Update pages/Types of Data.py

pages/Types of Data.py

@@ -1,25 +1,97 @@
-import os
-import subprocess
-import sys
 import streamlit as st
+from streamlit_lottie import st_lottie
+import requests
+from transformers import pipeline
+import gradio as gr
+from PIL import Image, ImageOps
+import numpy as np
+import random
 
-# Install transformers if not already installed
-try:
-    from transformers import pipeline
-except ModuleNotFoundError:
-    subprocess.check_call([sys.executable, "-m", "pip", "install", "transformers"])
-    from transformers import pipeline
-
-# Title
-st.title("Data Types Analysis with Transformers")
-
-# User input
-user_input = st.text_area("Enter your text here:")
-
-if user_input:
-    classifier = pipeline("sentiment-analysis")
-    result = classifier(user_input)
-    st.write("### Sentiment Analysis Result:")
-    st.write(result)
-else:
-    st.write("Please enter some text to analyze.")
+# Function to load Lottie animation from a URL
+def load_lottie_url(url: str):
+    r = requests.get(url)
+    if r.status_code != 200:
+        return None
+    return r.json()
+
+# Load animations using URLs
+structured_animation_url = "https://assets10.lottiefiles.com/packages/lf20_4j6cnjjm.json"
+semi_structured_animation_url = "https://assets10.lottiefiles.com/packages/lf20_0fhcmhgf.json"
+unstructured_animation_url = "https://assets10.lottiefiles.com/packages/lf20_rekwjvy0.json"
+
+# Sidebar navigation
+st.sidebar.title("Navigation")
+page = st.sidebar.radio("Choose a page", ["Home", "Structured Data", "Semi-Structured Data", "Unstructured Data", "Image Augmentation"])
+
+if page == "Home":
+    st.title("Understanding Data and Its Types 🌐")
+    st.header("What is Data?")
+    st.write("""
+    **Data** refers to raw facts, figures, or information that can be collected, measured, and analyzed for specific purposes.
+    It serves as the foundation for generating insights, making decisions, and solving problems in various fields.
+    """)
+
+elif page == "Structured Data":
+    st.title("Structured Data 📋")
+    animation = load_lottie_url(structured_animation_url)
+    if animation:
+        st_lottie(animation, height=300, key="structured_animation")
+    st.write("""
+    **Definition**: Structured data refers to data organized in predefined formats like tables, making it easily searchable.
+    """)
+
+elif page == "Semi-Structured Data":
+    st.title("Semi-Structured Data 🧩")
+    animation = load_lottie_url(semi_structured_animation_url)
+    if animation:
+        st_lottie(animation, height=300, key="semi_structured_animation")
+    st.write("""
+    **Definition**: Semi-structured data lacks a strict schema but is organized through tags and markers.
+    """)
+
+elif page == "Unstructured Data":
+    st.title("Unstructured Data 🗂️")
+    animation = load_lottie_url(unstructured_animation_url)
+    if animation:
+        st_lottie(animation, height=300, key="unstructured_animation")
+    st.write("""
+    **Definition**: Unstructured data lacks predefined organization, including images, videos, and audio files.
+    """)
+
+# Image Augmentation Section
+elif page == "Image Augmentation":
+    st.title("Image Augmentation Tool 🖼️")
+
+    def augment_image(image, crop_size, flip, rotation):
+        img = Image.fromarray(image)
+        if crop_size > 0:
+            width, height = img.size
+            left = random.randint(0, crop_size)
+            top = random.randint(0, crop_size)
+            right = width - random.randint(0, crop_size)
+            bottom = height - random.randint(0, crop_size)
+            img = img.crop((left, top, right, bottom))
+        if flip:
+            img = ImageOps.mirror(img)
+        if rotation != 0:
+            img = img.rotate(rotation, expand=True)
+        return np.array(img)
+
+    def interface(image, crop_size, flip, rotation):
+        augmented_image = augment_image(image, crop_size, flip, rotation)
+        return augmented_image
+
+    app = gr.Interface(
+        fn=interface,
+        inputs=[
+            gr.Image(type="numpy"),
+            gr.Slider(0, 100, step=1, label="Crop Size"),
+            gr.Checkbox(label="Flip"),
+            gr.Slider(0, 360, step=1, label="Rotation Angle")
+        ],
+        outputs=gr.Image(type="numpy"),
+        title="Image Augmentation Tool",
+        description="Upload an image to apply cropping, flipping, and rotation."
+    )
+
+    app.launch()