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Update app.py

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import streamlit as st
import pandas as pd
import requests
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import io  # Import the io module
import os
import numpy as np
import yaml
from datetime import datetime
import logging
import csv  # Import the csv module
from dotenv import load_dotenv  # Import load_dotenv
from plotly.colors import n_colors # Import n_colors
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List, Optional
from nixtla import NixtlaClient  # Import NixtlaClient

load_dotenv() # Load environment variables

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# FastAPI endpoint URL
# Use environment variable if available, otherwise default to localhost and port from FASTAPI_PORT
FASTAPI_URL = "https://huggingface.co/spaces/anujkum0x/backender/forecast"
st.set_page_config(
    page_title="🔮 Time Series Forecasting", layout="wide", initial_sidebar_state="expanded"
)

# --- Custom CSS for enhanced visual appeal ---
st.markdown(
    """
    <style>
    /* General app background */
    .reportview-container {
        background: linear-gradient(to right, #f0f2f6, #e1e8f2) !important; /* Light background */
    }
    /* Sidebar background */
    .sidebar .sidebar-content {
        background: linear-gradient(to bottom, #f0f2f6, #e1e8f2) !important; /* Light sidebar */
    }
    /* Headers and text */
    h1, h2, h3, h4, h5, h6, p, div, label {
        color: #333333 !important; /* Darker text for contrast */
    }
    /* Buttons */
    .stButton>button {
        color: #007bff !important; /* Primary blue color */
        border: 2px solid #007bff !important;
        background-color: transparent !important;
        transition: all 0.3s ease !important;
    }
    .stButton>button:hover {
        background-color: #007bff !important;
        color: white !important;
    }
    /* Input fields */
    .stTextInput>label, .stNumberInput>label, .stSelectbox>label, .stDateInput>label {
        color: #555555 !important;
    }
    /* Add a subtle shadow to elements */
    .element-container {
        box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1) !important;
        border-radius: 5px !important;
        padding: 10px !important;
        margin-bottom: 10px !important;
        background-color: rgba(255, 255, 255, 0.8) !important; /* Semi-transparent white for content boxes */
    }
    </style>
    """,
    unsafe_allow_html=True,
)

st.title("🔮 Time Series Forecasting")

# --- Sidebar for Settings ---
with st.sidebar:
    st.header("⚙️ Settings")
    # Hardcoded API key (not recommended for production)
    #api_key = 'nixak-1jDopAXEfaOielBz1ncfbHUdsxQuULpM1rrZL0dMmYILolFC1SIp6KrCQsfuArOBIazhXvamCQuPPBw6'
    
    horizon = st.number_input("Forecast Horizon", min_value=1, max_value=1000, value=30)
    finetune_steps = st.slider("Finetune Steps", min_value=0, max_value=2000, value=1000)
    freq = st.selectbox(
        "Model Frequency",
        options=['15min', '30min', 'H', '2H', '3H', '4H', '5H', '6H', '12H', 'D', 'W', 'M', 'Y'],
        index=2,
        help="Frequency of the time series data for the model."
    )

    resample_freq = st.selectbox(
        "Resample Frequency",
        options=['15min', '30min', 'H', '2H', '3H', '4H', '5H', '6H', '12H', 'D', 'W', 'M', 'Y'],
        index=2,
        help="Frequency to resample the input data to."
    )

    st.sidebar.header("📁 Data Input")
    uploaded_file = st.sidebar.file_uploader(
        "Upload your time series data (CSV, Excel, JSON, YAML)", type=["csv", "xlsx", "json", "yaml", "yml"], help="Upload a CSV, Excel, JSON, or YAML file containing your time series data."
    )

