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adding new model. {DeepSeek Coder}

My_SQL_Connection.py

@@ -4,22 +4,23 @@ import mysql.connector
 import pandas as pd
 ###======================================================================database details-=======================================================
 def database_details(host,user,password):
-    connection = mysql.connector.connect(
-        host = host,
-        user = user,
-        password = password,
-        buffered = True
-        )
-    cursor = connection.cursor()
-    databases = ("Show databases")
-    cursor.execute(databases)
-    db = []
-    for (databases) in cursor:
-        db.append(databases[0])
-
-    cursor.close()
-    connection.close()
-    return db, len(db)
+    
+        connection = mysql.connector.connect(
+            host = host,
+            user = user,
+            password = password,
+            buffered = True
+            )
+        cursor = connection.cursor()
+        databases = ("Show databases")
+        cursor.execute(databases)
+        db = []
+        for (databases) in cursor:
+            db.append(databases[0])
+
+        cursor.close()
+        connection.close()
+        return db, len(db)
 
 #### =========================================================================retrieving the tables==========================================================
 def tables_in_this_DB(host,user,password,db_name):
@@ -98,4 +99,23 @@ def create_table_command(host,user,password,db_name):
     cursor.close()
     connection.close()
 
-    return create_table_statements
+    return create_table_statements
+
+
+def retrieve_result(host,user,password,db_name,query):
+    db_config = {
+        'host': host,
+        'user': user,
+        'password': password,
+        'database': db_name,
+    }
+
+    connection = mysql.connector.connect(**db_config)
+    cursor = connection.cursor()
+    query = query
+    cursor.execute(query)
+    res = cursor.fetchall()
+
+    cursor.close()
+    connection.close()
+    return res

app.py

@@ -1,13 +1,12 @@
 import streamlit as st
 
-from My_SQL_Connection import database_details, tables_in_this_DB, printing_tables, create_table_command
+from My_SQL_Connection import database_details, tables_in_this_DB, printing_tables, create_table_command,retrieve_result
 from streamlit_option_menu import option_menu
-from model_functions import LOAD_GEMMA
+from model_functions import LOAD_GEMMA,DeepSeekCoder,LOAD_GEMMA_GGUF
 import torch
-
-
-user_name = 'arya'
-
+import mysql.connector
+user_name = 'arya & Shritama'
+st.set_page_config(page_title="My SQL Explorer", page_icon="🔍", layout="centered", initial_sidebar_state="expanded")
 
 
 if 'localhost' not in st.session_state:
@@ -18,23 +17,31 @@ if 'localhost' not in st.session_state:
 
 with st.sidebar:
     selected = option_menu("Querio Lingua", ["Log In", 'main functionalities','Chat with AI'],
-                           icons=['house', 'gear', 'robot'], menu_icon="cast", default_index=1)
+                           icons=['person-circle', 'info-circle-fill', 'chat-fill'], menu_icon="cast", default_index=0,
+                           styles={
+                        "container": {"padding": "5!important","background-color":'black'},
+                        "icon": {"color": "white", "font-size": "23px"}, 
+                        "nav-link": {"color":"white","font-size": "20px", "text-align": "left", "margin":"0px", "--hover-color": "gray"},
+                        "nav-link-selected": {"background-color": "#1B2135"},})
 
 if selected == 'Log In':
-    st.title(f'welcome to our web application :green[{user_name}]')
-    st.subheader('welcome to our MY SQL Database Explorer ~ ')
 
-    st.write('First Check how many databases you have in your server ~')
+    st.subheader('Please Log in into your MySql server by providing the following details ~ ')
+
     st.session_state.localhost = st.text_input("what is your host, (localhost if in local) or give the url", 'localhost',help='host')
     st.session_state.user = st.text_input("what is your user name (usually root)", 'root')
     st.session_state.password = st.text_input('Password', type='password')
 
 elif selected == 'main functionalities':
-    st.title(f'welcome to our web application :green[{user_name}]')
     st.subheader('welcome to our MY SQL Database Explorer ~ ')
     if st.button('All your databases ~ '):
-        db, l = database_details(st.session_state.localhost, st.session_state.user, st.session_state.password)
-        st.table(db)
+        try:
+            db, l = database_details(st.session_state.localhost, st.session_state.user, st.session_state.password)
+            st.table(db)
+        except mysql.connector.Error as e:
+            error_code = e.errno
+            st.warning(f"An error occurred (Error Code: {error_code}). Please check your login details.")
+
 
     st.subheader('Now we will see details of any database~ ')
 
