Upload 🤖SQL_Agent.py

🤖SQL_Agent.py

@@ -1,320 +1,321 @@
-import re
-import streamlit as st
-from langchain_core.messages import HumanMessage, AIMessage
-from utils.llm_logic import generate_llm_response
-from utils.sql_utils import (
-    extract_sql_command,
-    load_defaultdb_schema_text,
-    load_defaultdb_queries,
-    load_data,
-)
-from utils.handle_sql_commands import execute_sql_duckdb
-
-
-st.set_page_config(
-    page_title="Text-to-SQL Agent",
-    page_icon="🤖",
-    layout="wide",
-    initial_sidebar_state="expanded",
-)
-
-default_db_questions = {}
-default_dfs = load_data()
-selected_df = default_dfs
-use_default_schema = True
-llm_option = "gemini"
-
-
-st.markdown(
-    """
-<style>
-    /* Base styles for both themes */
-    .stPageLink {
-        background-image: linear-gradient(to right, #007BFF, #6610F2); /* Gradient background */
-        color: white !important; /* Ensure text is readable on the gradient */
-        padding: 12px 20px !important; /* Slightly larger padding */
-        border-radius: 8px !important; /* More rounded corners */
-        border: none !important; /* Remove default border */
-        text-decoration: none !important;
-        font-weight: 500 !important; /* Slightly lighter font weight */
-        transition: transform 0.2s ease-in-out, box-shadow 0.2s ease-in-out; /* Smooth transitions */
-        box-shadow: 0 2px 5px rgba(0, 0, 0, 0.15); /* Subtle shadow for depth */
-        display: inline-flex;
-        align-items: center;
-        justify-content: center;
-    }
-
-    .stPageLink:hover {
-        transform: scale(1.03); /* Slight scale up on hover */
-        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2); /* Increased shadow on hover */
-    }
-
-    .stPageLink span { /* Style the label text */
-        margin-left: 5px; /* Space between icon and text */
-    }
-
-    /* Dark theme adjustments (optional, if needed for better contrast) */
-    /* Consider using Streamlit's theme variables if possible for a more robust solution */
-    /* For simplicity, this example uses fixed colors that should work reasonably well */
-    /* [data-theme="dark"] .stPageLink {
-    }
-
-    [data-theme="dark"] .stPageLink:hover {
-    } */
-</style>
-""",
-    unsafe_allow_html=True,
-)
-
-
-with st.popover("Click here to see Database Schema", use_container_width=True):
-    uploaded_df_schema = st.session_state.get("uploaded_df_schema", False)
-
-    choice = st.segmented_control(
-        "Choose",
-        ["Default DB", "Uploaded Files"],
-        label_visibility="collapsed",
-        disabled=uploaded_df_schema == False,
-        default="Default DB" if uploaded_df_schema == False else "Uploaded Files",
-    )
-
-    if uploaded_df_schema is False:
-        st.markdown(
-            """> You can also upload your own files, to get your schemas. You can then use those schemas to cross-check our answers with ChatGpt/Gemini/Claude (Preferred if the Question is very Complex). You can run the queries directly with our Manual SQL Executer😊.
