GATE_Motion_Analysis / app_minimal.py
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#!/usr/bin/env python3
"""
GATE Motion Analysis - Minimal Deployment Version
Simplified to avoid all API-related issues
"""
import os
import gradio as gr
import numpy as np
def simple_analysis(image, exercise):
"""Simple analysis function that returns mock results."""
if image is None:
return None, "No image provided", 0, "Please upload an image"
# Mock analysis
confidence = np.random.uniform(70, 95)
status = f"Analysis complete for {exercise}"
feedback = f"Mock analysis result for {exercise}. Confidence: {confidence:.1f}%"
return image, status, confidence, feedback
def create_minimal_interface():
"""Create a minimal interface without complex features."""
with gr.Blocks(
title="GATE Motion Analysis - Minimal",
analytics_enabled=False
) as interface:
gr.Markdown("# GATE Motion Analysis - Minimal Version")
gr.Markdown("Upload an image and click analyze to test the system.")
with gr.Row():
with gr.Column():
image_input = gr.Image(label="Upload Image", type="pil")
exercise_input = gr.Dropdown(
choices=["Squats", "Push-ups", "Lunges"],
value="Squats",
label="Exercise"
)
analyze_btn = gr.Button("Analyze", variant="primary")
with gr.Column():
result_image = gr.Image(label="Result")
status_output = gr.Textbox(label="Status", interactive=False)
confidence_output = gr.Number(label="Confidence", interactive=False)
feedback_output = gr.Textbox(label="Feedback", interactive=False)
# Simple click handler
analyze_btn.click(
fn=simple_analysis,
inputs=[image_input, exercise_input],
outputs=[result_image, status_output, confidence_output, feedback_output]
)
return interface
if __name__ == "__main__":
print("πŸš€ Starting Minimal GATE Motion Analysis...")
# Create and launch with absolute minimal configuration
interface = create_minimal_interface()
interface.launch(
share=False,
show_api=False,
show_error=True
)