@@ -44,31 +51,43 @@ elif selected == 'main functionalities':
         if not st.session_state.db_name:
             st.warning('Input database name first')
         else:
-            tables, l = tables_in_this_DB(st.session_state.localhost, st.session_state.user, st.session_state.password, st.session_state.db_name)
-            st.write(f'There is only {l} tables present in this database')
-            st.write(tables)
-
+            try:
+                tables, l = tables_in_this_DB(st.session_state.localhost, st.session_state.user, st.session_state.password, st.session_state.db_name)
+                st.write(f'There is only {l} tables present in this database')
+                st.markdown(f"**:rainbow[{tables[0][0]}]**")
+            except mysql.connector.Error as e:
+                st.warning("An error occured. Please select the correct database from the above list or check that you are loged in into your server.")
     st.subheader('check out tables~ ')
 
     if st.button('Print the tables~'):
-        tables_data = printing_tables(st.session_state.localhost, st.session_state.user, st.session_state.password, st.session_state.db_name)
-        for table_name, table_data in tables_data.items():
-            st.write(f"Table: {table_name}")
-            st.table(table_data)
+        try:
+            tables_data = printing_tables(st.session_state.localhost, st.session_state.user, st.session_state.password, st.session_state.db_name)
+            for table_name, table_data in tables_data.items():
+                st.write(f"Table: {table_name}")
+                st.table(table_data)
+        except mysql.connector.Error as e:
+            st.warning("An error occured. Please check that you have selected a database or have loged in into your server.")
 
     st.subheader('Retrieve the CREATE TABLE Statements')
 
-    if st.button('Generate statements'):
-        statements = create_table_command(st.session_state.localhost, st.session_state.user, st.session_state.password, st.session_state.db_name)
-        for table_name, table_statements in statements.items():
-            st.write(f'{table_name}')
-            st.session_state.table_commands = table_statements
-            st.code(table_statements)
+    statement_options = st.radio("Choose the Context option for chat",["Generate the Context for chat AI based on your tables",
+                                                                       "Give custom chat context"])
+    if statement_options == 'Generate the Context for chat AI based on your tables':
+        if st.button('Generate context'):
+            try:
+                statements = create_table_command(st.session_state.localhost, st.session_state.user, st.session_state.password, st.session_state.db_name)
+                for table_name, table_statements in statements.items():
+                    st.write(f'{table_name}')
+                    st.session_state.table_commands = table_statements
+                    st.code(table_statements)
+            except mysql.connector.Error as e:
+                st.warning('An error occured. Please check that you have selected a database or have loged in into your server.')
+    elif statement_options == 'Give custom chat context':
+        context = st.text_area("Paste your context here (Usually the tables schema)")
+        st.session_state.table_commands = context
 
 
 elif selected == 'Chat with AI':
-    #st.set_page_config(page_title='🧠MemoryBot🤖', layout='wide')
-    # Initialize session states
     if "generated" not in st.session_state:
         st.session_state["generated"] = []
     if "past" not in st.session_state:
@@ -103,32 +122,65 @@ elif selected == 'Chat with AI':
         st.session_state["past"] = []
         st.session_state["input"] = ""
     
-    with st.sidebar.expander("🛠️ ", expanded=False):
-        MODEL = st.selectbox(label='Model', options=['GEMMA-2B FINE TUNED'])
-        
+    #with st.sidebar.expander("Available Fine Tuned Models", expanded=False):
+    MODEL = st.sidebar.selectbox(label='Available Fine Tuned Models', options=['GEMMA-2B','Gemma-GGUF', 'DeepSeekCoder 1.3B'])
+    st.sidebar.warning('Load only one model at a time as it loads the model into cache so it may cause cache overload',icon="⚠️")
 
-    st.title("🤖 Chat Bot with 🧠")
-    st.subheader(" Powered by 🚀 GEMMA")
+    st.title("Querio Lingua 🤖")
+    st.markdown("Your own SQL code helper⭐")
+    st.markdown(" Powered by GEMMA & DeepSeek🚀")
 
 
     st.sidebar.button("New Chat", on_click = new_chat, type='primary')
+
     user_input = get_text()
 
