Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import os
+import sqlite3
+from langchain_community.utilities.sql_database import SQLDatabase
+from langchain.chains import create_sql_query_chain
+from langchain_openai import AzureChatOpenAI
+from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool
+from operator import itemgetter
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import PromptTemplate
+from langchain_core.runnables import RunnablePassthrough
+from ydata_profiling import ProfileReport
+import streamlit.components.v1 as components
+import tempfile
+from langchain_openai import ChatOpenAI
+
+# Enhanced Page Configuration
+st.set_page_config(
+    page_title="Chat with Excel/CSV",
+    page_icon=":bar_chart:",
+    layout="centered",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS for styling
+st.markdown(
+    """
+    <style>
+    /* Main Layout */
+    .main {background-color: white;}
+    
+    /* Sidebar Styling */
+    .sidebar .sidebar-content {
+        background-color: #F1F5F9;
+        color: black;
+    }
+    .sidebar .sidebar-content .stButton>button, .sidebar .sidebar-content h1, .sidebar .sidebar-content h2 {
+        color: #1A202C;
+    }
+    /* Gradient Text for Main Greeting */
+    .greeting-text {
+        font-size: 3em;
+        color: transparent;
+        background-image: linear-gradient(90deg, #3b82f6, #ec4899);
+        -webkit-background-clip: text;
+        font-weight: 600;
+        text-align: center;
+    }
+    /* Chat Input Styling */
+    .stTextInput > div > input {
+        background-color: #F1F5F9;
+        color: #1A202C;
+        border-radius: 8px;
+        padding: 10px;
+        margin-top: 10px;
+        width: 100%;
+    }
+    /* Button Styling */
+    .stButton > button {
+        background-color: #3b82f6;
+        color: white;
+        border: none;
+        border-radius: 5px;
+        padding: 0.5em 1em;
+        font-size: 1em;
+        font-weight: 600;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Function to handle Q&A option
+def code_for_option_1(api_key):
+    st.write('<div class="greeting-text">Hello, Sangram!</div>', unsafe_allow_html=True)
+    st.sidebar.info("Ask any question about the uploaded Excel or CSV data.")
+    st.sidebar.image("https://miro.medium.com/v2/resize:fit:786/format:webp/1*qUFgGhSERoWAa08MV6AVCQ.jpeg", use_container_width=True)
+    
+    uploaded_file = st.file_uploader("Upload Excel or CSV file:", type=["xlsx", "csv"])
+    
+    if uploaded_file is not None:
+        # Use temporary file for uploaded content
+        with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
+            tmp_file.write(uploaded_file.read())
+            tmp_file_path = tmp_file.name
+        
+        # Load Excel or CSV file
+        if uploaded_file.name.endswith(".xlsx"):
+            df = pd.read_excel(tmp_file_path)
+        elif uploaded_file.name.endswith(".csv"):
+            df = pd.read_csv(tmp_file_path)
+
+        st.write("### Uploaded Data:")
+        st.dataframe(df.head(len(df)))
+
+        question = st.text_input("Ask a question:")
+        submit = st.button("Ask")
+        
+        if submit:
+            st.subheader("Answer:")
+            st.write("Please wait, answer is generating...")
+
+            # Initialize OpenAI chat model using the provided API key
+            llm_1 = ChatOpenAI(model="gpt-3.5-turbo", temperature=0, openai_api_key=api_key)
+
+            with sqlite3.connect(f"{uploaded_file.name}.db") as conn:
+                df.to_sql(f"{uploaded_file.name}s", conn, if_exists="replace")
+                db = SQLDatabase.from_uri(f"sqlite:///{uploaded_file.name}.db")
+                generate_query = create_sql_query_chain(llm_1, db)
+                execute_query = QuerySQLDataBaseTool(db=db)
+                
+                answer_prompt = PromptTemplate.from_template(
+                    """Given the following user question, SQL query, and SQL result, answer the question.
+                    Question: {question}
+                    SQL Query: {query}
+                    SQL Result: {result}
+                    Answer: """
+                )
+                
+                rephrase_answer = answer_prompt | llm_1 | StrOutputParser()
+                chain = (
+                    RunnablePassthrough.assign(query=generate_query)
+                    .assign(result=itemgetter("query") | execute_query)
+                    | rephrase_answer
+                )
+                
+                response = chain.invoke({"question": question})
+                st.subheader(response)
+
+# Function to handle EDA option
+def code_for_option_2():
+    st.sidebar.image("https://miro.medium.com/v2/resize:fit:702/1*Ra02AqsQlC0KV229EvM98g.png", use_container_width=True)
+    st.sidebar.info("Explore insights from the uploaded data.")
+
+    uploaded_file = st.file_uploader("Upload Excel or CSV file:", type=["xlsx", "csv"])
+    
+    if uploaded_file is not None:
+        with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
+            tmp_file.write(uploaded_file.read())
+            tmp_file_path = tmp_file.name
+
+        # Load Excel or CSV file
+        if uploaded_file.name.endswith(".xlsx"):
+            df = pd.read_excel(tmp_file_path)
+        elif uploaded_file.name.endswith(".csv"):
+            df = pd.read_csv(tmp_file_path)
+
+        st.write("### Uploaded Data:")
+        st.dataframe(df.head(len(df)))
+
+        st.subheader("Exploratory Data Analysis (EDA):")
+        st.write("Please wait, reports are generating...")
+        response = ProfileReport(df)
+        
+        response.to_file("data_profile_report.html")
+        with open("data_profile_report.html", "r", encoding="utf-8") as f:
+            data = f.read()
+        
+        components.html(data, width=800, height=600, scrolling=True)
+
+# Main UI layout
+def main():
+    st.sidebar.image("https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSZYA5fOIfm6K6v3Lrro3MXksMfO3SdglfSyg&s", use_container_width=True)
+    st.title("DocTalk : Chat with Excel/CSV")
+    st.sidebar.title("Options")
+    selected_option = st.sidebar.radio("Select an option:", ("Chat with Excel/CSV", "EDA"))
+
+    # Take user API key input
+    api_key = st.sidebar.text_input("Enter OpenAI API Key:", type="password")
+
+    if api_key:
+        if selected_option == "Chat with Excel/CSV":
+            code_for_option_1(api_key)
+        elif selected_option == "EDA":
+            code_for_option_2()
+        else:
+            st.write("Please select an option.")
+    else:
+        st.sidebar.warning("Please enter your OpenAI API key to proceed.")
+
+if __name__ == "__main__":
+    main()