Update app.py

app.py

@@ -1,59 +1,39 @@
 import gradio as gr
 import pandas as pd
-import matplotlib.pyplot as plt
 from prophet import Prophet
-import fitz  # PyMuPDF for PDF reading
-import io
-
-def extract_csv_from_pdf(pdf_file):
-    # Load and extract text from the PDF
-    with fitz.open(stream=pdf_file.read(), filetype="pdf") as doc:
-        text = ""
-        for page in doc:
-            text += page.get_text()
-
-    # Try to parse CSV-like data
-    from io import StringIO
-    lines = [line for line in text.split("\n") if "," in line]
-    data = "\n".join(lines)
-    df = pd.read_csv(StringIO(data))
-    return df
+import matplotlib.pyplot as plt
 
-def forecast_energy(pdf_file):
-    df = extract_csv_from_pdf(pdf_file)
+def forecast_energy(file):
+    # Read CSV
+    df = pd.read_csv(file)
     
-    if 'date' not in df.columns or 'energy_load' not in df.columns:
-        return "Error: PDF must contain columns 'date' and 'energy_load'."
-
-    df = df[['date', 'energy_load']]
+    # Rename columns to fit Prophet's expected format
     df.columns = ['ds', 'y']
+    
+    # Convert date column to datetime
     df['ds'] = pd.to_datetime(df['ds'])
-    df['y'] = pd.to_numeric(df['y'], errors='coerce')
-    df = df.dropna()
-
+    
+    # Build and train model
     model = Prophet()
     model.fit(df)
-
+    
+    # Forecast next 14 days
     future = model.make_future_dataframe(periods=14)
     forecast = model.predict(future)
-
-    # Extract next 14 days only
-    forecast_14 = forecast.tail(14)
-    peak_day = forecast_14.loc[forecast_14['yhat'].idxmax()]
-    peak_msg = f"🔺 Peak Forecasted Demand: {peak_day['yhat']:.2f} on {peak_day['ds'].date()}"
-
-    # Plot
+    
+    # Plot forecast
     fig = model.plot(forecast)
     plt.title("Energy Load Forecast")
+    
+    return fig
 
-    return peak_msg, fig
-
+# Gradio interface
 demo = gr.Interface(
     fn=forecast_energy,
-    inputs=gr.File(label="Upload Energy Load PDF"),
-    outputs=["text", "plot"],
+    inputs=gr.File(label="Upload Energy Load CSV", file_types=[".csv"]),
+    outputs=gr.Plot(label="Forecasted Load"),
     title="Smart Energy Load Forecasting",
-    description="Upload a PDF file with energy data. Columns required: 'date', 'energy_load'. Forecasts next 2 weeks and shows peak demand."
+    description="Upload a CSV file with columns 'ds' (date) and 'y' (energy load). This app predicts the next 2 weeks of load."
 )
 
 if __name__ == "__main__":