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import gradio as gr
from openai import OpenAI
import base64
from PIL import Image
import io
from datetime import datetime
# OpenAI client setup
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key='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'
)
def analyze_image(image, prompt):
if image is None:
return "Please upload or capture an image first."
# Convert image to base64
buffered = io.BytesIO()
image.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
try:
response = client.chat.completions.create(
model="opengvlab/internvl3-14b:free",
messages=[
{
"role": "system",
"content": """You are Dalton, an expert AI assistant specialized in image understanding.
Your tasks include:
- Extracting and structuring text from images
- Answering questions about image content
- Providing detailed descriptions
- Analyzing receipts, documents, and other visual content
Be thorough, accurate, and helpful in your responses."""
},
{
"role": "user",
"content": [
{"type": "text", "text": prompt},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{img_str}"
}
}
]
}
],
max_tokens=2048
)
result = response.choices[0].message.content
return result
except Exception as e:
return f"An error occurred: {str(e)}"
# Custom CSS for better mobile experience
css = """
#mobile-camera { width: 100% !important; }
#prompt-textbox { min-height: 100px !important; }
.result-box {
max-height: 500px;
overflow-y: auto;
padding: 15px;
border: 1px solid #e0e0e0;
border-radius: 8px;
}
.footer {
margin-top: 20px;
font-size: 12px;
color: #666;
text-align: center;
}
"""
with gr.Blocks(css=css, title="DaltonVision - Koshur AI") as demo:
gr.Markdown("""
# 🧾 DaltonVision - InternVL3-14B
### Advanced Image Understanding • Powered by OpenRouter • Developed by [Koshur AI](https://koshurai.com)
""")
with gr.Row():
with gr.Column():
# Image input section
image_input = gr.Image(
sources=["upload", "webcam"],
type="pil",
label="Upload or Capture Image",
elem_id="mobile-camera"
)
# Prompt input
prompt_input = gr.Textbox(
label="📝 Enter your question or instruction",
value="Extract all content structurally",
lines=3,
elem_id="prompt-textbox"
)
submit_btn = gr.Button("🔍 Analyze Image", variant="primary")
gr.Examples(
examples=[
["What is the total amount on this receipt?"],
["List all items and their prices"],
["Who is the vendor and what is the date?"],
["Describe this image in detail"]
],
inputs=[prompt_input],
label="💡 Try these example prompts:"
)
with gr.Column():
# Result output
result_output = gr.Markdown(
label="✅ Analysis Result",
elem_classes="result-box"
)
# Footer
gr.Markdown("""
<div class="footer">
© 2025 Koshur AI. All rights reserved.<br>
Note: Images are processed in real-time and not stored.
</div>
""")
# Button action
submit_btn.click(
fn=analyze_image,
inputs=[image_input, prompt_input],
outputs=result_output
)
# Launch the app
if __name__ == "__main__":
demo.launch() |