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README.md
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@@ -4,20 +4,65 @@ app_file: app.py
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sdk: gradio
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sdk_version: 5.11.0
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---
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#
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- Image Classification
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- Text Summarization
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- Data Visualization
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##
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```bash
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pip install -r requirements.txt
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python app.py
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```
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To deploy to Hugging Face Spaces:
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```bash
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-
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sdk: gradio
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sdk_version: 5.11.0
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---
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# AI Applications Portfolio
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A collection of interactive AI applications built with Gradio, showcasing various capabilities:
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## Applications
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### 1. Image Classification
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- Upload and analyze images
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- Get detailed image information
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- View thumbnails and analysis results
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### 2. Text Summary
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- Analyze text statistics
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- Generate summaries
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- Support for multiple languages
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### 3. Data Visualization
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- Create interactive graphs
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- Visualize numerical data
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- Customizable plotting options
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## Tech Stack
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- Python 3.8+
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- Gradio 4.0+
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- Matplotlib 3.7+
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- NumPy 1.24+
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- Pillow 10.0+
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## Setup and Installation
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1. Clone the repository:
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```bash
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git clone https://github.com/yourusername/portfolio.git
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cd portfolio
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```
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2. Install dependencies:
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```bash
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pip install -r requirements.txt
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```
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3. Run the application:
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```bash
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python app.py
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```
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## Deployment
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The application is deployed on Hugging Face Spaces:
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[View Live Demo](https://huggingface.co/spaces/shuhayas/AI_Applications_Portfolio)
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## Features
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- Modern, responsive UI
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- Interactive applications
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- Real-time processing
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- Cross-platform compatibility
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## Contributing
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Feel free to submit issues and enhancement requests!
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## License
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MIT License - feel free to use this project for your own portfolio!
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app.py
CHANGED
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@@ -3,7 +3,7 @@ import matplotlib.pyplot as plt
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import numpy as np
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from PIL import Image
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#
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custom_css = """
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.container {
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max-width: 900px;
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}
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"""
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#
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def image_classification(image):
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if image is None:
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return "
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#
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info = f"""
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"""
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#
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thumbnail = Image.fromarray(image).copy()
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thumbnail.thumbnail((200, 200))
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return info, thumbnail
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#
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def text_summary(text):
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if not text:
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return "
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#
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char_count = len(text)
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word_count = len(text.split())
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line_count = len(text.splitlines())
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summary = f"""
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{text[:200] + '...' if len(text) > 200 else text}
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"""
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return summary
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#
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def visualize_data(x_input, y_input):
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try:
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x = [float(i.strip()) for i in x_input.split(',')]
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y = [float(i.strip()) for i in y_input.split(',')]
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if len(x) != len(y):
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return None, "
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fig = plt.figure(figsize=(10, 6))
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plt.style.use('seaborn')
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plt.plot(x, y, 'o-', linewidth=2, markersize=8, color='#2e7af5')
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plt.title('
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plt.xlabel('X
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plt.ylabel('Y
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plt.grid(True, linestyle='--', alpha=0.7)
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#
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plt.tick_params(axis='both', which='major', labelsize=10)
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plt.gca().spines['top'].set_visible(False)
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plt.gca().spines['right'].set_visible(False)
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return fig, "
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except ValueError:
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return None, "
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# Gradio
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with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as portfolio:
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#
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with gr.Column(elem_classes="container"):
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gr.Markdown(
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"""
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#
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-
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""",
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elem_classes="gr-header"
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)
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#
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with gr.Tabs():
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#
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with gr.TabItem("📷
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with gr.Column(elem_classes="gr-box"):
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gr.Markdown(
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"""
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###
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""",
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elem_classes="gr-tab-text"
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)
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with gr.Row():
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with gr.Column():
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image_input = gr.Image(
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label="
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type="numpy",
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elem_classes="gr-input"
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)
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image_button = gr.Button(
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"
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elem_classes="gr-button"
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)
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with gr.Column():
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image_output = gr.Textbox(
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label="
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elem_classes="gr-input"
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)
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image_thumbnail = gr.Image(
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label="
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elem_classes="gr-input"
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)
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outputs=[image_output, image_thumbnail]
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)
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#
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with gr.TabItem("📝
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with gr.Column(elem_classes="gr-box"):
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gr.Markdown(
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"""
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###
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-
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""",
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elem_classes="gr-tab-text"
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)
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text_input = gr.Textbox(
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label="
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placeholder="
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lines=5,
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elem_classes="gr-input"
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)
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text_button = gr.Button(
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"
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elem_classes="gr-button"
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)
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text_output = gr.Textbox(
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label="
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elem_classes="gr-input"
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)
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outputs=text_output
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)
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#
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with gr.TabItem("📊
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with gr.Column(elem_classes="gr-box"):
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gr.Markdown(
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"""
