Merge branch 'main' of https://huggingface.co/spaces/yash-gupta-01/machineLearning

home.py

@@ -134,4 +134,4 @@ st.markdown("""
 <footer>
     <p>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</p>
 </footer>
-""", unsafe_allow_html=True)
+""", unsafe_allow_html=True)

pages/image_augmentation.py

@@ -0,0 +1,183 @@
+import streamlit as st
+from PIL import Image, ImageOps
+import numpy as np
+import io
+
+# Improved App Styling
+st.set_page_config(
+    page_title="Modern Image Augmentation",
+    page_icon="✨",
+    layout="wide",
+)
+
+# Custom CSS for modern look
+st.markdown(
+    """
+    <style>
+    /* App Background */
+    .stApp {
+        background: linear-gradient(135deg, #1f1f1f, #000000);
+        color: #ffffff;
+    }
+
+    /* Title Styling */
+    h1 {
+        text-align: center;
+        color: #00ffcc;
+        font-family: 'Arial Black', sans-serif;
+    }
+
+    /* Sidebar Styling */
+    [data-testid="stSidebar"] {
+        background-color: #1c1c1c;
+        border-right: 2px solid #00ffcc;
+    }
+
+    /* Slider Styling */
+    [class^="css-qbe2hs"] {
+        color: #00ffcc !important;
+    }
+
+    /* Buttons */
+    button {
+        background-color: #00ffcc !important;
+        border: none;
+        font-weight: bold;
+    }
+
+    button:hover {
+        background-color: #00e6b8 !important;
+    }
+
+    /* Image Uploader */
+    [data-testid="stFileUploadDropzone"] {
+        background: #1c1c1c;
+        border: 2px dashed #00ffcc;
+        border-radius: 10px;
+    }
+
+    /* Augmentation Options Styling */
+    [class^="stRadio"] > div > label {
+        color: #ffffff;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+st.title("✨ Modern Image Data Augmentation ✨")
+
+# File uploader for image
+uploaded_image = st.file_uploader("Upload an Image to Get Started", type=["jpg", "png", "jpeg"])
+
+if uploaded_image:
+    try:
+        image = Image.open(uploaded_image)
+        col1, col2 = st.columns(2)
+
+        # Display original image
+        with col1:
+            st.subheader("Original Image")
+            st.image(image, caption="Uploaded Image", use_container_width=True)
+
+        st.subheader("✨ Select an Augmentation")
+
+        # Augmentation options
+        augmentation_option = st.radio(
+            "Choose an Augmentation Type:",
+            [
+                "None",
+                "Cropping",
+                "Flipping",
+                "Rotation",
+                "Zooming In",
+                "Zooming Out",
+                "Translation",
+                "Shearing",
+            ],
+        )
+
+        augmented_image = image
+
+        # Augmentation Logic
+        if augmentation_option == "Cropping":
+            st.info("Adjust the crop sliders to modify the image.")
+            left = st.slider("Left Crop", 0, image.width // 2, 0)
+            top = st.slider("Top Crop", 0, image.height // 2, 0)
+            right = st.slider("Right Crop", 0, image.width // 2, 0)
+            bottom = st.slider("Bottom Crop", 0, image.height // 2, 0)
+            augmented_image = image.crop((left, top, image.width - right, image.height - bottom))
+
+        elif augmentation_option == "Flipping":
+            flip_option = st.selectbox("Choose Flip Type", ["Horizontal", "Vertical"])
+            if flip_option == "Horizontal":
+                augmented_image = ImageOps.mirror(image)
+            elif flip_option == "Vertical":
+                augmented_image = ImageOps.flip(image)
+
+        elif augmentation_option == "Rotation":
+            rotation_angle = st.slider("Rotation Angle (°)", 0, 360, 0)
+            if rotation_angle:
+                augmented_image = image.rotate(rotation_angle, expand=True)
+
+        elif augmentation_option == "Zooming In":
+            zoom_factor = st.slider("Zoom Factor (%)", 100, 200, 100) / 100.0
+            if zoom_factor != 1.0:
+                width, height = image.size
+                new_width = int(width * zoom_factor)
+                new_height = int(height * zoom_factor)
+                zoomed_image = image.resize((new_width, new_height))
+                augmented_image = zoomed_image.crop(((new_width - width) // 2, (new_height - height) // 2,
+                                                     (new_width + width) // 2, (new_height + height) // 2))
+
+        elif augmentation_option == "Zooming Out":
+            zoom_factor = st.slider("Zoom Factor (%)", 50, 100, 100) / 100.0
+            if zoom_factor != 1.0:
+                width, height = image.size
+                new_width = int(width * zoom_factor)
+                new_height = int(height * zoom_factor)
+                zoomed_image = image.resize((new_width, new_height))
+                padded_image = Image.new("RGB", (width, height), (0, 0, 0))  # Black padding
+                padded_image.paste(zoomed_image, ((width - new_width) // 2, (height - new_height) // 2))
+                augmented_image = padded_image
+
+        elif augmentation_option == "Translation":
+            translation_x = st.slider("Horizontal Shift (Pixels)", -100, 100, 0)
+            translation_y = st.slider("Vertical Shift (Pixels)", -100, 100, 0)
+            if translation_x or translation_y:
+                translation_matrix = (1, 0, translation_x, 0, 1, translation_y)
+                augmented_image = image.transform(
+                    image.size, Image.AFFINE, translation_matrix, fillcolor=(0, 0, 0)
+                )
+
+        elif augmentation_option == "Shearing":
+            shear_angle = st.slider("Shearing Angle (°)", -45, 45, 0)
+            if shear_angle:
+                shear_matrix = (1, np.tan(np.radians(shear_angle)), 0, 0, 1, 0)
+                augmented_image = image.transform(
+                    (image.width + abs(int(image.height * np.tan(np.radians(shear_angle)))), image.height),
+                    Image.AFFINE,
+                    shear_matrix,
+                    fillcolor=(0, 0, 0),
+                )
+
+        # Display Augmented Image
+        with col2:
+            st.subheader(f"{augmentation_option} Applied")
+            st.image(augmented_image, caption="Augmented Image", use_container_width=True)
+
+        # Download button
+        buffer = io.BytesIO()
+        augmented_image.save(buffer, format="PNG")
+        buffer.seek(0)
+        st.download_button(
+            "🎉 Download Augmented Image 🎉",
+            buffer,
+            file_name="augmented_image.png",
+            mime="image/png",
+        )
+
+    except Exception as e:
+        st.error(f"An error occurred: {e}")
+else:
+    st.warning("Please upload an image to begin.")

