| import streamlit as st | |
| import os | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from langchain.document_loaders.csv_loader import CSVLoader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain.vectorstores import FAISS | |
| from langchain.llms import CTransformers | |
| from langchain.chains import ConversationalRetrievalChain | |
| def add_vertical_space(spaces=1): | |
| for _ in range(spaces): | |
| st.sidebar.markdown("---") | |
| def plot_histogram(df): | |
| numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns.tolist() | |
| if numeric_columns: | |
| selected_column = st.selectbox('Select column for histogram', numeric_columns, key='hist_col') | |
| plt.figure(figsize=(10, 5)) | |
| plt.hist(df[selected_column], bins=20, alpha=0.75) | |
| plt.title(f'Distribution of {selected_column}') | |
| st.pyplot(plt) | |
| else: | |
| st.write("No numeric columns available for plotting.") | |
| def plot_scatter(df): | |
| numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns.tolist() | |
| if numeric_columns and len(numeric_columns) > 1: | |
| col1 = st.selectbox('Select the first variable', numeric_columns, key='first_col') | |
| col2 = st.selectbox('Select the second variable', numeric_columns, key='second_col') | |
| plt.figure(figsize=(10, 5)) | |
| plt.scatter(df[col1], df[col2], alpha=0.5) | |
| plt.title(f'Scatter Plot of {col1} vs {col2}') | |
| plt.xlabel(col1) | |
| plt.ylabel(col2) | |
| st.pyplot(plt) | |
| else: | |
| st.write("Need at least two numeric columns to create a scatter plot.") | |
| def plot_line(df): | |
| numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns.tolist() | |
| if numeric_columns: | |
| selected_column = st.selectbox('Select column for line plot', numeric_columns, key='line_col') | |
| plt.figure(figsize=(10, 5)) | |
| plt.plot(df[selected_column], marker='o', linestyle='-') | |
| plt.title(f'Line Plot of {selected_column}') | |
| plt.xlabel('Index') | |
| plt.ylabel(selected_column) | |
| st.pyplot(plt) | |
| else: | |
| st.write("No numeric columns available for a line plot.") | |
| def plot_bar(df): | |
| categorical_columns = df.select_dtypes(include=['object']).columns.tolist() | |
| numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns.tolist() | |
| if categorical_columns and numeric_columns: | |
| category_col = st.selectbox('Select the category column', categorical_columns, key='cat_col') | |
| numeric_col = st.selectbox('Select the numeric column', numeric_columns, key='num_col') | |
| data_to_plot = df.groupby(category_col)[numeric_col].sum().sort_values(ascending=False) | |
| plt.figure(figsize=(10, 5)) | |
| data_to_plot.plot(kind='bar') | |
| plt.title(f'Bar Chart of {numeric_col} by {category_col}') | |
| plt.xlabel(category_col) | |
| plt.ylabel(f'Sum of {numeric_col}') | |
| st.pyplot(plt) | |
| else: | |
| st.write("No suitable columns available for plotting a bar chart.") | |
| def main(): | |
| st.set_page_config(page_title="Falcon 7B CSV Chatbot", layout="wide") | |
| st.title("Falcon 7B CSV Chatbot") | |
| st.sidebar.title("Navigation") | |
| app_mode = st.sidebar.selectbox("Choose the app mode", | |
| ["Chat with Llama-2", "Data Visualization"]) | |
| if app_mode == "Chat with Llama-2": | |
| run_llama_chatbot() | |
| elif app_mode == "Data Visualization": | |
| data_visualization() | |
| st.sidebar.markdown('''The Falcon 7B CSV Chatbot uses the **Falcon-7B-GGML** model.''') | |
| def run_llama_chatbot(): | |
| DB_FAISS_PATH = "vectorstore/db_faiss" | |
| TEMP_DIR = "temp" | |
| if not os.path.exists(TEMP_DIR): | |
| os.makedirs(TEMP_DIR) | |
| uploaded_file = st.sidebar.file_uploader("Upload CSV file", type=['csv'], help="Upload a CSV file") | |
| add_vertical_space(1) | |
| st.sidebar.markdown('Made by Sunirmala Mohanta') | |
| if uploaded_file is not None: | |
| file_path = os.path.join(TEMP_DIR, uploaded_file.name) | |
| with open(file_path, "wb") as f: | |
| f.write(uploaded_file.getvalue()) | |
| st.write(f"Uploaded file: {uploaded_file.name}") | |
| st.write("Processing CSV file...") | |
| loader = CSVLoader(file_path=file_path, encoding="utf-8", csv_args={'delimiter': ','}) | |
| data = loader.load() | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20) | |
| text_chunks = text_splitter.split_documents(data) | |
| st.write(f"Total text chunks: {len(text_chunks)}") | |
| embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2') | |
| docsearch = FAISS.from_documents(text_chunks, embeddings) | |
| docsearch.save_local(DB_FAISS_PATH) | |
| llm = CTransformers(model="models/falcon-7b-instruct.ggccv1.q4_0.bin", | |
| model_type="falcon", | |
| max_new_tokens=512, | |
| temperature=0.1) | |
| qa = ConversationalRetrievalChain.from_llm(llm, retriever=docsearch.as_retriever()) | |
| st.write("### Enter your query:") | |
| query = st.text_input("Input Prompt:") | |
| if query: | |
| with st.spinner("Processing your question..."): | |
| chat_history = [] | |
| result = qa({"question": query, "chat_history": chat_history}) | |
| st.write("---") | |
| st.write("### Response:") | |
| st.write(f"> {result['answer']}") | |
| os.remove(file_path) | |
| def data_visualization(): | |
| uploaded_file = st.sidebar.file_uploader("Upload CSV file", type=['csv']) | |
| if uploaded_file is not None: | |
| df = pd.read_csv(uploaded_file) | |
| st.write("Uploaded file preview:") | |
| st.write(df.head()) | |
| plot_type = st.sidebar.radio("Choose a type of plot:", | |
| ('Histogram', 'Scatter Plot', 'Line Plot', 'Bar Chart')) | |
| if plot_type == 'Histogram': | |
| plot_histogram(df) | |
| elif plot_type == 'Scatter Plot': | |
| plot_scatter(df) | |
| elif plot_type == 'Line Plot': | |
| plot_line(df) | |
| elif plot_type == 'Bar Chart': | |
| plot_bar(df) | |
| if __name__ == "__main__": | |
| main() |