app.py

import os
import streamlit as st
import pandas as pd
import openai
import torch
import matplotlib.pyplot as plt
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from dotenv import load_dotenv
import anthropic

# Load environment variables
load_dotenv()
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
os.environ["ANTHROPIC_API_KEY"] = os.getenv("ANTHROPIC_API_KEY")

# UI Styling
st.markdown(
    """
    <style>
    .stButton button {
        background-color: #1F6FEB;
        color: white;
        border-radius: 8px;
        border: none;
        padding: 10px 20px;
        font-weight: bold;
    }
    .stButton button:hover {
        background-color: #1A4FC5;
    }
    .stTextInput > div > input {
        border: 1px solid #30363D;
        background-color: #161B22;
        color: #C9D1D9;
        border-radius: 6px;
        padding: 10px;
    }
    .stFileUploader > div {
        border: 2px dashed #30363D;
        background-color: #161B22;
        color: #C9D1D9;
        border-radius: 6px;
        padding: 10px;
    }
    .response-box {
        background-color: #161B22;
        padding: 10px;
        border-radius: 6px;
        margin-bottom: 10px;
        color: #FFFFFF;
    }
    </style>
    """,
    unsafe_allow_html=True
)

st.title("Excel Q&A Chatbot 📊")

# Model Selection
model_choice = st.selectbox("Select LLM Model", ["OpenAI GPT-3.5", "Claude 3 Haiku", "Mistral-7B"])

# Load appropriate model based on selection
if model_choice == "Mistral-7B":
    model_name = "mistralai/Mistral-7B-Instruct"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
    def ask_mistral(query):
        inputs = tokenizer(query, return_tensors="pt").to("cuda")
        output = model.generate(**inputs)
        return tokenizer.decode(output[0])

elif model_choice == "Claude 3 Haiku":
    client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
    def ask_claude(query):
        response = client.messages.create(
            model="claude-3-haiku",
            messages=[{"role": "user", "content": query}]
        )
        return response.content

else:
    client = openai.OpenAI()
    def ask_gpt(query):
        response = client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": query}]
        )
        return response.choices[0].message.content

# File Upload
uploaded_file = st.file_uploader("Upload an Excel file", type=["csv", "xlsx"])

if uploaded_file is not None:
    file_extension = uploaded_file.name.split(".")[-1].lower()
    df = pd.read_csv(uploaded_file) if file_extension == "csv" else pd.read_excel(uploaded_file)
    st.write("### Preview of Data:")
    st.write(df.head())
    
    # Extract metadata
    column_names = df.columns.tolist()
    data_types = df.dtypes.apply(lambda x: x.name).to_dict()
    missing_values = df.isnull().sum().to_dict()
    
    # Display metadata
    st.write("### Column Details:")
    st.write(pd.DataFrame({"Column": column_names, "Type": data_types.values(), "Missing Values": missing_values.values()}))
    
    # User Query
    query = st.text_input("Ask a question about this data:")
    
    if st.button("Submit Query"):
        if query:
            # Interpret the query using selected LLM
            if model_choice == "Mistral-7B":
                parsed_query = ask_mistral(f"Convert this question into a Pandas operation: {query}")
            elif model_choice == "Claude 3 Haiku":
                parsed_query = ask_claude(f"Convert this question into a Pandas operation: {query}")
            else:
                parsed_query = ask_gpt(f"Convert this question into a Pandas operation: {query}")
            
            # Execute the query
            try:
                result = eval(f"df.{parsed_query}")
                st.write("### Result:")
                st.write(result if isinstance(result, pd.DataFrame) else str(result))
                
                # If numerical data, show a visualization
                if isinstance(result, pd.Series) and result.dtype in ["int64", "float64"]:
                    fig, ax = plt.subplots()
                    result.plot(kind="bar", ax=ax)
                    st.pyplot(fig)
                
            except Exception as e:
                st.error(f"Error executing query: {str(e)}")
    
    # Memory for context retention
    if "query_history" not in st.session_state:
        st.session_state.query_history = []
    st.session_state.query_history.append(query)