# --- Main App Logic ---
st.write("About to display the generate forecast button")  # Debugging statement
data_loaded = False
df = None

if uploaded_file is not None:
    try:
        logger.info(f"Attempting to load file: {uploaded_file.name}")
        file_extension = uploaded_file.name.split('.')[-1].lower()

        if file_extension == 'csv':
            try:
                df = pd.read_csv(uploaded_file)
                logger.info(f"CSV file loaded successfully using Pandas. Shape: {df.shape}")
            except Exception as e:
                st.error(f"❌ Error parsing CSV file with Pandas: {e}")
                logger.exception(f"Error parsing CSV with Pandas: {e}")
                st.stop()

        elif file_extension == 'xlsx':
            try:
                df = pd.read_excel(uploaded_file)
                logger.info(f"Excel file loaded successfully using Pandas. Shape: {df.shape}")
            except Exception as e:
                st.error(f"❌ Error parsing Excel file with Pandas: {e}")
                logger.exception(f"Error parsing Excel with Pandas: {e}")
                st.stop()

        elif file_extension == 'json':
            try:
                df = pd.read_json(uploaded_file)
                logger.info(f"JSON file loaded successfully using Pandas. Shape: {df.shape}")
            except Exception as e:
                st.error(f"❌ Error parsing JSON file with Pandas: {e}")
                logger.exception(f"Error parsing JSON with Pandas: {e}")
                st.stop()

        elif file_extension in ['yaml', 'yml']:
            try:
                df = pd.DataFrame(yaml.safe_load(uploaded_file))
                logger.info(f"YAML file loaded successfully using Pandas. Shape: {df.shape}")
            except Exception as e:
                st.error(f"❌ Error parsing YAML file with Pandas: {e}")
                logger.exception(f"Error parsing YAML with Pandas: {e}")
                st.stop()

        else:
            st.error("❌ Unsupported file format. Please upload a CSV, Excel, JSON, or YAML file.")
            logger.error(f"Unsupported file format: {file_extension}")
            st.stop()

        st.success("✅ Data loaded successfully!")
        data_loaded = True

        # --- Column Selection ---
        st.sidebar.header("📊 Column Selection")
        time_col = st.sidebar.selectbox("Select Timestamp Column", df.columns, help="Column containing the timestamps.")
        value_col = st.sidebar.selectbox("Select Value Column", df.columns, help="Column containing the values to forecast.")

        if value_col == time_col:
            st.error("❌ Value column cannot be the same as the Timestamp column")
            logger.error("Value column and Timestamp column are the same.")
            st.stop()

        # --- Convert Value Column to Numeric ---
        try:
            # Convert to numeric, coercing errors
            df[value_col] = pd.to_numeric(df[value_col], errors='coerce')
            logger.info(f"Value column '{value_col}' converted to numeric.")

            # Handle potential NaN values (failed conversions)
            if df[value_col].isnull().any():
                st.warning(f"Some values in {value_col} could not be converted to numeric and were replaced with NaN.")
                logger.warning(f"NaN values found in value column '{value_col}'.")
                df = df.dropna(subset=[value_col])
                logger.info(f"Rows with NaN values in '{value_col}' dropped. Shape: {df.shape}")

        except Exception as e:
            st.error(f"Error converting {value_col} to numeric: {e}")
            logger.exception(f"Error converting value column to numeric: {e}")
            st.stop()

        # --- Convert Timestamp Column to Datetime ---
        try:
            df[time_col] = pd.to_datetime(df[time_col], errors='coerce')
            logger.info(f"Timestamp column '{time_col}' converted to datetime.")

            # Handle potential NaT values (failed conversions)
            if df[time_col].isnull().any():
                st.warning(f"Some values in {time_col} could not be converted to datetime. These rows will be dropped.")
                logger.warning(f"NaT values found in timestamp column '{time_col}'.")
                df = df.dropna(subset=[time_col])
                logger.info(f"Rows with NaT values in '{time_col}' dropped. Shape: {df.shape}")

        except Exception as e:
            st.error(f"Error converting {time_col} to datetime: {e}")
            logger.exception(f"Error converting timestamp column to datetime: {e}")
            st.stop()

