Update app.py

app.py

@@ -15,8 +15,6 @@ import rasterio
 import matplotlib.pyplot as plt
 from tensorflow.keras.applications import ResNet50
 from tensorflow.keras.models import Model
-import cv2
-import joblib
 
 # Load crop data
 def load_data():
@@ -57,64 +55,7 @@ def predict_traditional(model_name, year, state, crop, yield_):
     else:
         return "Model not found"
 
-# Train RandomForestRegressor model for deep learning model
-def train_random_forest_model():
-    def process_tiff(file_path):
-        with rasterio.open(file_path) as src:
-            tiff_data = src.read()
-        B2_image = tiff_data[1, :, :]  # Assuming B2 is the second band
-        target_size = (50, 50)
-        B2_resized = cv2.resize(B2_image, target_size, interpolation=cv2.INTER_NEAREST)
-        return B2_resized.reshape(-1, 1)
-
-    data_dir = 'Data'
-    X_list = []
-    y_list = []
-
-    for root, dirs, files in os.walk(data_dir):
-        for file in files:
-            if file.endswith('.tiff'):
-                file_path = os.path.join(root, file)
-                X_list.append(process_tiff(file_path))
-                y_list.append(np.random.rand(2500))  # Replace with actual target data
-
-    X = np.vstack(X_list)
-    y = np.hstack(y_list)
-
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-    model = RandomForestRegressor(n_estimators=100, random_state=42)
-    model.fit(X_train, y_train)
-
-    return model
-
-rf_model = train_random_forest_model()
-
-def predict_random_forest(file):
-    if file is not None:
-        def process_tiff(file_path):
-            with rasterio.open(file_path) as src:
-                tiff_data = src.read()
-            B2_image = tiff_data[1, :, :]
-            target_size = (50, 50)
-            B2_resized = cv2.resize(B2_image, target_size, interpolation=cv2.INTER_NEAREST)
-            return B2_resized.reshape(-1, 1)
-
-        tiff_processed = process_tiff(file.name)
-        prediction = rf_model.predict(tiff_processed)
-        prediction_reshaped = prediction.reshape((50, 50))
-
-        plt.figure(figsize=(10, 10))
-        plt.imshow(prediction_reshaped, cmap='viridis')
-        plt.colorbar()
-        plt.title('Yield Prediction for Single TIFF File')
-        plt.savefig('/tmp/rf_prediction_overlay.png')
-
-        return '/tmp/rf_prediction_overlay.png'
-    else:
-        return "No file uploaded"
-
-# Load deep learning models
+# Load pre-trained deep learning models
 def load_deep_learning_model(model_name):
     base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(128, 128, 3))
     base_model.trainable = False
@@ -182,9 +123,9 @@ def predict_deep_learning(model_name, file):
             plt.colorbar()
 
             # Save the plot to a file
-            plt.savefig('/tmp/dl_prediction_overlay.png')
+            plt.savefig('/tmp/prediction_overlay.png')
 
-            return '/tmp/dl_prediction_overlay.png'
+            return '/tmp/prediction_overlay.png'
         else:
             return "No file uploaded"
     else:
@@ -200,7 +141,7 @@ inputs_traditional = [
 outputs_traditional = gr.Textbox(label='Predicted Profit')
 
 inputs_deep_learning = [
-    gr.Dropdown(choices=list(deep_learning_models.keys()) + ['Random Forest'], label='Model'),
+    gr.Dropdown(choices=list(deep_learning_models.keys()), label='Model'),
     gr.File(label='Upload TIFF File')
 ]
 outputs_deep_learning = gr.Image(label='Prediction Overlay')
@@ -216,10 +157,10 @@ with gr.Blocks() as demo:
         
     with gr.Tab("Deep Learning Models"):
         gr.Interface(
-            fn=lambda model_name, file: predict_deep_learning(model_name, file) if model_name != 'Random Forest' else predict_random_forest(file),
+            fn=predict_deep_learning,
             inputs=inputs_deep_learning,
             outputs=outputs_deep_learning,
-            title="Crop Yield Prediction using Deep Learning Models and Random Forest"
+            title="Crop Yield Prediction using Deep Learning Models"
         )
 
 demo.launch()