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.env

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+GEMINI_API_KEY="AIzaSyAVpLXDazfH6mSlo-CfMzQ4nq5YOnqEA9A"

.gitattributes

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+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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.gitignore

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+.env
+env

app.py

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+import streamlit as st
+from itertools import tee
+import google.generativeai as genai
+import sqlite3
+
+
+def connect_to_database(db_path):
+    # debugging
+    try:
+        conn = sqlite3.connect(db_path)
+        cursor = conn.cursor()
+        
+        # Running a test query
+        cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
+        tables = cursor.fetchall()
+        if tables:
+            print("Connection successful. Found tables:", tables)
+        else:
+            print("Connection successful but no tables found.")
+        
+        return conn
+    except sqlite3.Error as e:
+        print(f"Error connecting to database: {e}")
+        return None
+
+# Connect to the database
+db_connection = connect_to_database('phones.db')
+
+st.set_page_config(layout="wide")
+MODEL_AVATAR_URL = "./icon.png"
+
+GEMINI = st.secrets["GEMINI_API_KEY"]
+
+genai.configure(api_key=GEMINI)
+generation_config = {
+    "temperature": 0.7,
+    "top_p": 0.95,
+    "top_k": 0,
+    "max_output_tokens": 8192,
+}
+safety_settings = [
+    {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
+    {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
+    {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
+    {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
+]
+model = genai.GenerativeModel(model_name="gemini-1.0-pro",
+                              generation_config=generation_config,
+                              safety_settings=safety_settings)
+
+st.title("DBRX Instruct")
+
+with open("style.css") as css:
+    st.markdown(f'<style>{css.read()}</style>', unsafe_allow_html=True)
+
+if "messages" not in st.session_state:
+    st.session_state["messages"] = []
+
+def clear_chat_history():
+    st.session_state["messages"] = []
+
+st.button('Clear Chat', on_click=clear_chat_history)
+
+
+# Function to generate SQL query
+def generate_sql_query(user_input):
+    prompt = f"Generate an SQL query to find information based on the user's question: '{user_input}'. Note: The table name is 'phones' and the columns are 'ProductName', 'Brand', 'Storage', 'Color', 'Price', 'QuantityInStock', and 'Location'. Note: GIve the query without '''sql at the start and end of the query. just give the query text content"
+    response = model.generate_content(prompt)
+    print(response.text)
+    return response.text 
+
+def execute_sql_query(query):
+    try:
+        cursor = db_connection.cursor()
+        cursor.execute(query)
+        results = cursor.fetchall()
+        cursor.close()
+        print(results)
+        return results
+    except Exception as e:
+        return f"Error executing query: {str(e)}"
+
+def format_response(user_input, query_results):
+    if not query_results:
+        return "No data found for your query."
+
+    result_text = f"Found {len(query_results)} results: " + ', '.join([str(item) for sublist in query_results for item in sublist])
+    
+    prompt = f"Rephrase this in a more conversational and informative way based on the user's question: '{user_input}'. Here are the details: {result_text}. Answer the user's question in a conversational manner. Note: as its a conversational response, give the response in correct mannser with correct formatting"
+    formatted_response = model.generate_content(prompt)
+    print(formatted_response.text)
+    return formatted_response.text
+
+
+def handle_user_input(user_input):
+    with history:
+        st.session_state["messages"].append({"role": "user", "content": user_input})
+        with st.chat_message("user"):
+            st.markdown(user_input)
+        
+        # Generate SQL query from user input
+        sql_query = generate_sql_query(user_input)
+        if sql_query:
+            # Execute the generated SQL query
+            query_results = execute_sql_query(sql_query)
+            formatted_answer = format_response(user_input, query_results)
+            
+            with st.chat_message("assistant", avatar=MODEL_AVATAR_URL):
+                st.markdown(formatted_answer)
+                st.session_state["messages"].append({"role": "assistant", "content": formatted_answer})
+        else:
+            with st.chat_message("assistant", avatar=MODEL_AVATAR_URL):
+                st.markdown("Failed to generate a valid SQL query.")
+                st.session_state["messages"].append({"role": "assistant", "content": "Failed to generate a valid SQL query."})
+
+
+main = st.container()
+with main:
+    history = st.container(height=400)
+    with history:
+        for message in st.session_state["messages"]:
+            avatar = None
+            if message["role"] == "assistant":
+                avatar = MODEL_AVATAR_URL
+            with st.chat_message(message["role"], avatar=avatar):
+                st.markdown(message["content"])
+
+    if prompt := st.chat_input("Type your question:", max_chars=1000):
+        handle_user_input(prompt)

create_database.py

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+import sqlite3
+
+# Connect to the SQLite database (creates a new file if it doesn't exist)
+conn = sqlite3.connect('phones.db')
+cursor = conn.cursor()
+
+# Create the table
+cursor.execute("""
+CREATE TABLE IF NOT EXISTS phones (
+    ProductID INTEGER PRIMARY KEY,
+    ProductName TEXT,
+    Brand TEXT,
+    Storage TEXT,
+    Color TEXT,
+    Price REAL,
+    QuantityInStock INTEGER,
+    Location TEXT
+)
+""")
+
+# Sample data
+data = [
+    (1, 'iPhone 13', 'Apple', '128GB', 'Blue', 999, 20, 'Shelf 1'),
+    (2, 'Galaxy S21', 'Samsung', '256GB', 'Phantom Black', 899, 15, 'Shelf 2'),
+    (3, 'Pixel 6', 'Google', '128GB', 'Stormy Black', 799, 18, 'Shelf 3'),
+    (4, 'OnePlus 9', 'OnePlus', '256GB', 'Winter Mist', 729, 25, 'Shelf 1'),
+    (5, 'Xperia 1 III', 'Sony', '256GB', 'Frosted Black', 1199, 10, 'Shelf 2'),
+    (6, 'Mi 11X', 'Xiaomi', '128GB', 'Cosmic Black', 599, 30, 'Shelf 3'),
+    (7, 'Find X3 Pro', 'OPPO', '256GB', 'Gloss Black', 1099, 12, 'Shelf 1'),
+    (8, 'ROG Phone 5', 'ASUS', '256GB', 'Phantom Black', 999, 20, 'Shelf 2'),
+    (9, 'Vivo X60 Pro', 'Vivo', '256GB', 'Midnight Black', 799, 15, 'Shelf 3'),
+    (10, 'Moto G Power 2021', 'Motorola', '64GB', 'Aurora Black', 249, 35, 'Shelf 1')
+]
+
+# Insert the data
+cursor.executemany("INSERT INTO phones VALUES (?, ?, ?, ?, ?, ?, ?, ?)", data)
+conn.commit()
+
+# Close the connection
+conn.close()

requirements.txt

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+google-generativeai
+streamlit

style.css

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+.st-emotion-cache-1tpusnk a{
+    color: #FF5F46;
+}