        # --- Data Preview ---
        with st.expander("🔍 Data Preview", expanded=False):
            st.dataframe(df.head())

    except Exception as e:
        st.error(f"❌ An error occurred during data loading: {e}")
        logger.exception(f"An error occurred during data loading: {e}")
        st.stop()

if data_loaded:  # The button should ALWAYS appear if data_loaded is True
    if st.button("✨ Generate Forecast"):
        if df is not None:
            with st.spinner("⏳ Generating forecast..."):
                try:
                    # Ensure no Nulls in the data being sent to the API
                    df = df.dropna(subset=[time_col, value_col])
                    logger.info(f"Null values dropped before API call. Shape: {df.shape}")

                    # Convert timestamps to string and values to list
                    timestamps = [ts.isoformat() for ts in df[time_col]]
                    values = df[value_col].tolist()

                    payload = {
                        "timestamps": timestamps,
                        "values": values,
                        "forecast_horizon": horizon,
                        "finetune_steps": finetune_steps,
                        "freq": freq,
                        "resample_freq": resample_freq,
                        "target_col": value_col,
                        "format": "json"  # Default format
                    }

                    response = requests.post(FASTAPI_URL, json=payload)
                    response.raise_for_status()  # Raise HTTPError for bad responses
                    logger.info(f"API call successful. Status code: {response.status_code}")
                    forecast_data = response.json()

                    # Convert forecast data to DataFrame
                    forecast_df = pd.DataFrame(forecast_data)

                    # Determine the forecast value column name
                    forecast_value_col = [col for col in forecast_df.columns if col != time_col][0]

                    # Convert back to datetime for plotting
                    forecast_df[time_col] = pd.to_datetime(forecast_df[time_col])

                    # --- Plotting ---
                    st.subheader("📈 Time Series Visualization")
                    fig = make_subplots(
                        rows=2, cols=1,
                        shared_xaxes=True,
                        vertical_spacing=0.05,
                        subplot_titles=('Historical Data vs Forecast', 'Combined Data (Inner Join)')
                    )

                    # Historical Data
                    fig.add_trace(go.Scatter(
                        x=df[time_col],
                        y=df[value_col],
                        mode='lines',
                        name='Historical Data',
                        line=dict(color='#636EFA'),
                        showlegend=False
                    ), row=1, col=1)

                    # Forecast Data
                    fig.add_trace(go.Scatter(
                        x=forecast_df[time_col],
                        y=forecast_df[forecast_value_col],
                        mode='lines',
                        name='Forecast',
                        line=dict(color='#FFA15A'),
                        showlegend=False
                    ), row=1, col=1)

                    # Combined Data
                    fig.add_trace(go.Scatter(
                        x=df[time_col],
                        y=df[value_col],
                        mode='lines',
                        name='Historical Data',
                        line=dict(color='#636EFA'),
                        showlegend=False
                    ), row=2, col=1)
                    fig.add_trace(go.Scatter(
                        x=forecast_df[time_col],
                        y=forecast_df[forecast_value_col],
                        mode='lines',
                        name='Forecast',
                        line=dict(color='#FFA15A'),
                        showlegend=False
                    ), row=2, col=1)

                    fig.update_layout(
                        title="Time Series Forecast",
                        xaxis_title="Time",
                        yaxis_title="Value",
                        template="plotly_white",  # Changed to white template
                        hovermode="x unified"
                    )

                    st.plotly_chart(fig, use_container_width=True)

                    # --- Forecast Data Display ---
                    st.subheader("Forecast Data")
                    st.dataframe(forecast_df)

                    # --- Download Forecast Data ---
                    csv = forecast_df.to_csv(index=False)
                    st.download_button(
                        label="Download forecast data as CSV",
                        data=csv,
                        file_name="forecast.csv",
                        mime="text/csv",
                    )

                except requests.exceptions.RequestException as e:
                    st.error(f"❌ Error communicating with backend: {e}")
                    logger.exception(f"Error communicating with backend: {e}")
                except Exception as e:
                    st.error(f"❌ An error occurred during forecasting: {e}")
                    logger.exception(f"Error occurred during forecasting: {e}")
        else:
            st.warning("Please upload data and select columns to generate a forecast.")

