first commit

cotton_app.py

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+import tensorflow as tf
+model = tf.keras.models.load_model('cotton_crop.h5')
+import streamlit as st
+st.write("""
+         # Cotton crop identification
+         """
+         )
+#st.write("This is a simple image classification web app to predict rock-paper-scissor hand sign")
+file = st.file_uploader("Please upload an image file", type=["jpg", "png"])
+
+import cv2
+from PIL import Image, ImageOps
+import numpy as np
+def import_and_predict(image_data, model):
+    
+        size = (300,300)    
+        image = ImageOps.fit(image_data, size, Image.ANTIALIAS)
+        image = np.asarray(image)
+        img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        img_resize = (cv2.resize(img, dsize=(300, 300),    interpolation=cv2.INTER_CUBIC))/255.
+        
+        img_reshape = img_resize[np.newaxis,...]
+    
+        prediction = model.predict(img_reshape)
+        
+        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)
+    prediction = import_and_predict(image, model)
+    
+#    if np.argmax(prediction) == 0:
+    if prediction[0][0]==1.0:
+        st.write("Cotton crop")
+    else:
+        st.write("Not a cotton crop")
+    st.write(prediction)

cotton_crop.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:889341b135a27d285b0a5cbb2887a57650bd67b3ec0d10818f6ff227a1df861f
+size 2763943432