initial commit

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.db filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,4 @@
+processed/embeddings.csv
+processed/scraped.csv
+.DS_Store
+__pycache__

.streamlit/config.toml

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+[server]
+runOnSave = true
+headless = true
+maxUploadSize = 2000

README.md

@@ -10,3 +10,38 @@ pinned: false
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+
+
+# AI Chatbot Prototype on Business Insights  
+
+Having a sample database with a structure
+```
+    - application
+        - app_number
+        - amount
+        - amount_paid
+        - state. (APPROVED, REJECTED, PENDING_PAYMENT, PAID)
+        - office_code [FK]
+        - service_code [FK]
+        - date_created
+        - date_paid
+        - date_processed
+    - office
+        - office_name
+        - office_location_code [FK]
+    - location
+        - location_name
+        - location_code
+    - service
+        - service_code
+        - service_name
+```
+
+The chatbot  will provide answers from that database
+
+```
+    a- The number of applications rejected in a location during the current month
+    b- The trend of applications in particular states, for a location
+    c- Any question you think relevant from this DB
+```

app.py

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+import streamlit as st
+from openai import OpenAI
+from app_config import *
+from app_access_db import *
+
+
+model = "gpt-3.5-turbo"
+
+# ------------------------------------------------------------------------------------------------
+# SIDEBAR
+# ------------------------------------------------------------------------------------------------
+st.sidebar.title('OpenAI Business Chat')
+st.sidebar.write('This chat bot is build with Tools and Function feature of OpenAI to be able to answer question regarding applications and performance of officers')
+st.sidebar.markdown("""
+    ### Having a sample database with a structure
+        - application
+            - app_number
+            - amount
+            - amount_paid
+            - state. (APPROVED, REJECTED, PENDING_PAYMENT, PAID)
+            - office_code [FK]
+            - service_code [FK]
+            - date_created
+            - date_paid
+            - date_processed
+        - office
+            - office_name
+            - office_location_code [FK]
+        - location
+            - location_name
+            - location_code
+        - service
+            - service_code
+            - service_name
+                    
+    ### The chatbot  will provide answers from that database
+        - The number of applications rejected is a location during the current month
+        - The trend of applications in particular states, for a location
+        - Any question you think relevant from this DB
+""")
+
+def onchange_openai_key():
+    print(st.session_state.openai_key) 
+
+openai_key = st.sidebar.text_input('OpenAI key', on_change=onchange_openai_key, key='openai_key')
+
+def submit_openai_key(model=model):
+    if(openai_key == None or openai_key==''):
+        st.sidebar.write('Please provide the key before')
+        return
+    else:
+        client = OpenAI(api_key=openai_key)
+        model = model
+        completion = client.chat.completions.create(
+            model=model,  
+            messages=[
+                {"role": "system", "content": "You are an assistant giving simple and short answer for question of child"},
+                {"role": "user", "content": "count from 0 to 10"}
+            ]
+        )        
+        st.sidebar.write(f'Simple count : {completion.choices[0].message.content}')
+
+submit_key = st.sidebar.button(label='Submit', on_click=submit_openai_key)
+
+
+
+# ------------------------------------------------------------------------------------------------
+# CHAT
+# ------------------------------------------------------------------------------------------------
+
+st.title('OpenAI Business Chat')
+st.write(f'Ask any question that can be answer by the LLM {model}.')
+
+
+def askQuestion(model=model, question=''):
+    if(openai_key == None or openai_key==''):
+        print('Please provide the key before')
+        return 'LLM API is not defined. Please provide the key before'
+    else:
+        client = OpenAI(api_key=openai_key)
+        model = model
+        completion = client.chat.completions.create(
+            model=model,  
+            messages=[
+                {"role": "system", "content": f'{query_context}'},
+                {"role": "user", "content": f'{question}'}
+            ]
+        )        
+        return completion.choices[0].message.content
+
+class AssistantMessage:
+    def __init__(self):
+        self.sql : str
+        self.response_data : DataFrame
+
+
+
+def displayAssistantMessage( assistantMessage: AssistantMessage):
+    with st.chat_message("assistant"):
+        st.code(assistantMessage.sql, language='sql')
+        st.code(assistantMessage.response_data, language='markdown')
+        if assistantMessage.response_data.columns.size == 2:
+            st.bar_chart(assistantMessage.response_data, x=assistantMessage.response_data.columns[0], y=assistantMessage.response_data.columns[1])
+        if assistantMessage.response_data.columns.size == 1:
+            st.metric(label=assistantMessage.response_data.columns[0], value=f'{assistantMessage.response_data.values[0]}')
+            
+
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    if message["role"] == "user":
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+    elif message["role"] == "assistant":
+        displayAssistantMessage(message["content"])
+
+# React to user input
+if prompt := st.chat_input("What is up?"):
+    with st.status('Running', expanded=True) as status:
+        # Display user message in chat message container
+        st.chat_message("user").markdown(prompt)
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+
+        response = askQuestion(question=prompt)
+        st.code(response, language='sql')
+        response_data = run_query(response)
+        # Display assistant response in chat message container
+        assistanMsg = AssistantMessage()
+        assistanMsg.sql = response
+        assistanMsg.response_data = response_data
+        displayAssistantMessage(assistanMsg)
+        # with st.chat_message("assistant"):
+        #     st.code(response, language='sql')
+        #     st.caption(response_data)
+        #     st.bar_chart(response_data, x=response_data.columns[0], y=response_data.columns[1])
+            
+        # Add assistant response to chat history
+        st.session_state.messages.append({"role": "assistant", "content": assistanMsg})
+        status.update(label='Response of last question', state="complete", expanded=True)
+
+
+