	import streamlit as st
	import pandas as pd
	import requests
	import plotly.graph_objects as go
	from plotly.subplots import make_subplots
	import io # Import the io module
	import os
	import numpy as np
	import yaml
	from datetime import datetime
	import logging
	import csv # Import the csv module
	from dotenv import load_dotenv # Import load_dotenv
	from plotly.colors import n_colors # Import n_colors
	from fastapi import FastAPI, HTTPException
	from pydantic import BaseModel
	from typing import List, Optional
	from nixtla import NixtlaClient # Import NixtlaClient

	load_dotenv() # Load environment variables

	# Configure logging
	logging.basicConfig(level=logging.INFO)
	logger = logging.getLogger(__name__)

	# FastAPI endpoint URL
	# Use environment variable if available, otherwise default to localhost and port from FASTAPI_PORT
	FASTAPI_URL = "https://huggingface.co/spaces/anujkum0x/backender/forecast"
	st.set_page_config(
	page_title="🔮 Time Series Forecasting", layout="wide", initial_sidebar_state="expanded"
	)

	# --- Custom CSS for enhanced visual appeal ---
	st.markdown(
	"""
	<style>
	/* General app background */
	.reportview-container {
	background: linear-gradient(to right, #f0f2f6, #e1e8f2) !important; /* Light background */
	}
	/* Sidebar background */
	.sidebar .sidebar-content {
	background: linear-gradient(to bottom, #f0f2f6, #e1e8f2) !important; /* Light sidebar */
	}
	/* Headers and text */
	h1, h2, h3, h4, h5, h6, p, div, label {
	color: #333333 !important; /* Darker text for contrast */
	}
	/* Buttons */
	.stButton>button {
	color: #007bff !important; /* Primary blue color */
	border: 2px solid #007bff !important;
	background-color: transparent !important;
	transition: all 0.3s ease !important;
	}
	.stButton>button:hover {
	background-color: #007bff !important;
	color: white !important;
	}
	/* Input fields */
	.stTextInput>label, .stNumberInput>label, .stSelectbox>label, .stDateInput>label {
	color: #555555 !important;
	}
	/* Add a subtle shadow to elements */
	.element-container {
	box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1) !important;
	border-radius: 5px !important;
	padding: 10px !important;
	margin-bottom: 10px !important;
	background-color: rgba(255, 255, 255, 0.8) !important; /* Semi-transparent white for content boxes */
	}
	</style>
	""",
	unsafe_allow_html=True,
	)

	st.title("🔮 Time Series Forecasting")

	# --- Sidebar for Settings ---
	with st.sidebar:
	st.header("⚙️ Settings")
	# Hardcoded API key (not recommended for production)
	#api_key = 'nixak-1jDopAXEfaOielBz1ncfbHUdsxQuULpM1rrZL0dMmYILolFC1SIp6KrCQsfuArOBIazhXvamCQuPPBw6'

	horizon = st.number_input("Forecast Horizon", min_value=1, max_value=1000, value=30)
	finetune_steps = st.slider("Finetune Steps", min_value=0, max_value=2000, value=1000)
	freq = st.selectbox(
	"Model Frequency",
	options=['15min', '30min', 'H', '2H', '3H', '4H', '5H', '6H', '12H', 'D', 'W', 'M', 'Y'],
	index=2,
	help="Frequency of the time series data for the model."
	)

	resample_freq = st.selectbox(
	"Resample Frequency",
	options=['15min', '30min', 'H', '2H', '3H', '4H', '5H', '6H', '12H', 'D', 'W', 'M', 'Y'],
	index=2,
	help="Frequency to resample the input data to."
	)

	st.sidebar.header("📁 Data Input")
	uploaded_file = st.sidebar.file_uploader(
	"Upload your time series data (CSV, Excel, JSON, YAML)", type=["csv", "xlsx", "json", "yaml", "yml"], help="Upload a CSV, Excel, JSON, or YAML file containing your time series data."
	)

