+                
+                # Initialize a support vector machine, with class_weight='balanced' because 
+                # our training set has roughly an equal amount of positive and negative 
+                # sentiment sentences
+                st.session_state['svm_classifier']  = make_pipeline(StandardScaler(), SVC(class_weight='balanced')) 
+
+                # fit the support vector machine
+                st.session_state['svm_classifier'].fit(st.session_state['sentences_train'], st.session_state['labels_train'])
+            st.success('Done!')
+
+#Model Evaluation TAB
+with tabs[2]:
+    st.header('Model Evaluation')
+    st.write('Here we evaluate the model...')
+    button = st.button("Evaluate model",key="Evaluation")
+
+    if button:
+        with st.spinner('Wait for it...'):
+            accuracy_score=get_score(st.session_state['svm_classifier'],st.session_state['sentences_test'],st.session_state['labels_test'])
+            st.success(f"Validation accuracy is {100*accuracy_score}%!")
+
+
+            st.write("A sample run:")
+
+
+            #text="lack of communication regarding policy updates salary, can we please look into it?"
+            text="Rude driver with scary driving"
+            st.write("***Our issue*** : "+text)
+
+            #Converting out TEXT to NUMERICAL representaion
+            embeddings= get_embeddings()
+            query_result = embeddings.embed_query(text)
+
+            #Sample prediction using our trained model
+            result= st.session_state['svm_classifier'].predict([query_result])
+            st.write("***Department it belongs to*** : "+result[0])
+            
+
+        st.success('Done!')
+
+#Save model TAB
+with tabs[3]:
+    st.header('Save model')
+    st.write('Here we save the model...')
+
+    button = st.button("Save model",key="save")
+    if button:
+
+        with st.spinner('Wait for it...'):
+             joblib.dump(st.session_state['svm_classifier'], 'modelsvm.pk1')
+        st.success('Done!')

pages/Load_Data_Store.py

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+import streamlit as st
+from dotenv import load_dotenv
+from pages.admin_utils import *
+
+
+def main():
+    load_dotenv()
+    st.set_page_config(page_title="Dump PDF to Pinecone - Vector Store")
+    st.title("Please upload your files...📁 ")
+
+    # Upload the pdf file
+    pdf = st.file_uploader("Only PDF files allowed", type=["pdf"])
+
+    # Extract the whole text from the uploaded pdf file
+    if pdf is not None:
+        with st.spinner('Wait for it...'):
+            text=read_pdf_data(pdf)
+            st.write("👉Reading PDF done")
+
+            # Create chunks
+            docs_chunks=split_data(text)
+            #st.write(docs_chunks)
+            st.write("👉Splitting data into chunks done")
+
+            # Create the embeddings
+            embeddings=create_embeddings_load_data()
+            st.write("👉Creating embeddings instance done")
+
+            # Build the vector store (Push the PDF data embeddings)
+            push_to_pinecone("e697b71c-d5ed-4c66-8625-ac1c403a2df1","us-west1-gcp-free","tickets",embeddings,docs_chunks)
+
+        st.success("Successfully pushed the embeddings to Pinecone")
+
+
+if __name__ == '__main__':
+    main()

pages/Pending_tickets.py

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+import streamlit as st
+ 
+st.title('Departments')
+ 
+# Create tabs
+tab_titles = ['HR Support', 'IT Support', 'Transportation Support']
+tabs = st.tabs(tab_titles)
+ 
+# Add content to each tab
+with tabs[0]:
+    st.header('HR Support tickets')
+    for ticket in st.session_state['HR_tickets']:
+        st.write(str(st.session_state['HR_tickets'].index(ticket)+1)+" : "+ticket)
+    
+ 
+with tabs[1]:
+    st.header('IT Support tickets')
+    for ticket in st.session_state['IT_tickets']:
+        st.write(str(st.session_state['IT_tickets'].index(ticket)+1)+" : "+ticket)
+ 
+with tabs[2]:
+    st.header('Transportation Support tickets')
+    for ticket in st.session_state['Transport_tickets']:
+        st.write(str(st.session_state['Transport_tickets'].index(ticket)+1)+" : "+ticket)
+    
+