-- Ask Questions
-- Run Queries: automatic + manual
-- Download Results """
-        )
-        st.page_link(
-            page="pages/3 📂File Upload for SQL.py",
-            label="Upload your own CSV or Excel files",
-            icon="📜",
-        )
-        schema = load_defaultdb_schema_text()
-        st.markdown(schema, unsafe_allow_html=True)
-    elif choice == "Default DB":
-        schema = load_defaultdb_schema_text()
-        st.markdown(schema, unsafe_allow_html=True)
-    else:
-        pretty_schema, markdown = st.tabs(["Schema", "Copy Schema in Markdown"])
-        with pretty_schema:
-            st.info(
-                "You can copy this schema, and give it to any state of the art LLM models like (Gemini /ChatGPT /Claude etc) to cross check your answers.\n You can run the queries directly here, by using ***Manual Query Executer*** in the sidebar and download your results 😊",
-                icon="ℹ️",
-            )
-            st.markdown(uploaded_df_schema, unsafe_allow_html=True)
-        with markdown:
-            st.info(
-                "You can copy this schema, and give it to any state of the art LLM models like (Gemini /ChatGPT /Claude etc) to cross check your answers.\n You can run the queries directly here, by using ***Manual Query Executer*** in the sidebar and download your results 😊",
-                icon="ℹ️",
-            )
-            st.markdown(f"```\n{uploaded_df_schema}\n```")
-
-
-col1, col2 = st.columns([2, 1], vertical_alignment="bottom")
-with col1:
-    st.header("Natural Language to SQL Query Agent🤖")
-
-with col2:
-    st.caption("> ***Execute on the Go!*** 🚀   In-Built DuckDB Execution Engine")
-
-st.caption(
-    "This is a Qwen2.5-Coder-3B model fine-tuned for SQL queries integrated with langchain for Agentic Workflow. To see the Fine-Tuning code - [click here](https://www.kaggle.com/code/debopamchowdhury/qwen-2-5coder-3b-instruct-finetuning)."
-)
-
-
-col1, col2, col3 = st.columns([1.5, 2, 1], vertical_alignment="top")
-with col1:
-    disabled_selection = True
-    if (
-        "uploaded_dataframes" in st.session_state
-    ) and st.session_state.uploaded_dataframes:
-        disabled_selection = False
-    options = ["default_db", "uploaded_files"]
-    selected = st.segmented_control(
-        "Choose",
-        options,
-        selection_mode="single",
-        disabled=disabled_selection,
-        label_visibility="collapsed",
-        default="default_db" if disabled_selection else "uploaded_files",
-    )
-    if not disabled_selection:
-        if selected == "uploaded_files":
-            selected_df = st.session_state.uploaded_dataframes
-            # print(selected_df)
-            use_default_schema = False
-        else:
-            selected_df = default_dfs
-            # print(selected_df)
-            use_default_schema = True
-    if selected_df == default_dfs:
-        with st.popover("Default Database Queries 📚 - Trial"):
-            default_db_questions = load_defaultdb_queries()
-            st.markdown(default_db_questions)
-
-with col2:
-    llm_option_radio = st.radio(
-        "Choose LLM Model",
-        ["Gemini-2.0-Flash-Exp", "FineTuned Qwen2.5-Coder-3B for SQL"],
-        captions=[
-            "Used via API",
-            "Run Locally on this Server. Extremely Slow because of Free vCPUs",
-        ],
-        label_visibility="collapsed",
-    )
-    if llm_option_radio == "Gemini-2.0-Flash":
-        llm_option = "gemini"
-    else:
-        llm_option = "qwen"
-
-with col3:
-    # Button to refresh the conversation
-    if st.button("Start New Conversation", type="primary"):
-        st.session_state.chat_history = []
-        st.session_state.conversation_turns = 0
-        st.rerun()
-
-# Initialize chat history in session state
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = []
-
-# Initialize conversation turn counter
-if "conversation_turns" not in st.session_state:
-    st.session_state.conversation_turns = 0
-
-# Set the maximum number of conversation turns
-MAX_TURNS = 5
-
-# Display existing chat messages
-for message in st.session_state.chat_history:
-    with st.chat_message(message.type):
-        st.markdown(message.content)
-        if (
-            isinstance(message, AIMessage)
-            and "response_df" in message.additional_kwargs
-            and message.additional_kwargs["response_df"] is not None
-            and not message.additional_kwargs["response_df"].empty
-        ):
-            with st.expander("View SQL-Query Execution Result"):
-                df = message.additional_kwargs["response_df"]
-                # download_csv = convert_df(df)
-                # st.download_button(
-                #     label="Download data as CSV",
-                #     data=download_csv,
-                #     file_name="query_results.csv",
-                #     mime="text/csv",
-                # )
-                # renderer = StreamlitRenderer(
-                #     df,
