initial check-in gradio vlm UI

app.py

@@ -0,0 +1,85 @@
+import gradio as gr
+import websockets
+import asyncio
+import json
+import base64
+from PIL import Image
+import io
+
+async def process_image_stream(image_path, prompt, max_tokens=512):
+    """
+    Process image with streaming response via WebSocket
+    """
+    if not image_path:
+        yield "Please upload an image first."
+        return
+    
+    try:
+        # Read and convert image to base64
+        with Image.open(image_path) as img:
+            img = img.convert('RGB')
+            buffer = io.BytesIO()
+            img.save(buffer, format="JPEG")
+            base64_image = base64.b64encode(buffer.getvalue()).decode('utf-8')
+            
+        # Connect to WebSocket
+        async with websockets.connect('wss://nexa-omni.nexa4ai.com/ws/process-image/') as websocket:
+            # Send image data and parameters as JSON
+            await websocket.send(json.dumps({
+                "image": f"data:image/jpeg;base64,{base64_image}",
+                "prompt": prompt,
+                "task": "instruct",  # Fixed to instruct
+                "max_tokens": max_tokens
+            }))
+            
+            # Initialize response
+            response = ""
+            
+            # Receive streaming response
+            async for message in websocket:
+                try:
+                    data = json.loads(message)
+                    if data["status"] == "generating":
+                        response += data["token"]
+                        yield response
+                    elif data["status"] == "complete":
+                        break
+                    elif data["status"] == "error":
+                        yield f"Error: {data['error']}"
+                        break
+                except json.JSONDecodeError:
+                    continue
+                
+    except Exception as e:
+        yield f"Error connecting to server: {str(e)}"
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=process_image_stream,
+    inputs=[
+        gr.Image(type="filepath", label="Upload Image"),
+        gr.Textbox(
+            label="Question", 
+            placeholder="Ask a question about the image...",
+            value="Describe this image"
+        ),
+        gr.Slider(
+            minimum=50,
+            maximum=200,
+            value=200,
+            step=1,
+            label="Max Tokens"
+        )
+    ],
+    outputs=gr.Textbox(label="Response", interactive=False),
+    title="Nexa Omni Vision",
+    description="""
+    Upload an image and ask questions about it. The model will analyze the image and provide detailed answers to your queries.
+    """,
+    examples=[
+        ["example_images/example_1.jpg", "Describe this image", 128],
+    ]
+)
+
+if __name__ == "__main__":
+    demo.queue().launch(server_name="0.0.0.0", server_port=7860)