Update pages/Types of Data.py

pages/Types of Data.py

@@ -1,98 +1,25 @@
+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
 
-# 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"])
-
-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 like business, 
-    science, and technology. 🧠
-    """)
-
-    st.header("Types of Data 📊")
-    st.write("Data can exist in various forms depending on its source and nature. Common forms include:")
-    st.markdown("""
-    1. **Structured Data**
-    2. **Semi-Structured Data**
-    3. **Unstructured Data**
-    """)
-
-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("Structured data is organized in rows and columns, like in databases and spreadsheets.")
-
-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("Semi-structured data includes JSON, XML, and other formats that have some organizational properties.")
-
-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("Unstructured data includes images, videos, and text that do not follow a specific schema.")
-
-    # Gradio Interface for Image Augmentation
-    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."
-    )
-
-    if st.button("Launch Image Augmentation Tool"):
-        app.launch()
+# 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.")