Upload 3 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
+fahrnphi_exam_project.keras filter=lfs diff=lfs merge=lfs -text

fahrnphi_exam_project.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:817ff30404dab55d0679049aafc26be1e7f963def186972d49c5c7cc186edf0f
+size 250707767

intelligent_recipe_finder.py

@@ -0,0 +1,68 @@
+import streamlit as st
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import cv2
+
+# Load the saved model
+model = tf.keras.models.load_model("/mnt/data/fahrnphi_exam_project.keras")
+
+# Set image dimensions
+img_height, img_width = 150, 150  # Input size for the model
+
+# Define a function for prediction and returning labels and probabilities
+def predict_labels_and_probabilities(image_path):
+    # Load the image using OpenCV
+    img = cv2.imdecode(np.frombuffer(image_path.read(), np.uint8), 1)
+    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    
+    # Assuming the input image might contain multiple ingredients, we will process it in patches.
+    # For simplicity, let's divide the image into 4 patches and classify each one
+    h, w, _ = img.shape
+    patches = [
+        img_rgb[0:h//2, 0:w//2],  # Top-left
+        img_rgb[0:h//2, w//2:w],  # Top-right
+        img_rgb[h//2:h, 0:w//2],  # Bottom-left
+        img_rgb[h//2:h, w//2:w],  # Bottom-right
+    ]
+    
+    predictions = []
+    
+    for patch in patches:
+        patch_resized = cv2.resize(patch, (img_height, img_width))
+        patch_array = image.img_to_array(patch_resized)
+        patch_array = np.expand_dims(patch_array, axis=0)
+        patch_array /= 255.  # Scale pixel values
+        
+        preds = model.predict(patch_array)
+        class_idx = np.argmax(preds[0])
+        
+        # Map class indices to class names
+        class_labels = {
+            0: 'Bell Pepper', 1: 'Carrot', 2: 'Garlic', 3: 'Ginger',
+            4: 'Jalapeno', 5: 'Onion', 6: 'Potato', 7: 'Sweetpotato', 8: 'Tomato'
+        }
+        predicted_class = class_labels[class_idx]
+        probability = preds[0][class_idx]
+        
+        predictions.append((predicted_class, probability))
+    
+    return predictions
+
+# Streamlit App
+st.title("Intelligent Recipe Finder Classification")
+
+uploaded_file = st.file_uploader("Choose an ingredients image...", type="jpg")
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    st.image(uploaded_file, caption='Uploaded Ingredient Image.', use_column_width=True)
+
+    # Perform the prediction and display the results
+    predictions = predict_labels_and_probabilities(uploaded_file)
+    st.write("Predictions for different patches of the image:")
+    
+    for i, (label, probability) in enumerate(predictions):
+        st.write(f"Patch {i+1}:")
+        st.write("Prediction:", label)
+        st.write("Probability:", probability)

requirements.txt

@@ -0,0 +1 @@
+tensorflow