-                #     spec_io_mode="rw",
-                #     default_tab="data",
-                #     appearance="dark",
-                #     kernel_computation=True,
-                # )
-                # renderer.explorer(default_tab="data")
-                st.dataframe(df)
-                st.info(f"Rows x Columns: {df.shape[0]} x {df.shape[1]}")
-                st.subheader("Data Description:")
-                st.markdown(df.describe().T.to_markdown())
-                st.subheader("Data Types:")
-                st.write(df.dtypes)
-
-# Get user input only if the conversation turn limit is not reached
-if st.session_state.conversation_turns < MAX_TURNS:
-    if prompt := st.chat_input("Ask me a SQL query question"):
-        # Add user message to chat history in session state
-        st.session_state.chat_history.append(HumanMessage(content=prompt))
-        # Display user message in chat
-        with st.chat_message("user"):
-            st.markdown(prompt)
-
-        duckdb_result = None
-        # Get assistant response with streaming
-        with st.chat_message("assistant"):
-            message_placeholder = st.empty()
-            full_response = ""
-            spinner_text = ""
-            if llm_option == "gemini":
-                spinner_text = (
-                    "Using Gemini-2.0-Flash-Exp to run your query. Please wait...😊"
-                )
-            else:
-                spinner_text = "I know it is taking a lot of time. To run the model I'm using `Free` small vCPUs provided by `HuggingFace Spaces` for deployment. Thank you so much for your patience😊"
-            with st.spinner(
-                spinner_text,
-            ):
-                for response_so_far in generate_llm_response(
-                    prompt, llm_option, use_default_schema
-                ):
-                    # Remove <sql> and </sql> tags for streaming display
-                    streaming_response = response_so_far.replace("<sql>", "").replace(
-                        "</sql>", ""
-                    )
-                    # Remove duplicate ```sql tags with or without space for streaming display
-                    streaming_response = re.sub(
-                        r"```sql\s*```sql", "```sql", streaming_response
-                    )
-                    message_placeholder.markdown(streaming_response + "▌")
-                    full_response = response_so_far
-
-            # Remove <sql> and </sql> tags from the full response
-            full_response = full_response.replace("<sql>", "").replace("</sql>", "")
-            # Remove duplicate ```sql tags with or without space from the full response
-            full_response = re.sub(r"```sql\s*```sql", "```sql", full_response)
-            # Remove trailing duplicate ``` tags from the full response
-            full_response = re.sub(r"[\s\n]*`+$", "```", full_response)
-            message_placeholder.markdown(full_response)
-            # st.text(extract_sql_command(full_response))
-
-            sql_command = extract_sql_command(full_response)
-            # dataframe_html = None
-            if sql_command:
-                # st.text("Extracted SQL Command:")
-                # st.code(sql_command, language="sql")
-                duckdb_result = execute_sql_duckdb(sql_command, selected_df)
-                if duckdb_result is not None:
-                    st.text("Query Execution Result:")
-                    with st.expander("View Result"):
-                        # st.dataframe(duckdb_result)
-                        st.dataframe(duckdb_result)
-                        st.info(
-                            f"Rows x Columns: {duckdb_result.shape[0]} x {duckdb_result.shape[1]}"
-                        )
-                        st.subheader("Data Description:")
-                        st.markdown(duckdb_result.describe().T.to_markdown())
-                        st.subheader("Data Types:")
-                        st.write(duckdb_result.dtypes)
-                        # renderer = StreamlitRenderer(
-                        #     duckdb_result,
-                        #     spec_io_mode="rw",
-                        #     default_tab="data",
-                        #     appearance="dark",
-                        #     kernel_computation=True,
-                        # )
-                        # renderer.explorer(default_tab="data")
-
-            else:
-                # st.warning("No SQL command found in the response.")
-                pass
-
-        # Add assistant response to chat history in session state
-        st.session_state.chat_history.append(
-            AIMessage(
-                content=full_response,
-                additional_kwargs={"response_df": duckdb_result},
-            )
-        )
-
-        # Increment the conversation turn counter
-        st.session_state.conversation_turns += 1
-else:
-    st.warning(
-        "Maximum number of questions reached. Please click 'Start New Conversation' to continue."
