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Upload 3 files
Browse files- app.py +67 -0
- forecastData.csv +366 -0
- requirements.txt +4 -0
app.py
ADDED
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import streamlit as st
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import pandas as pd
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import openai
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import os
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from dotenv import load_dotenv
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from datetime import datetime, timedelta
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load_dotenv()
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# Set OpenAI API key
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openai.api_key = os.getenv("OPENAI_API_KEY")
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# Load CSV file
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df = pd.read_csv(r"C:\Users\sadac\OneDrive\Desktop\salesforecast-chatbot\forecastData.csv")
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df['Date'] = pd.to_datetime(df['Date']) # Ensure Date is in datetime format
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# Streamlit UI
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st.set_page_config(page_title="Sales Forecast Chatbot")
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st.title("Sales Forecast Chatbot")
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st.markdown("Ask for sales forecast by day, week, or month.")
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# User input for period
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period = st.selectbox("Select Forecast Period", ["Day", "Week", "Month"])
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start_date = None
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end_date = None
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# Handle date selection and period logic
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if period == "Day":
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selected_date = st.date_input("Select Date")
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start_date = datetime.combine(selected_date, datetime.min.time())
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end_date = start_date + timedelta(days=1) # Include full day
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elif period == "Week":
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selected_date = st.date_input("Select Start Date of Week")
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start_date = datetime.combine(selected_date, datetime.min.time())
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end_date = start_date + timedelta(days=7) # 7-day period
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elif period == "Month":
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selected_date = st.date_input("Select Start Date for Monthly Forecast")
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start_date = datetime.combine(selected_date, datetime.min.time())
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end_date = start_date + timedelta(days=30)
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# When user clicks the button
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if st.button("Get Forecast"):
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filtered = df[(df['Date'] >= start_date) & (df['Date'] < end_date)]
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if not filtered.empty:
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total_sales = filtered['Forecasted_Sales'].sum()
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formatted_total = f"{total_sales:,.2f}"
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st.success(f"🧾 Total Forecasted Sales from {start_date.date()} to {end_date.date()}: {formatted_total}")
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# Generate GPT suggestion
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prompt = f"The total forecasted sales from {start_date.date()} to {end_date.date()} is {formatted_total}. Provide a brief suggestion for improving sales or maintaining performance."
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with st.spinner("Generating GPT suggestion..."):
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response = openai.ChatCompletion.create(
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model="gpt-3.5-turbo", # or gpt-4 if you have access
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messages=[
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{"role": "user", "content": prompt}
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]
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)
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gpt_reply = response['choices'][0]['message']['content']
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st.markdown("**GPT Suggestion:**")
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st.write(gpt_reply)
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else:
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st.warning(" No forecast data found for selected period.")
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forecastData.csv
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| 1 |
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Date,Forecasted_Sales
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2025-03-01,4685661.387
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| 3 |
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2025-03-02,2586351.492
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2025-03-03,4675843.927
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2025-03-04,5052969.364
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2025-03-05,4977911.143
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2025-03-06,4875497.257
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2025-03-07,3522924.054
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2025-03-08,4496472.553
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2025-03-09,2474748.294
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2025-03-10,4471271.522
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2025-03-11,5023028.628
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2025-03-12,5042117.902
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2025-03-13,4825123.487
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2025-03-14,3564184.507
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2025-03-15,4463466.871
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2025-03-16,2466828.851
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2025-03-17,4530176.331
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2025-03-18,4981621.956
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2025-03-19,5030621.096
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2025-03-20,4886472.334
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2025-03-21,3590695.43
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2025-03-22,4507196.067
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2025-03-23,2484495.597
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2025-03-24,4520779.214
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2025-03-25,5012889.54
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2025-03-26,5027726.102
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2025-03-27,4854277.921
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2025-03-28,3567430.115
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2025-03-29,4484790.629
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2025-03-30,2475577.98
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2025-03-31,4519072.906
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2025-04-01,5000178.57
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2025-04-03,4867033.75
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2025-04-08,5003991.995
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2025-12-06,4491562.014
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| 283 |
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2025-12-07,2478428.131
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| 287 |
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2025-12-11,4864191.423
|
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2025-12-12,3576249.878
|
| 289 |
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2025-12-13,4491562.014
|
| 290 |
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| 291 |
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|
| 292 |
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|
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|
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|
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|
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|
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|
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|
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2026-01-05,4520962.97
|
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2026-01-06,5003376.272
|
| 314 |
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2026-01-07,5030945.296
|
| 315 |
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| 316 |
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| 317 |
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|
| 318 |
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| 319 |
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2026-01-12,4520962.97
|
| 320 |
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2026-01-13,5003376.272
|
| 321 |
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|
| 322 |
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2026-01-15,4864191.423
|
| 323 |
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2026-01-16,3576249.878
|
| 324 |
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2026-01-17,4491562.014
|
| 325 |
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2026-01-18,2478428.131
|
| 326 |
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2026-01-19,4520962.97
|
| 327 |
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2026-01-20,5003376.272
|
| 328 |
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2026-01-21,5030945.296
|
| 329 |
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2026-01-22,4864191.423
|
| 330 |
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2026-01-23,3576249.878
|
| 331 |
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2026-01-24,4491562.014
|
| 332 |
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2026-01-25,2478428.131
|
| 333 |
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2026-01-26,4520962.97
|
| 334 |
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2026-01-27,5003376.272
|
| 335 |
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2026-01-28,5030945.296
|
| 336 |
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2026-01-29,4864191.423
|
| 337 |
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2026-01-30,3576249.878
|
| 338 |
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2026-01-31,4491562.014
|
| 339 |
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2026-02-01,2478428.131
|
| 340 |
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2026-02-02,4520962.97
|
| 341 |
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2026-02-03,5003376.272
|
| 342 |
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2026-02-04,5030945.296
|
| 343 |
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2026-02-05,4864191.423
|
| 344 |
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2026-02-06,3576249.878
|
| 345 |
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2026-02-07,4491562.014
|
| 346 |
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2026-02-08,2478428.131
|
| 347 |
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2026-02-09,4520962.97
|
| 348 |
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2026-02-10,5003376.272
|
| 349 |
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2026-02-11,5030945.296
|
| 350 |
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2026-02-12,4864191.423
|
| 351 |
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2026-02-13,3576249.878
|
| 352 |
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2026-02-14,4491562.014
|
| 353 |
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2026-02-15,2478428.131
|
| 354 |
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2026-02-16,4520962.97
|
| 355 |
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2026-02-17,5003376.272
|
| 356 |
+
2026-02-18,5030945.296
|
| 357 |
+
2026-02-19,4864191.423
|
| 358 |
+
2026-02-20,3576249.878
|
| 359 |
+
2026-02-21,4491562.014
|
| 360 |
+
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|
| 361 |
+
2026-02-23,4520962.97
|
| 362 |
+
2026-02-24,5003376.272
|
| 363 |
+
2026-02-25,5030945.296
|
| 364 |
+
2026-02-26,4864191.423
|
| 365 |
+
2026-02-27,3576249.878
|
| 366 |
+
2026-02-28,4491562.014
|
requirements.txt
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
streamlit
|
| 2 |
+
pandas
|
| 3 |
+
openai==0.28
|
| 4 |
+
python-dotenv
|