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-
###
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-
X
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-
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-
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- X
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- Y
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""",
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elem_classes="gr-tab-text"
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)
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with gr.Row():
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x_input = gr.Textbox(
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label="X
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placeholder="
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value="1, 2, 3, 4, 5",
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elem_classes="gr-input"
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)
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y_input = gr.Textbox(
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label="Y
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placeholder="
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value="10, 20, 30, 40, 50",
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elem_classes="gr-input"
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)
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plot_button = gr.Button(
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"
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elem_classes="gr-button"
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)
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with gr.Row():
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plot_output = gr.Plot(label="
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plot_message = gr.Textbox(
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label="
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elem_classes="gr-input"
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)
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@@ -255,44 +255,44 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as portfolio:
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outputs=[plot_output, plot_message]
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)
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#
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with gr.TabItem("ℹ️
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with gr.Column(elem_classes="gr-box"):
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gr.Markdown(
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"""
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-
###
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-
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-
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-
####
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1. **
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-
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-
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2. **
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-
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-
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3. **
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-
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####
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- Python 3.8+
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- Gradio 4.0+
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- Matplotlib 3.7+
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- NumPy 1.24+
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- Pillow 10.0+
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-
####
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-
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####
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-
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- Email: your.email@example.com
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- Twitter: @yourusername
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- LinkedIn: [Your Name](https://linkedin.com/in/yourprofile)
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@@ -300,6 +300,6 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as portfolio:
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elem_classes="gr-tab-text"
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)
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-
#
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if __name__ == "__main__":
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portfolio.launch(share=True)
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import numpy as np
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from PIL import Image
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# Custom CSS
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custom_css = """
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.container {
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max-width: 900px;
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}
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"""
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# Application 1: Image Classification
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def image_classification(image):
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if image is None:
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return "Please upload an image.", None
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# Get basic image information
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info = f"""
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Image Analysis Results:
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- Size: {image.shape}
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- Color Space: RGB
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- Resolution: {image.shape[0]}x{image.shape[1]} pixels
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"""
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# Generate thumbnail
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thumbnail = Image.fromarray(image).copy()
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thumbnail.thumbnail((200, 200))
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return info, thumbnail
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+
# Application 2: Text Summary
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def text_summary(text):
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if not text:
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return "Please enter text to summarize."
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# Analyze basic text information
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char_count = len(text)
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word_count = len(text.split())
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line_count = len(text.splitlines())
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summary = f"""
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Text Analysis Results:
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- Character Count: {char_count}
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- Word Count: {word_count}
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- Line Count: {line_count}
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Summary:
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{text[:200] + '...' if len(text) > 200 else text}
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"""
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return summary
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# Application 3: Data Visualization
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def visualize_data(x_input, y_input):
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try:
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x = [float(i.strip()) for i in x_input.split(',')]
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y = [float(i.strip()) for i in y_input.split(',')]
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if len(x) != len(y):
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return None, "Error: Number of X and Y coordinates must match."
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fig = plt.figure(figsize=(10, 6))
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plt.style.use('seaborn')
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plt.plot(x, y, 'o-', linewidth=2, markersize=8, color='#2e7af5')
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plt.title('Data Visualization', fontsize=16, pad=20)
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plt.xlabel('X Axis', fontsize=12)
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plt.ylabel('Y Axis', fontsize=12)
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plt.grid(True, linestyle='--', alpha=0.7)
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# Improve graph appearance
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plt.tick_params(axis='both', which='major', labelsize=10)
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plt.gca().spines['top'].set_visible(False)
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plt.gca().spines['right'].set_visible(False)
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+
return fig, "Graph generated successfully."
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except ValueError:
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return None, "Error: Invalid input. Please enter numbers separated by commas."
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# Build Gradio interface
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with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as portfolio:
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# Header
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with gr.Column(elem_classes="container"):
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gr.Markdown(
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"""
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# Portfolio Site
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Welcome to my portfolio site where you can experience various applications I've developed.
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Try out different applications in each tab.
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""",
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elem_classes="gr-header"
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)
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# Tab container
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with gr.Tabs():
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# Image Classification Tab
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with gr.TabItem("📷 Image Classification", elem_classes="gr-tab"):
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with gr.Column(elem_classes="gr-box"):
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gr.Markdown(
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"""
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### Image Classification App
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Upload an image to analyze its features. Higher quality images will yield
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more accurate analysis results.
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""",
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elem_classes="gr-tab-text"
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)
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with gr.Row():
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with gr.Column():
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image_input = gr.Image(
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label="Upload Image",
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type="numpy",
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elem_classes="gr-input"
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)
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image_button = gr.Button(
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"Analyze",
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elem_classes="gr-button"
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)
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with gr.Column():
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image_output = gr.Textbox(
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label="Analysis Results",
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elem_classes="gr-input"
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)
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image_thumbnail = gr.Image(
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label="Thumbnail",
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elem_classes="gr-input"
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)
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outputs=[image_output, image_thumbnail]
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)
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# Text Summary Tab
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with gr.TabItem("📝 Text Summary", elem_classes="gr-tab"):
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with gr.Column(elem_classes="gr-box"):
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gr.Markdown(
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"""
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### Text Summary App
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| 186 |
+
Enter text to generate a summary and basic statistics.
|
| 187 |
+
Works with both English and other languages.