-    )
-    st.chat_input(
-        "Ask me a SQL query question", disabled=True
-    )  # Disable the input field
-
-with st.sidebar:
-    st.caption("Made with ❤️ by @Debopam_Chowdhury")
+# main.py
+import re
+import streamlit as st
+from langchain_core.messages import HumanMessage, AIMessage
+from utils.llm_logic import generate_llm_response
+from utils.sql_utils import (
+    extract_sql_command,
+    load_defaultdb_schema_text,
+    load_defaultdb_queries,
+    load_data,
+)
+from utils.handle_sql_commands import execute_sql_duckdb
+
+
+st.set_page_config(
+    page_title="Text-to-SQL Agent",
+    page_icon="🤖",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+default_db_questions = {}
+default_dfs = load_data()
+selected_df = default_dfs
+use_default_schema = True
+llm_option = "gemini"
+
+
+st.markdown(
+    """
+<style>
+    /* Base styles for both themes */
+    .stPageLink {
+        background-image: linear-gradient(to right, #007BFF, #6610F2); /* Gradient background */
+        color: white !important; /* Ensure text is readable on the gradient */
+        padding: 12px 20px !important; /* Slightly larger padding */
+        border-radius: 8px !important; /* More rounded corners */
+        border: none !important; /* Remove default border */
+        text-decoration: none !important;
+        font-weight: 500 !important; /* Slightly lighter font weight */
+        transition: transform 0.2s ease-in-out, box-shadow 0.2s ease-in-out; /* Smooth transitions */
+        box-shadow: 0 2px 5px rgba(0, 0, 0, 0.15); /* Subtle shadow for depth */
+        display: inline-flex;
+        align-items: center;
+        justify-content: center;
+    }
+
+    .stPageLink:hover {
+        transform: scale(1.03); /* Slight scale up on hover */
+        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2); /* Increased shadow on hover */
+    }
+
+    .stPageLink span { /* Style the label text */
+        margin-left: 5px; /* Space between icon and text */
+    }
+
+    /* Dark theme adjustments (optional, if needed for better contrast) */
+    /* Consider using Streamlit's theme variables if possible for a more robust solution */
+    /* For simplicity, this example uses fixed colors that should work reasonably well */
+    /* [data-theme="dark"] .stPageLink {
+    }
+
+    [data-theme="dark"] .stPageLink:hover {
+    } */
+</style>
+""",
+    unsafe_allow_html=True,
+)
+
+
+with st.popover("Click here to see Database Schema", use_container_width=True):
+    uploaded_df_schema = st.session_state.get("uploaded_df_schema", False)
+
+    choice = st.segmented_control(
+        "Choose",
+        ["Default DB", "Uploaded Files"],
+        label_visibility="collapsed",
+        disabled=uploaded_df_schema == False,
+        default="Default DB" if uploaded_df_schema == False else "Uploaded Files",
+    )
+
+    if uploaded_df_schema is False:
+        st.markdown(
+            """> You can also upload your own files, to get your schemas. You can then use those schemas to cross-check our answers with ChatGpt/Gemini/Claude (Preferred if the Question is very Complex). You can run the queries directly with our Manual SQL Executer😊.
+- Ask Questions
+- Run Queries: automatic + manual
+- Download Results """
+        )
+        st.page_link(
+            page="pages/3 📂File Upload for SQL.py",
+            label="Upload your own CSV or Excel files",
+            icon="📜",
+        )
+        schema = load_defaultdb_schema_text()
+        st.markdown(schema, unsafe_allow_html=True)
+    elif choice == "Default DB":
+        schema = load_defaultdb_schema_text()
+        st.markdown(schema, unsafe_allow_html=True)
+    else:
+        pretty_schema, markdown = st.tabs(["Schema", "Copy Schema in Markdown"])
+        with pretty_schema:
+            st.info(
+                "You can copy this schema, and give it to any state of the art LLM models like (Gemini /ChatGPT /Claude etc) to cross check your answers.\n You can run the queries directly here, by using ***Manual Query Executer*** in the sidebar and download your results 😊",
+                icon="ℹ️",
+            )
+            st.markdown(uploaded_df_schema, unsafe_allow_html=True)
+        with markdown:
+            st.info(
+                "You can copy this schema, and give it to any state of the art LLM models like (Gemini /ChatGPT /Claude etc) to cross check your answers.\n You can run the queries directly here, by using ***Manual Query Executer*** in the sidebar and download your results 😊",
+                icon="ℹ️",
+            )
+            st.markdown(f"```\n{uploaded_df_schema}\n```")
+
+
+col1, col2 = st.columns([2, 1], vertical_alignment="bottom")
+with col1:
+    st.header("Natural Language to SQL Query Agent🤖")
+
+with col2:
+    st.caption("> ***Execute on the Go!*** 🚀   In-Built DuckDB Execution Engine")
+
+st.caption(
+    "This is a Qwen2.5-Coder-3B model fine-tuned for SQL queries integrated with langchain for Agentic Workflow. To see the Fine-Tuning code - [click here](https://www.kaggle.com/code/debopamchowdhury/qwen-2-5coder-3b-instruct-finetuning)."