	# --- Main App Logic ---
	st.write("About to display the generate forecast button") # Debugging statement
	data_loaded = False
	df = None

	if uploaded_file is not None:
	try:
	logger.info(f"Attempting to load file: {uploaded_file.name}")
	file_extension = uploaded_file.name.split('.')[-1].lower()

	if file_extension == 'csv':
	try:
	df = pd.read_csv(uploaded_file)
	logger.info(f"CSV file loaded successfully using Pandas. Shape: {df.shape}")
	except Exception as e:
	st.error(f"❌ Error parsing CSV file with Pandas: {e}")
	logger.exception(f"Error parsing CSV with Pandas: {e}")
	st.stop()

	elif file_extension == 'xlsx':
	try:
	df = pd.read_excel(uploaded_file)
	logger.info(f"Excel file loaded successfully using Pandas. Shape: {df.shape}")
	except Exception as e:
	st.error(f"❌ Error parsing Excel file with Pandas: {e}")
	logger.exception(f"Error parsing Excel with Pandas: {e}")
	st.stop()

	elif file_extension == 'json':
	try:
	df = pd.read_json(uploaded_file)
	logger.info(f"JSON file loaded successfully using Pandas. Shape: {df.shape}")
	except Exception as e:
	st.error(f"❌ Error parsing JSON file with Pandas: {e}")
	logger.exception(f"Error parsing JSON with Pandas: {e}")
	st.stop()

	elif file_extension in ['yaml', 'yml']:
	try:
	df = pd.DataFrame(yaml.safe_load(uploaded_file))
	logger.info(f"YAML file loaded successfully using Pandas. Shape: {df.shape}")
	except Exception as e:
	st.error(f"❌ Error parsing YAML file with Pandas: {e}")
	logger.exception(f"Error parsing YAML with Pandas: {e}")
	st.stop()

	else:
	st.error("❌ Unsupported file format. Please upload a CSV, Excel, JSON, or YAML file.")
	logger.error(f"Unsupported file format: {file_extension}")
	st.stop()

	st.success("✅ Data loaded successfully!")
	data_loaded = True

	# --- Column Selection ---
	st.sidebar.header("📊 Column Selection")
	time_col = st.sidebar.selectbox("Select Timestamp Column", df.columns, help="Column containing the timestamps.")
	value_col = st.sidebar.selectbox("Select Value Column", df.columns, help="Column containing the values to forecast.")

	if value_col == time_col:
	st.error("❌ Value column cannot be the same as the Timestamp column")
	logger.error("Value column and Timestamp column are the same.")
	st.stop()

	# --- Convert Value Column to Numeric ---
	try:
	# Convert to numeric, coercing errors
	df[value_col] = pd.to_numeric(df[value_col], errors='coerce')
	logger.info(f"Value column '{value_col}' converted to numeric.")

	# Handle potential NaN values (failed conversions)
	if df[value_col].isnull().any():
	st.warning(f"Some values in {value_col} could not be converted to numeric and were replaced with NaN.")
	logger.warning(f"NaN values found in value column '{value_col}'.")
	df = df.dropna(subset=[value_col])
	logger.info(f"Rows with NaN values in '{value_col}' dropped. Shape: {df.shape}")

	except Exception as e:
	st.error(f"Error converting {value_col} to numeric: {e}")
	logger.exception(f"Error converting value column to numeric: {e}")
	st.stop()

	# --- Convert Timestamp Column to Datetime ---
	try:
	df[time_col] = pd.to_datetime(df[time_col], errors='coerce')
	logger.info(f"Timestamp column '{time_col}' converted to datetime.")

