Update app.py

app.py

@@ -1,46 +1,50 @@
-import streamlit as st
-from PIL import Image
-from tensorflow.keras.models import load_model
-import tensorflow as tf
-import numpy as np
-from keras.preprocessing.image import img_to_array
-import os
-
-
-@st.cache_resource
-def load_model():
-    model_path = "model.keras"  # Update with the absolute file path
-    return tf.keras.models.load_model(model_path)
-
-model = load_model()
-
-def prepare_image(img):
-    img = img.resize((220, 220))
-    img_array = img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0)
-
-    prediction = model.predict(img_array)
-    predicted_class = "Smoking" if prediction > 0.5 else "Not Smoking"
-    
-    return predicted_class, prediction[0]
-
-def run():
-    st.title("Smoking or Not Smoking Detection")
-    img_file = st.file_uploader("Choose an Image", type=["jpg", "png"])
-    
-    if img_file is not None:
-        img = Image.open(img_file).resize((250, 250))
-        st.image(img, use_column_width=False)
-        
-        # Create the directory if it doesn't exist
-        upload_dir = './upload_images/'
-        os.makedirs(upload_dir, exist_ok=True)
-        
-        save_image_path = os.path.join(upload_dir, img_file.name)
-        with open(save_image_path, "wb") as f:
-            f.write(img_file.getbuffer())
-
-        predicted_class, score = prepare_image(img)
-        st.success(f"**Predicted : {predicted_class}, Score: {score}**")
-
-run()
+import streamlit as st
+from PIL import Image
+import tensorflow as tf
+import numpy as np
+import os
+
+@st.cache_resource
+def load_captcha_model():
+    model_path = "captcha_model.keras"  # Update with the actual CAPTCHA model path
+    return tf.keras.models.load_model(model_path)
+
+model = load_captcha_model()
+
+def prepare_captcha_image(img):
+    # Resize image to the input shape required by the CAPTCHA model
+    img = img.resize((200, 50))  # Adjust size according to the trained model
+    img_array = np.array(img)
+    img_array = img_array / 255.0  # Normalize image
+    img_array = np.expand_dims(img_array, axis=0)
+
+    # Predict the CAPTCHA characters
+    predictions = model.predict(img_array)
+
+    # Assuming the model outputs one-hot encoded characters, decode the predictions
+    decoded_captcha = ''.join([chr(np.argmax(pred) + ord('A')) for pred in predictions])
+
+    return decoded_captcha, predictions
+
+def run():
+    st.title("CAPTCHA Prediction")
+    img_file = st.file_uploader("Upload a CAPTCHA Image", type=["jpg", "png"])
+
+    if img_file is not None:
+        img = Image.open(img_file)
+        st.image(img, use_column_width=False)
+
+        # Create the directory if it doesn't exist
+        upload_dir = './upload_images/'
+        os.makedirs(upload_dir, exist_ok=True)
+
+        # Save the uploaded image
+        save_image_path = os.path.join(upload_dir, img_file.name)
+        with open(save_image_path, "wb") as f:
+            f.write(img_file.getbuffer())
+
+        # Predict the CAPTCHA
+        predicted_captcha, score = prepare_captcha_image(img)
+        st.success(f"**Predicted CAPTCHA: {predicted_captcha}**")
+
+run()