File size: 2,065 Bytes
6eaa671 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | #!/usr/bin/env python3
"""
Vision AI - Detailed Image Analysis
"""
import gradio as gr
from transformers import pipeline
from PIL import Image
print("Loading vision model...")
# Advanced captioning model for detailed descriptions
vision_pipeline = pipeline(
"image-to-text",
model="Salesforce/blip-image-captioning-large",
device=-1 # CPU
)
print("✓ Model loaded!")
def analyze_image_detailed(image, prompt=""):
"""Analyze image with detailed captioning"""
if image is None:
return "Please upload an image first"
try:
if isinstance(image, str):
image = Image.open(image)
image.thumbnail((512, 512))
# Generate detailed caption
result = vision_pipeline(image)
caption = result[0]["generated_text"] if result else "No output"
return caption
except Exception as e:
return f"Error: {str(e)}"
# Create Gradio interface
with gr.Blocks(title="Vision AI - Detailed Analysis", theme=gr.themes.Soft()) as demo:
gr.Markdown("""
# 🖼️ Vision AI - Advanced Image Analysis
Get detailed analysis of your images using advanced AI.
- **Model**: BLIP Large (Salesforce)
- **Processing**: 100% local (no cloud)
- **Analysis**: Comprehensive image descriptions
""")
with gr.Row():
with gr.Column():
gr.Markdown("### Upload Image")
image_input = gr.Image(label="Select Image", type="pil")
analyze_btn = gr.Button("🔍 Analyze Image", size="lg", variant="primary")
with gr.Column():
gr.Markdown("### Detailed Analysis")
output = gr.Textbox(
label="Image Description",
lines=8,
interactive=False
)
analyze_btn.click(
fn=analyze_image_detailed,
inputs=image_input,
outputs=output
)
if __name__ == "__main__":
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=True
)
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