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.gitattributes

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+*.keras filter=lfs diff=lfs merge=lfs -text
+Static/Images/dog2.jpg filter=lfs diff=lfs merge=lfs -text
+Static/Upload/dog2.jpg filter=lfs diff=lfs merge=lfs -text

.vscode/launch.json

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+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "type": "chrome",
+            "request": "launch",
+            "name": "Launch Chrome against localhost",
+            "url": "http://localhost:8080",
+            "webRoot": "${workspaceFolder}"
+        }
+    ]
+}

App/DBI_Model.py

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+import cv2
+from dog_vision import identificationPipeline

App/dog_vision.py

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+import tensorflow as tf
+import tensorflow_hub as hub
+import numpy as np
+import os
+import pandas as pd
+import cv2
+import keras
+from matplotlib.pyplot import imread
+from flask import jsonify
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+
+# Create a function to load a trained model
+def load_model(model_path):
+    '''
+    Loads a saved model from a specified path.
+    '''
+    print(f'Loading saved model from: {model_path}')
+    model = tf.keras.models.load_model(model_path,
+                                        custom_objects={'KerasLayer': hub.KerasLayer})
+    return model
+
+loaded_full_model = load_model(r'C:\Users\piyus\OneDrive\Desktop\FlaskApp\Model\final-keras-model-xception.keras')
+
+
+
+labels_csv = pd.read_csv(r'C:\Users\piyus\OneDrive\Desktop\FlaskApp\Model\labels.csv')
+labels = labels_csv['breed'].to_numpy()
+
+# Find the unique label values
+unique_breeds = np.unique(labels)
+IMG_SIZE = 150
+
+# Turn probabilities into their respective label (easier to understand)
+def get_pred_label(prediction_probabilities):
+    '''
+    Turn an array of prediction probabilities into a label.
+    '''
+    return unique_breeds[np.argmax(prediction_probabilities)]
+
+
+# Identification Pipeline
+# Identification Pipeline
+def identificationPipeline(img_path):
+    '''
+    Takes an image path, preprocesses it, and returns the predicted breed.
+    '''
+    print(f"Received img_path: {img_path}")
+
+    # Validate Image Path
+    if not os.path.exists(img_path):
+        raise FileNotFoundError(f"Image not found at: {img_path}")
+
+    # Read & Preprocess Image
+    try:
+        image = tf.io.read_file(img_path)
+        image = tf.image.decode_jpeg(image, channels=3)
+        image = tf.image.convert_image_dtype(image, tf.float32)
+        image = tf.image.resize(image, size=[IMG_SIZE, IMG_SIZE])
+        image_reshaped = tf.reshape(image, (1, IMG_SIZE, IMG_SIZE, 3))  # Add batch dimension
+
+        # Predict
+        results = loaded_full_model.predict(image_reshaped)
+        confidence = np.max(results)
+        pred_label = get_pred_label(results)
+        
+        if confidence < 0.1:
+            return "Unknown Object (Not a Dog)"
+
+        print(f"Predicted Label: {pred_label}")
+    
+        return pred_label
+    
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+

App/views.py

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+from flask import Flask,render_template, request, jsonify, redirect, url_for
+import os
+import cv2
+from App.dog_vision import identificationPipeline
+import numpy as np
+
+app = Flask(__name__)
+UPLOAD_FOLDER ='Static/Upload'
+PREDICT_FOLDER = './Static/Predict'
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+
+@app.route('/', methods=['GET', 'POST'])
+def index():
+    if request.method == 'POST':
+        file = request.files.get('image_name')
+        
+        # ✅ Check if file is provided
+        if not file or file.filename == '':
+            return jsonify({"error": "No file was uploaded"}), 400
+        
+        # ✅ Save Image
+        path = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
+        file.save(path)
+
+        print(f"Received path in views.py: {path}, Type: {type(path)}")
+
+        # ✅ Get Prediction (Expecting a label string)
+        output = identificationPipeline(path)
+
+        # ✅ Ensure the output is a valid string (Label name)
+        if isinstance(output, np.ndarray):
+            output = output.tolist()  # Convert NumPy array to Python list
+        elif hasattr(output, 'numpy'):
+            output = output.numpy().tolist()  # Convert TensorFlow tensor to list
+        elif isinstance(output, dict):
+            return jsonify(output)  # If it's a dictionary, return as JSON
+        elif not isinstance(output, str):
+            return jsonify({"error": "Unexpected output format"}), 500
+
+        print(f"Output from identificationPipeline: {output}")  # Debugging line
+
+        # ✅ Read and save the image properly
+        image = cv2.imread(path)
+        if image is None:
+            return jsonify({"error": "Uploaded file is not a valid image format or is corrupted"}), 400
+
+        pred_filename = 'image.jpg'
+        pred_path = os.path.join(PREDICT_FOLDER, pred_filename)
+        cv2.imwrite(pred_path, image)
+
+        return redirect(url_for('breedIdentification', filename=file.filename, prediction=output))
+
+    return render_template('index.html')
+
+
+@app.route("/breedIdentification/")
+def breedIdentification():
+    filename = request.args.get('filename')
+    prediction = request.args.get('prediction')
+
+    if not filename or not prediction:
+        return redirect(url_for('index'))  # Redirect to home if missing data
+
+    return render_template('breedIdentification.html', filename=filename, prediction=prediction)

Aptfile

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+libsm6
+libxrender1
+libfontconfig1
+libice6

Model/final-keras-model-xception.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:37f321464e18cf48b3be5acf710f46595d9c91ee4cecf00bdb0426bea1210249
+size 253469690

Procfile

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+web: gunicorn main:app