|
| 188 |
""",
|
| 189 |
elem_classes="gr-tab-text"
|
| 190 |
)
|
| 191 |
text_input = gr.Textbox(
|
| 192 |
+
label="Enter Text",
|
| 193 |
+
placeholder="Enter the text you want to summarize...",
|
| 194 |
lines=5,
|
| 195 |
elem_classes="gr-input"
|
| 196 |
)
|
| 197 |
text_button = gr.Button(
|
| 198 |
+
"Summarize",
|
| 199 |
elem_classes="gr-button"
|
| 200 |
)
|
| 201 |
text_output = gr.Textbox(
|
| 202 |
+
label="Summary Results",
|
| 203 |
elem_classes="gr-input"
|
| 204 |
)
|
| 205 |
|
|
|
|
| 209 |
outputs=text_output
|
| 210 |
)
|
| 211 |
|
| 212 |
+
# Data Visualization Tab
|
| 213 |
+
with gr.TabItem("📊 Data Visualization", elem_classes="gr-tab"):
|
| 214 |
with gr.Column(elem_classes="gr-box"):
|
| 215 |
gr.Markdown(
|
| 216 |
"""
|
| 217 |
+
### Data Visualization Tool
|
| 218 |
+
Enter X and Y coordinates to generate a beautiful graph.
|
| 219 |
+
Separate numbers with commas.
|
| 220 |
|
| 221 |
+
Example:
|
| 222 |
+
- X coordinates: 1, 2, 3, 4, 5
|
| 223 |
+
- Y coordinates: 10, 20, 15, 25, 30
|
| 224 |
""",
|
| 225 |
elem_classes="gr-tab-text"
|
| 226 |
)
|
| 227 |
with gr.Row():
|
| 228 |
x_input = gr.Textbox(
|
| 229 |
+
label="X Coordinates",
|
| 230 |
+
placeholder="Example: 1, 2, 3, 4, 5",
|
| 231 |
value="1, 2, 3, 4, 5",
|
| 232 |
elem_classes="gr-input"
|
| 233 |
)
|
| 234 |
y_input = gr.Textbox(
|
| 235 |
+
label="Y Coordinates",
|
| 236 |
+
placeholder="Example: 10, 20, 30, 40, 50",
|
| 237 |
value="10, 20, 30, 40, 50",
|
| 238 |
elem_classes="gr-input"
|
| 239 |
)
|
| 240 |
|
| 241 |
plot_button = gr.Button(
|
| 242 |
+
"Generate Graph",
|
| 243 |
elem_classes="gr-button"
|
| 244 |
)
|
| 245 |
with gr.Row():
|
| 246 |
+
plot_output = gr.Plot(label="Graph")
|
| 247 |
plot_message = gr.Textbox(
|
| 248 |
+
label="Message",
|
| 249 |
elem_classes="gr-input"
|
| 250 |
)
|
| 251 |
|
|
|
|
| 255 |
outputs=[plot_output, plot_message]
|
| 256 |
)
|
| 257 |
|
| 258 |
+
# Project Information Tab
|
| 259 |
+
with gr.TabItem("ℹ️ Project Information", elem_classes="gr-tab"):
|
| 260 |
with gr.Column(elem_classes="gr-box"):
|
| 261 |
gr.Markdown(
|
| 262 |
"""
|
| 263 |
+
### About the Project
|
| 264 |
|
| 265 |
+
This portfolio site integrates multiple applications built using Python's powerful
|
| 266 |
+
Gradio library.
|
| 267 |
|
| 268 |
+
#### Features
|
| 269 |
+
1. **Image Classification App** 🖼️
|
| 270 |
+
- Upload and analyze images
|
| 271 |
+
- Display basic image information
|
| 272 |
+
- Thumbnail generation
|
| 273 |
|
| 274 |
+
2. **Text Summary App** 📚
|
| 275 |
+
- Basic text statistics
|
| 276 |
+
- Text summarization
|
| 277 |
+
- Multi-language support
|
| 278 |
|
| 279 |
+
3. **Data Visualization Tool** 📈
|
| 280 |
+
- Interactive graph generation
|
| 281 |
+
- Beautiful data visualization
|
| 282 |
+
- Customizable plots
|
| 283 |
|
| 284 |
+
#### Tech Stack
|
| 285 |
- Python 3.8+
|
| 286 |
- Gradio 4.0+
|
| 287 |
- Matplotlib 3.7+
|
| 288 |
- NumPy 1.24+
|
| 289 |
- Pillow 10.0+
|
| 290 |
|
| 291 |
+
#### Source Code
|
| 292 |
+
The source code for this project is available on [GitHub](https://github.com/yourusername/portfolio)
|
| 293 |
|
| 294 |
+
#### Contact
|
| 295 |
+
For questions or feedback, please reach out:
|
| 296 |
- Email: your.email@example.com
|
| 297 |
- Twitter: @yourusername
|
| 298 |
- LinkedIn: [Your Name](https://linkedin.com/in/yourprofile)
|
|
|
|
| 300 |
elem_classes="gr-tab-text"
|
| 301 |
)
|
| 302 |
|
| 303 |
+
# Launch application
|
| 304 |
if __name__ == "__main__":
|
| 305 |
portfolio.launch(share=True)
|