+)
+
+
+col1, col2, col3 = st.columns([1.5, 2, 1], vertical_alignment="top")
+with col1:
+    disabled_selection = True
+    if (
+        "uploaded_dataframes" in st.session_state
+    ) and st.session_state.uploaded_dataframes:
+        disabled_selection = False
+    options = ["default_db", "uploaded_files"]
+    selected = st.segmented_control(
+        "Choose",
+        options,
+        selection_mode="single",
+        disabled=disabled_selection,
+        label_visibility="collapsed",
+        default="default_db" if disabled_selection else "uploaded_files",
+    )
+    if not disabled_selection:
+        if selected == "uploaded_files":
+            selected_df = st.session_state.uploaded_dataframes
+            # print(selected_df)
+            use_default_schema = False
+        else:
+            selected_df = default_dfs
+            # print(selected_df)
+            use_default_schema = True
+    if selected_df == default_dfs:
+        with st.popover("Default Database Queries 📚 - Trial"):
+            default_db_questions = load_defaultdb_queries()
+            st.markdown(default_db_questions)
+
+with col2:
+    llm_option_radio = st.radio(
+        "Choose LLM Model",
+        ["Gemini-2.0-Flash-Exp", "FineTuned Qwen2.5-Coder-3B for SQL"],
+        captions=[
+            "Used via API",
+            "Run Locally on this Server. Extremely Slow because of Free vCPUs, [Download & Run Locally](https://huggingface.co/DebopamC/Text-to-SQL__Qwen2.5-Coder-3B-FineTuned/tree/main)",
+        ],
+        label_visibility="collapsed",
+    )
+    if llm_option_radio == "Gemini-2.0-Flash-Exp":
+        llm_option = "gemini"
+    else:
+        llm_option = "qwen"
+
+with col3:
+    # Button to refresh the conversation
+    if st.button("Start New Conversation", type="primary"):
+        st.session_state.chat_history = []
+        st.session_state.conversation_turns = 0
+        st.rerun()
+
+# Initialize chat history in session state
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+# Initialize conversation turn counter
+if "conversation_turns" not in st.session_state:
+    st.session_state.conversation_turns = 0
+
+# Set the maximum number of conversation turns
+MAX_TURNS = 5
+
+# Display existing chat messages
+for message in st.session_state.chat_history:
+    with st.chat_message(message.type):
+        st.markdown(message.content)
+        if (
+            isinstance(message, AIMessage)
+            and "response_df" in message.additional_kwargs
+            and message.additional_kwargs["response_df"] is not None
+            and not message.additional_kwargs["response_df"].empty
+        ):
+            with st.expander("View SQL-Query Execution Result"):
+                df = message.additional_kwargs["response_df"]
+                # download_csv = convert_df(df)
+                # st.download_button(
+                #     label="Download data as CSV",
+                #     data=download_csv,
+                #     file_name="query_results.csv",
+                #     mime="text/csv",
+                # )
+                # renderer = StreamlitRenderer(
+                #     df,
+                #     spec_io_mode="rw",
+                #     default_tab="data",
+                #     appearance="dark",
+                #     kernel_computation=True,
+                # )
+                # renderer.explorer(default_tab="data")
+                st.dataframe(df)
+                st.info(f"Rows x Columns: {df.shape[0]} x {df.shape[1]}")
+                st.subheader("Data Description:")
+                st.markdown(df.describe().T.to_markdown())
+                st.subheader("Data Types:")
+                st.write(df.dtypes)
+
+# Get user input only if the conversation turn limit is not reached
+if st.session_state.conversation_turns < MAX_TURNS:
+    if prompt := st.chat_input("Ask me a SQL query question"):
+        # Add user message to chat history in session state
+        st.session_state.chat_history.append(HumanMessage(content=prompt))
+        # Display user message in chat
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        duckdb_result = None