app_access_db.py

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+import sqlite3
+from pandas import DataFrame
+
+DB_FILENAME = 'irembo_application_4.db'
+
+
+def run_query(query=''):
+    print(query)
+    conn = sqlite3.connect(DB_FILENAME)
+    cursor = conn.cursor()
+    cursor.execute(query)     
+    #data = cursor.fetchall()
+    #print(data)
+    #conn.close()
+    
+    df = DataFrame(cursor.fetchall())
+    df.columns = [i[0] for i in cursor.description]
+
+    # print(f'Field Names : {field_names}')
+    print(cursor.description)
+    print(df.head())
+    conn.close()
+    return df

app_config.py

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+query_context = """
+Given the following SQL tables, your job is to write queries given a user’s request.
+
+CREATE TABLE application (
+    application_id int,
+    application_number  varchar(10),
+    amount int,
+    amount_paid int,
+    state  varchar(10),
+    office_code varchar(10), 
+    service_code varchar(10), 
+    date_created datetime,
+    date_paid datetime,
+    date_processed datetime,
+    PRIMARY KEY (application_id),
+    FOREIGN KEY(office_code) REFERENCES Office(office_code),
+    FOREIGN KEY(service_code) REFERENCES Service(service_code)
+
+);
+
+CREATE TABLE Office (
+      office_code varchar(10),
+      office_name varchar(20),
+      location_code varchar(10),
+      PRIMARY KEY (office_code),
+      FOREIGN KEY(location_code) REFERENCES location(location_code)
+ );
+
+CREATE TABLE location (
+      location_code varchar(10),
+      location_name varchar(20),
+      PRIMARY KEY (location_code)
+    );
+
+CREATE TABLE service (
+      service_code varchar(10),
+      service_name varchar(20),
+      PRIMARY KEY (service_code)
+    );
+
+
+Important, The query should be in SQLite format
+Important, Your response should be only the SQL script in SQLite format with no comment and no explanation.
+
+"""

irembo_application_4.db

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a7b01b7ad04e5a60032efc417d0ef6165d20d9b367d0cb72bcb056e6714e0019
+size 5910528

requirements.txt

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+streamlit
+openai
+watchdog