pages/life_cycle_of_ml_project.py

@@ -0,0 +1,65 @@
+<<<<<<< HEAD
+=======
+import matplotlib.pyplot as plt
+import numpy as np
+
+def draw_lifecycle_diagram():
+    # Labels and colors for the lifecycle phases
+    labels = [
+        "1. Gathering Data", 
+        "2. Data Preparation", 
+        "3. Data Wrangling", 
+        "4. Analyze Data", 
+        "5. Train Model", 
+        "6. Test Model", 
+        "7. Deployment"
+    ]
+    colors = [
+        "#3CB371", "#8FBC8F", "#00CED1", "#1E90FF", 
+        "#6A5ACD", "#FF8C00", "#DC143C"
+    ]
+
+    # Create a figure and axis with equal aspect ratio
+    fig, ax = plt.subplots(figsize=(9, 9), subplot_kw={"aspect": "equal"})
+    size = 0.3  # Width of the pie sections
+
+    # Create pie sections for the lifecycle phases
+    wedges, _ = ax.pie(
+        [1] * len(labels), 
+        colors=colors, 
+        radius=1, 
+        startangle=90, 
+        wedgeprops=dict(width=size, edgecolor='w')
+    )
+
+    # Add text labels around the circle
+    for i, wedge in enumerate(wedges):
+        # Calculate the angle for the label placement
+        angle = (wedge.theta2 - wedge.theta1) / 2.0 + wedge.theta1
+        x = np.cos(np.deg2rad(angle))
+        y = np.sin(np.deg2rad(angle))
+
+        # Add label text
+        ax.text(
+            1.2 * x, 1.2 * y, labels[i],
+            ha="center", va="center", fontsize=10, weight="bold",
+            bbox=dict(boxstyle="round,pad=0.3", facecolor=colors[i], edgecolor="w")
+        )
+
+    # Add center text with a descriptive title
+    ax.text(
+        0, 0, "Machine Learning\nLifecycle", 
+        ha="center", va="center", fontsize=16, weight="bold", color="black",
+        bbox=dict(boxstyle="round,pad=0.5", facecolor="white", edgecolor="black")
+    )
+
+    # Clean up the diagram style
+    ax.set(aspect="equal", xticks=[], yticks=[], title="Machine Learning Lifecycle")
+    return fig
+
+# Save the diagram or display it in Streamlit
+if __name__ == "__main__":
+    # Display the diagram
+    fig = draw_lifecycle_diagram()
+    plt.show()
+>>>>>>> 8f9a0e1fa29de18412c739d9d5c3da24894df34d