     if user_input:
-        tokenizer,model = LOAD_GEMMA()
-        device = torch.device("cpu")
-        alpeca_prompt = f"""Below are sql tables schemas paired with instruction that describes a task. Using valid SQLite, write a response that appropriately completes the request for the provided tables.
-         ### Instruction: {user_input}. ### Input: {st.session_state.table_commands}
-                            ### Response:
-                            """
-        with st.status('Generating Result',expanded=False) as status:
-            inputs = tokenizer([alpeca_prompt], return_tensors="pt").to(device)
-            outputs = model.generate(**inputs, max_new_tokens=30)
-            output = tokenizer.decode(outputs[0], skip_special_tokens=False)
+        if MODEL == 'GEMMA-2B':
+
+            gemma_tokenizer,gemma_model = LOAD_GEMMA()
+            device = torch.device("cpu")
+            alpeca_prompt = f"""Below are sql tables schemas paired with instruction that describes a task. Using valid SQLite, write a response that appropriately completes the request for the provided tables.
+            ### Instruction: {user_input}. ### Input: {st.session_state.table_commands}
+                                ### Response:
+                                """
+            with st.status('Generating Result',expanded=False) as status:
+                inputs = gemma_tokenizer([alpeca_prompt], return_tensors="pt").to(device)
+                outputs = gemma_model.generate(**inputs, max_new_tokens=30)
+                output = gemma_tokenizer.decode(outputs[0], skip_special_tokens=True)
+                response_portion = output.split("### Response:")[-1].strip()
+
+                st.session_state.past.append(user_input)  
+                st.session_state.generated.append(response_portion)
+
+                status.update(label="Result Generated!", state="complete", expanded=False)
+
+
+        elif MODEL == 'Gemma-GGUF':
+
+            with st.status('Generating Result',expanded=False) as status:
+                response = LOAD_GEMMA_GGUF(user_input,st.session_state.table_commands)
+            response_portion = response.split("### Response:")[-1].strip()
             st.session_state.past.append(user_input)  
-            st.session_state.generated.append(output)
-            print(output)
-            status.update(label="Result Generated!", state="complete", expanded=False)
+            st.session_state.generated.append(response_portion)
+
+        elif MODEL == 'DeepSeekCoder 1.3B':
+            
+            with st.status('Generating Result',expanded=False) as status:
+                try:
+                    response_portion = DeepSeekCoder(user_input,st.session_state.table_commands)
+                    final_output = response_portion + f"\n {retrieve_result(st.session_state.localhost, st.session_state.user, st.session_state.password, st.session_state.db_name,response_portion)}"
+
+                    st.session_state.past.append(user_input)  
+                    st.session_state.generated.append(final_output)
+                    print(final_output)
+                except mysql.connector.Error as e:
+                    
+                    st.session_state.past.append(user_input)  
+                    st.session_state.generated.append(response_portion + '{Query not executable}')
+
+                status.update(label="Result Generated!", state="complete", expanded=False)
+
 
     download_str = []
     # Display the conversation history using an expander, and allow the user to download it

model_functions.py

@@ -1,19 +1,49 @@
 from transformers import AutoModelForCausalLM,AutoTokenizer
 import streamlit as st
+import torch
+from langchain.prompts import PromptTemplate
+from langchain_community.llms import CTransformers
 
 @st.cache_resource(show_spinner='Loading the Gemma model. Be patient🙏')
 def LOAD_GEMMA():
   model_id = "aryachakraborty/GEMMA-2B-NL-SQL"
   tokenizer = AutoTokenizer.from_pretrained(model_id)
-  model = AutoModelForCausalLM.from_pretrained(model_id,
-                                               low_cpu_mem_usage = True
-                                                ).cpu()
+  model = AutoModelForCausalLM.from_pretrained(model_id).cpu()
   return tokenizer,model
 
-
-def LOAD_MISTRAL():
-  model_id=''
+@st.cache_resource(show_spinner='Loading the DeepSeek Coder model. Be patient🙏')
+def DeepSeekCoder(user_input, context):
+  model_id='aryachakraborty/DeepSeek_1.3B_Fine_Tuned'
   tokenizer = AutoTokenizer.from_pretrained(model_id)
-  model = AutoModelForCausalLM.from_pretrained(model_id,
-                                               low_cpu_usage=True,
-                                               ).cpu()
+  model = AutoModelForCausalLM.from_pretrained(model_id
+                                               ).cpu()
+  device = torch.device("cpu")
+  alpeca_prompt = f"""You are an AI programming assistant, utilizing the Deepseek Coder model, developed by arya chakraborty, and your task is to convert natural language to sql queries. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer.\n
+            Below are sql tables schemas paired with instruction that describes a task. Using valid SQLite, write a response that appropriately completes the request for the provided tables.
+            ### Instruction: {user_input}. ### Input: {context}
+                                ### Response:
+                                """
+  inputs = tokenizer([alpeca_prompt.format(user_input=user_input, context=context)], return_tensors="pt").to(device)
+  outputs = model.generate(**inputs, max_new_tokens=30)
+  output = tokenizer.decode(outputs[0], skip_special_tokens=True)
+  response_portion = output.split("### Response:")[-1].strip()
+  return response_portion
+
+
+@st.cache_resource(show_spinner='Loading the GGUF GEMMA model. Be patient🙏')
+def LOAD_GEMMA_GGUF(user_input, context):
+    llm = CTransformers(
+        model='D:\ISnartech Folder\Project_Folder\Streamlit APP\GgufModels\Q4_K_M.gguf',
+        #model_type='llama',
+        config={'max_new_tokens': 256, 'temperature': 0.01}
+    )
+
+    alpeca_prompt = f"""Below are sql tables schemas paired with instruction that describes a task. Using valid SQLite, write a response that appropriately completes the request for the provided tables.
+            ### Instruction: {user_input}. ### Input: {context}
+                                ### Response:
+                                """
+
+    prompt = PromptTemplate(input_variables=['user_input', 'context'], template=alpeca_prompt)
+    #return prompt,llm
+    response = llm(prompt.format(user_input=user_input, context=context))
+    return response

requirements.txt

@@ -1,6 +1,3 @@
-bitsandbytes==0.42.0
-peft==0.8.2
-trl==0.7.10
 accelerate==0.27.1
 datasets==2.17.0
 transformers==4.38.0
@@ -8,4 +5,6 @@ streamlit
 streamlit_option_menu
 torch
 mysql.connector
-pandas
+pandas
+langchain_community
+langchain