Delete model_1.py

model_1.py

@@ -1,103 +0,0 @@
-# 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)