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app.py

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+import warnings
+import gradio as gr
+warnings.filterwarnings('ignore')
+
+from img_cap_utils import img_cap_with_markdown
+from img_gen_utils import img_gen_with_markdown
+
+# Combine both the app
+demo = gr.Blocks()
+with demo:
+    gr.TabbedInterface(
+        [img_cap_with_markdown, img_gen_with_markdown],
+        ['Image Captioning', 'Image Generation']
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()

img_cap_utils.py

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+from transformers import pipeline
+import io
+import base64
+import gradio as gr
+cap_model = pipeline(task = 'image-to-text', model = "Salesforce/blip-image-captioning-base")
+
+def image_to_base64_str(pil_image):
+    byte_arr = io.BytesIO()
+    pil_image.save(byte_arr, format = 'PNG')
+    byte_arr = byte_arr.getvalue()
+    return str(base64.b64encode(byte_arr).decode('utf-8'))
+
+def captioner(image):
+    base64_image = image_to_base64_str(image)
+    result = cap_model(base64_image)
+    return result[0]['generated_text']
+
+gr.close_all()
+image_captioning = gr.Interface(
+    fn = captioner,
+    inputs = [gr.Image(label = "Upload Image", type = 'pil')],
+    outputs = [gr.Textbox(label = 'Caption')],
+    allow_flagging = 'never'
+)
+
+# Add Markdown content
+markdown_content_img_cap = gr.Markdown(
+    """
+    <div style='text-align: center; font-family: "Times New Roman";'>
+        <h1 style='color: #FF6347;'>Caption Any Image Using the BLIP model</h1>
+        <h3 style='color: #4682B4;'>Model: Salesforce/blip-image-captioning-base</h3>
+        <h3 style='color: #32CD32;'>Made By: Md. Mahmudun Nabi</h3>
+    </div>
+    """
+)
+
+# Combine the Markdown content and the demo interface
+img_cap_with_markdown = gr.Blocks()
+with img_cap_with_markdown:
+    markdown_content_img_cap.render()
+    image_captioning.render()

img_gen_utils.py

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+import gradio as gr
+from diffusers import DiffusionPipeline
+
+# Load the pipeline
+gen_model = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+gen_model = gen_model.to('cpu')
+
+# Define the function
+def get_completion(prompt):
+    return gen_model(prompt).images[0]    
+
+# Create the Gradio interface
+img_gen = gr.Interface(
+    fn=get_completion,
+    inputs=gr.Textbox(lines=2, placeholder="Enter a prompt here..."),
+    outputs=gr.Image(type="pil"),
+)
+
+# Add Markdown content
+markdown_content_img_gen = gr.Markdown(
+    """
+    <div style='text-align: center; font-family: "Times New Roman";'>
+        <h1 style='color: #FF6347;'>Image Generation Using Stable Diffusion</h1>
+        <h3 style='color: #4682B4;'>Model: runwayml/stable-diffusion-v1-5</h3>
+        <h3 style='color: #32CD32;'>Made By: Md. Mahmudun Nabi</h3>
+    </div>
+    """
+)
+
+# Combine the Markdown content and the demo interface
+img_gen_with_markdown = gr.Blocks()
+with img_gen_with_markdown:
+    markdown_content_img_gen.render()
+    img_gen.render()

requirements.txt

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+gradio
+diffusers
+pybase64
+transformers