Create test.py

test.py

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+# Trained on 80 epochs at Batch size 256 with Learning Rate of 0.001 under 50 seconds!!
+from PIL import Image, ImageOps
+import numpy as np
+import pandas as pd
+import streamlit as st
+import tensorflow as tf
+
+# Suppress warnings
+import warnings
+warnings.filterwarnings('ignore', category=UserWarning)
+warnings.filterwarnings('ignore', category=FutureWarning)
+
+# Disable scientific notation for clarity
+np.set_printoptions(suppress=True)
+
+def load_model_teachable():
+    # Load the model
+    model = tf.keras.models.load_model("cnn_model.h5")
+    return model
+
+with st.spinner('Model is being loaded..'):
+    model = load_model_teachable()
+
+st.write("""
+         # Teachable Machine Model 
+         """
+         )
+
+st.sidebar.info("You should be happy if it classifies as - Healthy plant 🙂")
+
+file = st.file_uploader("Upload the image to be classified", type=["jpg", "png"])
+st.set_option('deprecation.showfileUploaderEncoding', False)
+
+
+def upload_predict_teachable(image, model, class_names):
+
+    # Create the array of the right shape to feed into the keras model
+    # The 'length' or number of images you can put into the array is
+    # determined by the first position in the shape tuple, in this case 1
+    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+
+    # Replace this with the path to your image
+    image_RGB = image.convert("RGB")
+
+    # resizing the image to be at least 224x224 and then cropping from the center
+    image_resized = image_RGB.resize((224, 224), resample=Image.LANCZOS)
+
+    # turn the image into a numpy array
+    image_array = np.asarray(image_resized)
+
+    # Normalize the image
+    normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
+
+    # Load the image into the array
+    data[0] = normalized_image_array
+
+    # Predicts the model
+    prediction = model.predict(data)
+    return prediction
+
+
+if file is None:
+    st.text("Please upload an image file")
+else:
+    image = Image.open(file)
+    st.image(image, use_column_width=True)
+    
+    # Load the labels
+    class_names = open("pages/labels.txt", "r").readlines()
+    prediction = upload_predict_teachable(image, model,class_names)
+    index = np.argmax(prediction)
+    class_name = class_names[index]
+    confidence_score = prediction[0][index]
+
+    # Print prediction and confidence score
+    result = f"Your plant is suffering from: {class_name[2:]}"
+    result_score = "✅ Accurate prediction score is: {} / 100".format("%.2f"  %  confidence_score)
+    st.success(result)
+    st.info(result_score)
+
+
+st.markdown("""
+    #### Know more about this disease and suggestions to prevent them from spreading.
+
+    - *Just copy the disease name (result) from above & paste it in the below input box as it is!*
+""")
+
+# Loading diseases info & management strategies
+df = pd.read_csv('pages/Disease_solutions.csv')
+disease = st.text_input('Enter the disease name below 👇', '')
+
+# Selecting rows based on condition
+about = df.loc[df['Diseases'] == disease, ['Type', 'Description']]
+strategies = df.loc[df['Diseases'] == disease, 'Management Strategies']
+
+# Print diseases' other info
+tab1, tab2 = st.tabs(["👀 Description", "⭐ Solution"])
+
+with tab1:
+    st.dataframe(about, use_container_width=True)
+        
+with tab2:
+    st.dataframe(strategies, use_container_width=True)