+        # Get assistant response with streaming
+        with st.chat_message("assistant"):
+            message_placeholder = st.empty()
+            full_response = ""
+            spinner_text = ""
+            if llm_option == "gemini":
+                spinner_text = (
+                    "Using Gemini-2.0-Flash-Exp to run your query. Please wait...😊"
+                )
+            else:
+                spinner_text = "I know it is taking a lot of time. To run the model I'm using `Free` small vCPUs provided by `HuggingFace Spaces` for deployment. Thank you so much for your patience😊"
+            with st.spinner(
+                spinner_text,
+            ):
+                for response_so_far in generate_llm_response(
+                    prompt, llm_option, use_default_schema
+                ):
+                    # Remove <sql> and </sql> tags for streaming display
+                    streaming_response = response_so_far.replace("<sql>", "").replace(
+                        "</sql>", ""
+                    )
+                    # Remove duplicate ```sql tags with or without space for streaming display
+                    streaming_response = re.sub(
+                        r"```sql\s*```sql", "```sql", streaming_response
+                    )
+                    message_placeholder.markdown(streaming_response + "▌")
+                    full_response = response_so_far
+
+            # Remove <sql> and </sql> tags from the full response
+            full_response = full_response.replace("<sql>", "").replace("</sql>", "")
+            # Remove duplicate ```sql tags with or without space from the full response
+            full_response = re.sub(r"```sql\s*```sql", "```sql", full_response)
+            # Remove trailing duplicate ``` tags from the full response
+            full_response = re.sub(r"[\s\n]*`+$", "```", full_response)
+            message_placeholder.markdown(full_response)
+            # st.text(extract_sql_command(full_response))
+
+            sql_command = extract_sql_command(full_response)
+            # dataframe_html = None
+            if sql_command:
+                # st.text("Extracted SQL Command:")
+                # st.code(sql_command, language="sql")
+                duckdb_result = execute_sql_duckdb(sql_command, selected_df)
+                if duckdb_result is not None:
+                    st.text("Query Execution Result:")
+                    with st.expander("View Result"):
+                        # st.dataframe(duckdb_result)
+                        st.dataframe(duckdb_result)
+                        st.info(
+                            f"Rows x Columns: {duckdb_result.shape[0]} x {duckdb_result.shape[1]}"
+                        )
+                        st.subheader("Data Description:")
+                        st.markdown(duckdb_result.describe().T.to_markdown())
+                        st.subheader("Data Types:")
+                        st.write(duckdb_result.dtypes)
+                        # renderer = StreamlitRenderer(
+                        #     duckdb_result,
+                        #     spec_io_mode="rw",
+                        #     default_tab="data",
+                        #     appearance="dark",
+                        #     kernel_computation=True,
+                        # )
+                        # renderer.explorer(default_tab="data")
+
+            else:
+                # st.warning("No SQL command found in the response.")
+                pass
+
+        # Add assistant response to chat history in session state
+        st.session_state.chat_history.append(
+            AIMessage(
+                content=full_response,
+                additional_kwargs={"response_df": duckdb_result},
+            )
+        )
+
+        # Increment the conversation turn counter
+        st.session_state.conversation_turns += 1
+else:
+    st.warning(
+        "Maximum number of questions reached. Please click 'Start New Conversation' to continue."
+    )
+    st.chat_input(
+        "Ask me a SQL query question", disabled=True
+    )  # Disable the input field
+
+with st.sidebar:
+    st.caption("Made with ❤️ by @Debopam_Chowdhury")