this is pushing mew visual model

app.py

@@ -5,7 +5,18 @@ from sklearn.svm import SVR
 from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
 from sklearn.preprocessing import LabelEncoder
 import gradio as gr
+import os
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Input
+from tensorflow.keras.optimizers import Adam
+from PIL import Image
+import rasterio
+from tensorflow.keras.applications import ResNet50
+from tensorflow.keras.models import Model
 
+# Load crop data
 def load_data():
     url = 'https://raw.githubusercontent.com/NarutoOp/Crop-Recommendation/master/cropdata.csv'
     data = pd.read_csv(url)
@@ -34,7 +45,7 @@ models = {
 for name, model in models.items():
     model.fit(X_train, y_train)
 
-def predict(model_name, year, state, crop, yield_):
+def predict_traditional(model_name, year, state, crop, yield_):
     if model_name in models:
         model = models[model_name]
         state_encoded = label_encoders['STATE'].transform([state])[0]
@@ -44,21 +55,92 @@ def predict(model_name, year, state, crop, yield_):
     else:
         return "Model not found"
 
-inputs = [
+# 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
+
+    inputs = Input(shape=(128, 128, 3))
+    x = base_model(inputs, training=False)
+    x = GlobalAveragePooling2D()(x)
+    outputs = Dense(1, activation='linear')(x)
+
+    model = Model(inputs, outputs)
+    model.compile(optimizer=Adam(), loss='mean_squared_error', metrics=['mae'])
+
+    return model
+
+deep_learning_models = {
+    'ResNet50': load_deep_learning_model('ResNet50'),
+    # Add other models here if needed
+}
+
+def predict_deep_learning(model_name, file):
+    if model_name in deep_learning_models:
+        if file is not None:
+            with rasterio.open(file.name) as src:
+                img_data = src.read(1)
+
+            patch_size = 128
+            n_patches_x = img_data.shape[1] // patch_size
+            n_patches_y = img_data.shape[0] // patch_size
+
+            patches = []
+            for i in range(n_patches_y):
+                for j in range(n_patches_x):
+                    patch = img_data[i*patch_size:(i+1)*patch_size, j*patch_size:(j+1)*patch_size]
+                    patches.append(patch)
+            patches = np.array(patches)
+
+            preprocessed_patches = []
+            for patch in patches:
+                img = Image.fromarray(patch)
+                img = img.convert('RGB')
+                img = img.resize((128, 128))
+                img_array = np.array(img) / 255.0
+                preprocessed_patches.append(img_array)
+            preprocessed_patches = np.array(preprocessed_patches)
+
+            model = deep_learning_models[model_name]
+            predictions = model.predict(preprocessed_patches)
+            predictions = predictions.reshape((n_patches_y, n_patches_x))
+
+            return predictions
+        else:
+            return "No file uploaded"
+    else:
+        return "Model not found"
+
+inputs_traditional = [
     gr.Dropdown(choices=list(models.keys()), label='Model'),
     gr.Number(label='Year'),
     gr.Textbox(label='State'),
     gr.Textbox(label='Crop'),
-    gr.Number(label='Yield')
+    gr.Number(label='Yield'),
+]
+outputs_traditional = gr.Textbox(label='Predicted Profit')
+
+inputs_deep_learning = [
+    gr.Dropdown(choices=list(deep_learning_models.keys()), label='Model'),
+    gr.File(label='Upload TIFF File')
 ]
-outputs = gr.Textbox(label='Predicted Profit')
-
-demo = gr.Interface(
-    fn=predict,
-    inputs=inputs,
-    outputs=outputs,
-    title="Profit Prediction using various ML models",
-    theme=gr.themes.Soft()
-)
+outputs_deep_learning = gr.Textbox(label='Predictions')
+
+with gr.Blocks() as demo:
+    with gr.Tab("Traditional ML Models"):
+        gr.Interface(
+            fn=predict_traditional,
+            inputs=inputs_traditional,
+            outputs=outputs_traditional,
+            title="Profit Prediction using Traditional ML Models"
+        ).launch()
+        
+    with gr.Tab("Deep Learning Models"):
+        gr.Interface(
+            fn=predict_deep_learning,
+            inputs=inputs_deep_learning,
+            outputs=outputs_deep_learning,
+            title="Crop Yield Prediction using Deep Learning Models"
+        ).launch()
 
 demo.launch()

requirements.txt

@@ -1,3 +1,7 @@
 pandas
 scikit-learn
-gradio
+gradio
+tensorflow
+rasterio
+Pillow
+matplotlib