	# Handle potential NaT values (failed conversions)
	if df[time_col].isnull().any():
	st.warning(f"Some values in {time_col} could not be converted to datetime. These rows will be dropped.")
	logger.warning(f"NaT values found in timestamp column '{time_col}'.")
	df = df.dropna(subset=[time_col])
	logger.info(f"Rows with NaT values in '{time_col}' dropped. Shape: {df.shape}")

	except Exception as e:
	st.error(f"Error converting {time_col} to datetime: {e}")
	logger.exception(f"Error converting timestamp column to datetime: {e}")
	st.stop()

	# --- Data Preview ---
	with st.expander("🔍 Data Preview", expanded=False):
	st.dataframe(df.head())

	except Exception as e:
	st.error(f"❌ An error occurred during data loading: {e}")
	logger.exception(f"An error occurred during data loading: {e}")
	st.stop()

	if data_loaded: # The button should ALWAYS appear if data_loaded is True
	if st.button("✨ Generate Forecast"):
	if df is not None:
	with st.spinner("⏳ Generating forecast..."):
	try:
	# Ensure no Nulls in the data being sent to the API
	df = df.dropna(subset=[time_col, value_col])
	logger.info(f"Null values dropped before API call. Shape: {df.shape}")

	# Convert timestamps to string and values to list
	timestamps = [ts.isoformat() for ts in df[time_col]]
	values = df[value_col].tolist()

	payload = {
	"timestamps": timestamps,
	"values": values,
	"forecast_horizon": horizon,
	"finetune_steps": finetune_steps,
	"freq": freq,
	"resample_freq": resample_freq,
	"target_col": value_col,
	"format": "json" # Default format
	}

	response = requests.post(FASTAPI_URL, json=payload)
	response.raise_for_status() # Raise HTTPError for bad responses
	logger.info(f"API call successful. Status code: {response.status_code}")
	forecast_data = response.json()

	# Convert forecast data to DataFrame
	forecast_df = pd.DataFrame(forecast_data)

	# Determine the forecast value column name
	forecast_value_col = [col for col in forecast_df.columns if col != time_col][0]

	# Convert back to datetime for plotting
	forecast_df[time_col] = pd.to_datetime(forecast_df[time_col])

	# --- Plotting ---
	st.subheader("📈 Time Series Visualization")
	fig = make_subplots(
	rows=2, cols=1,
	shared_xaxes=True,
	vertical_spacing=0.05,
	subplot_titles=('Historical Data vs Forecast', 'Combined Data (Inner Join)')
	)

	# Historical Data
	fig.add_trace(go.Scatter(
	x=df[time_col],
	y=df[value_col],
	mode='lines',
	name='Historical Data',
	line=dict(color='#636EFA'),
	showlegend=False
	), row=1, col=1)

	# Forecast Data
	fig.add_trace(go.Scatter(
	x=forecast_df[time_col],
	y=forecast_df[forecast_value_col],
	mode='lines',
	name='Forecast',
	line=dict(color='#FFA15A'),
	showlegend=False
	), row=1, col=1)

	# Combined Data
	fig.add_trace(go.Scatter(
	x=df[time_col],
	y=df[value_col],
	mode='lines',
	name='Historical Data',
	line=dict(color='#636EFA'),
	showlegend=False
	), row=2, col=1)
	fig.add_trace(go.Scatter(
	x=forecast_df[time_col],
	y=forecast_df[forecast_value_col],
	mode='lines',
	name='Forecast',
	line=dict(color='#FFA15A'),
	showlegend=False
	), row=2, col=1)

	fig.update_layout(
	title="Time Series Forecast",
	xaxis_title="Time",
	yaxis_title="Value",
	template="plotly_white", # Changed to white template
	hovermode="x unified"
	)

	st.plotly_chart(fig, use_container_width=True)

	# --- Forecast Data Display ---
	st.subheader("Forecast Data")
	st.dataframe(forecast_df)

	# --- Download Forecast Data ---
	csv = forecast_df.to_csv(index=False)
	st.download_button(
	label="Download forecast data as CSV",
	data=csv,
	file_name="forecast.csv",
	mime="text/csv",
	)

	except requests.exceptions.RequestException as e:
	st.error(f"❌ Error communicating with backend: {e}")
	logger.exception(f"Error communicating with backend: {e}")
	except Exception as e:
	st.error(f"❌ An error occurred during forecasting: {e}")
	logger.exception(f"Error occurred during forecasting: {e}")
	else:
	st.warning("Please upload data and select columns to generate a forecast.")