Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+
+image_class_pipe = pipeline(task="image-classification", model="google/vit-large-patch16-224")
+video_class_pipe = pipeline(task="video-classification", model="nateraw/videomae-base-finetuned-ucf101-subset")
+depth_estimator = pipeline(task="depth-estimation", model="Intel/dpt-large")
+image_caption = pipeline("image-to-text",model="Salesforce/blip-image-captioning-base")
+
+def classify_image_func(arr):
+  img = Image.fromarray(arr)
+  image_result = image_class_pipe(img)
+  return image_result[0]["label"]
+
+def classify_video_func(vid):
+  video_result = video_class_pipe(vid)
+  return video_result
+
+def estimate_depth_func(arr):
+  img = Image.fromarray(arr)
+  depth_result = depth_estimator(img)
+  return depth_result["depth"]
+
+def blip_captioning_func(arr):
+  img = Image.fromarray(arr)
+  image_caption_result = image_caption(img, max_new_tokens=500)
+  return image_caption_result[0]["generated_text"]
+
+with gr.Blocks() as demo:
+    gr.Markdown("# AI Methods")
+
+    with gr.Tab("Media Classification"):
+        gr.Markdown("# Image Classification")
+
+        with gr.Row():
+          classify_image_input = gr.Image(width=340, height=340)
+        with gr.Row():
+          classify_image_btn = gr.Button("Classify Image")
+          classify_image_output = gr.Textbox(label="Result")
+
+        classify_image_btn.click(fn=classify_image_func, inputs=[classify_image_input], outputs=[classify_image_output])
+
+        gr.Markdown("# Video Classification")
+
+        with gr.Row():
+          classify_video_input = gr.Video(width=340, height=340)
+        with gr.Row():
+          classify_video_btn = gr.Button("Classify Video")
+          classify_video_output = gr.Textbox(label="Result")
+
+        classify_video_btn.click(fn=classify_video_func, inputs=[classify_video_input], outputs=[classify_video_output])
+
+    with gr.Tab("Depth"):
+        gr.Markdown("# Depth Estimation")
+
+        with gr.Row():
+          depth_estimation_input = gr.Image(width=260, height=260)
+        with gr.Row():
+          depth_estimation_btn = gr.Button("Estimate Depth")
+        with gr.Row():
+          depth_estimation_output = gr.Image()
+        
+        depth_estimation_btn.click(fn=estimate_depth_func, inputs=[depth_estimation_input], outputs=[depth_estimation_output])
+
+    with gr.Tab("BLIP Captioning"):
+        gr.Markdown("# BLIP Captioning")
+
+        with gr.Row():
+          blip_input = gr.Image(width=260, height=260)
+        with gr.Row():
+          blip_btn = gr.Button("BLIP Caption")
+          blip_output = gr.Textbox(label="Caption")
+
+        blip_btn.click(fn=blip_captioning_func, inputs=[blip_input], outputs=[blip_output])
+
+demo.launch(debug=True)