Upload 10 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+1/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

1/fingerprint.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:169ddeaf53bb0d5a2e5bc95f10451649b077733685c7336135aea9628ed5fd8d
+size 59

1/keras_metadata.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bd190c796996520cb6535f5301d9a57316589f3f9c5a930b9d93a3f0c6dbb7ed
+size 32271

1/saved_model.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c5e3d8b097a0631279608b125f44b7fd3b9e3f4927af50b0c5421442f3269721
+size 276478

1/variables/variables.data-00000-of-00001

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:25e2b37e304e0e6100d4c54e014448d03636166fbb484432547d18244f6361d2
+size 2220878

app.py

@@ -0,0 +1,72 @@
+import streamlit as st
+import numpy as np
+from PIL import Image
+from keras.models import load_model
+
+# Load the pre-trained model for banana ripeness detection
+banana_model = load_model("trained model/best_model.h5")
+
+# Define class names for the banana disease detection
+class_names_disease = {
+    0: 'BUNCHY_TOP',
+    1: 'CORDANA',
+    2: 'PANAMA',
+    3: 'SIGATOKA'
+}
+
+# Define class names for the banana ripeness detection
+class_names_ripeness = ["Banana_G1", "Banana_G2", "Rotten"]
+model = load_model("trained model/best_model.h5")
+
+def preprocess_image(image):
+    img = Image.open(image)
+    img = img.resize((256, 256))  # Resize the image to the input size of the model
+    img_array = np.array(img)
+    img_array = img_array / 255.0  # Normalize the pixel values
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    return img_array
+
+
+def predict(image):
+    img_array = preprocess_image(image)
+    predictions = model.predict(img_array)
+    predicted_class = np.argmax(predictions)
+    predicted_label = class_names_disease[predicted_class]
+    return predicted_label
+
+def predict_disease(uploaded_file):
+    if uploaded_file is not None:
+        predicted_label = predict(uploaded_file)
+        return predicted_label
+
+
+def predict_ripeness(image):
+    img_array = preprocess_image(image)
+    predictions = banana_model.predict(img_array)
+    predicted_class = np.argmax(predictions)
+    predicted_label = class_names_ripeness[predicted_class]
+    return predicted_label
+
+def main():
+    st.title("Banana Analysis App")
+    st.write("Choose an option to analyze bananas")
+
+    # Options for banana analysis
+    analysis_option = st.radio("Choose an option", ["Banana Disease Detection", "Banana Ripeness Detection"])
+
+    # File uploader
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        # Display the uploaded image
+        st.image(uploaded_file, caption='Uploaded Image', use_column_width=True)
+        if st.button("Analyze"):
+            if analysis_option == "Banana Disease Detection":
+                predicted_label = predict_disease(uploaded_file)
+                st.success(f"Predicted disease: {predicted_label}")
+            elif analysis_option == "Banana Ripeness Detection":
+                predicted_label = predict_ripeness(uploaded_file)
+                st.success(f"Predicted ripeness: {predicted_label}")
+
+if __name__ == '__main__':
+    main()

requirements.txt

@@ -0,0 +1,13 @@
+Flask==2.0.2
+Flask-PyMongo==2.3.0
+Werkzeug==2.2.2
+requests==2.26.0
+pandas==1.3.4
+tensorflow-cpu==2.7.0
+opencv-python==4.5.4.58
+keras==2.7.0
+Keras-Preprocessing==1.1.2
+twilio==7.2.0
+Pillow==8.4.0
+protobuf==3.20.*
+streamlit==1.34.0

runtime.txt

@@ -0,0 +1 @@
+python-3.8

streamlit.py

@@ -0,0 +1,77 @@
+import os
+os.environ["TF_USE_LEGACY_KERAS"] = "1"
+
+from keras.models import load_model
+import streamlit as st
+import numpy as np
+from io import BytesIO
+from PIL import Image
+import tensorflow as tf
+
+st.markdown(
+    """
+    <style>
+    .reportview-container {
+        background: url('./bg.jpg');
+        background-size: cover;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+st.markdown("# Bananas Maturity Classification ")
+st.sidebar.markdown("# Main Page")
+
+MODEL = load_model("./1")
+
+CLASS_NAMES = ["Banana_G1", "Banana_G2", "Rotten"]
+
+
+def read_file_as_image(data) -> np.ndarray:
+    image = np.array(Image.open(BytesIO(data)))
+    return image
+
+
+def predict(
+    file,
+):
+    image = read_file_as_image(file.read())
+    shape = image.shape
+    img_batch = np.expand_dims(image, 0)
+    # resize image to (256,256,3)
+    img_batch = tf.image.resize(img_batch, (256, 256))
+    prediction = MODEL.predict(img_batch)
+    predicted_class = CLASS_NAMES[np.argmax(prediction[0])]
+    confidence = np.max(prediction[0])
+    if predicted_class == "Banana_G2":
+        predicted_class = "Green Banana- not ripen"
+    elif predicted_class == "Banana_G1":
+        predicted_class = "Mature Banana -ripen"
+    else:
+        predicted_class = "Rotten Banana"
+    return {
+        'class': predicted_class,
+        'confidence': float(confidence)
+    }
+
+
+st.write("Upload an image or capture one with your camera")
+
+option = st.selectbox("Choose an option", ["Upload Image", "Capture Image"])
+
+if option == "Upload Image":
+    uploaded_file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
+    if uploaded_file is not None:
+        result = predict(uploaded_file)
+        predicted_class = result['class']
+        confidence = result['confidence']
+        if predicted_class == "Green Banana- not ripen":
+            color = 'green'
+        elif predicted_class == "Mature Banana -ripen":
+            color = 'yellow'
+        else:
+            color = 'red'
+        st.markdown(
+            f'<p style="color:{color}; font-size:24px;">Predicted class: {predicted_class}, Confidence: {confidence:.2f}</p>',
+            unsafe_allow_html=True)

trained model/best_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:592009892209cc32196e69c98094ab96beef82019a53d535c59343b181256b98
+size 81761440