Update app.py

app.py

@@ -2,6 +2,7 @@ import cv2
 import numpy as np
 import gradio as gr
 import tensorflow as tf
+import pandas as pd
 import matplotlib.pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D
 
@@ -19,8 +20,6 @@ USD_TO_INR_CONVERSION_RATE = 83  # Convert USD to INR
 # Load the pre-trained LSTM model
 model = tf.keras.models.load_model('./solar_irradiance_model.keras')  # Or .h5
 
-
-
 # Preprocessing image
 def preprocess_image(image, clip_limit=2.0, tile_size=8):
     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
@@ -102,10 +101,20 @@ def visualize_roof_panels(roof_area, panel_length, panel_width, num_panels):
     return fig
 
 # Process uploaded image and calculate solar panel details
-def process_image_and_forecast(image, min_area, pixel_area, clip_limit, tile_size, panel_type, panel_length, panel_width, irradiance, electricity_rate, historical_energy_data):
+def process_image_and_forecast(image, min_area, pixel_area, clip_limit, tile_size, panel_type, panel_length, panel_width, irradiance, electricity_rate, historical_file):
     if image is None:
         return None, "Error: No image provided."
 
+    # Process historical file and extract energy data
+    if historical_file is None:
+        return None, "Error: No dataset provided."
+
+    try:
+        df = pd.read_csv(historical_file.name)
+        historical_data = df['energy_kWh'].tolist()  # Extract energy values (assuming the column name is 'energy_kWh')
+    except Exception as e:
+        return None, f"Error reading file: {str(e)}"
+
     # Preprocess the image
     preprocessed = preprocess_image(image, clip_limit, tile_size)
 
@@ -116,16 +125,13 @@ def process_image_and_forecast(image, min_area, pixel_area, clip_limit, tile_siz
     roof_area = calculate_roof_area(roof_contour, pixel_area)
 
     # Calculate the number of panels, energy output, and cost
-    num_panels, total_energy_kwh, total_cost_inr = calculate_panels_and_energy(
-        roof_area, panel_length, panel_width, panel_type, irradiance
-    )
+    num_panels, total_energy_kwh, total_cost_inr = calculate_panels_and_energy(roof_area, panel_length, panel_width, panel_type, irradiance)
 
     # Estimate yearly cost savings
     yearly_savings = estimate_savings(total_energy_kwh, electricity_rate)
 
     # Forecast future energy production using the LSTM model
     try:
-        historical_data = list(map(float, historical_energy_data.split(',')))  # Convert input to a list of floats
         predicted_energy = forecast_solar_energy(historical_data)
         forecast_text = f"Predicted Energy Output for Next Day: {predicted_energy:.2f} kWh"
     except Exception as e:
@@ -147,7 +153,7 @@ def process_image_and_forecast(image, min_area, pixel_area, clip_limit, tile_siz
 interface = gr.Interface(
     fn=process_image_and_forecast,
     inputs=[
-        gr.Image(type="numpy", label="Upload Image"),
+        gr.Image(type="numpy", label="Upload Roof Image"),
         gr.Slider(500, 5000, value=1000, step=100, label="Minimum Contour Area"),
         gr.Number(value=0.1, label="Pixel to Area Conversion (sq. meters)"),
         gr.Slider(1.0, 5.0, value=2.0, step=0.1, label="CLAHE Clip Limit"),
@@ -157,7 +163,7 @@ interface = gr.Interface(
         gr.Number(value=1.0, label="Panel Width (m)"),
         gr.Number(value=1000, label="Average Solar Irradiance (W/m²)"),
         gr.Number(value=0.15, label="Electricity Rate (₹/kWh)"),
-        gr.Textbox(label="Enter Historical Energy Production (last 7 days in kWh, separated by commas)", lines=2)
+        gr.File(label="Upload CSV Dataset (Historical Energy Data)")
     ],
     outputs=[
         gr.Image(type="numpy", label="Processed Image with Contours"),
@@ -165,7 +171,7 @@ interface = gr.Interface(
         gr.Plot(label="3D Visualization of Panels")
     ],
     title="Advanced Solar Panel Placement Estimator with Energy Forecasting",
-    description="Upload an image and adjust the parameters to estimate the roof area, panel placement, energy output, cost, and savings. Forecast future energy output using historical data."
+    description="Upload an image and a CSV file to estimate the roof area, panel placement, energy output, cost, savings, and forecast future energy production."
 )
 
 if __name__ == '__main__':