Spaces:
Runtime error
Runtime error
File size: 4,419 Bytes
fe36943 |
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 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
import gradio as gr
from transformers import AutoImageProcessor, AutoModelForImageClassification, AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import torch
import json
class SinogramAnalysisSystem:
def __init__(self):
print("Initializing system...")
self.device = "cuda" if torch.cuda.is_available() else "cpu"
# Load analysis models
print("Loading tumor detection models...")
self.tumor_classifier = AutoModelForImageClassification.from_pretrained(
"SIATCN/vit_tumor_classifier"
).to(self.device)
self.tumor_processor = AutoImageProcessor.from_pretrained("SIATCN/vit_tumor_classifier")
self.size_classifier = AutoModelForImageClassification.from_pretrained(
"SIATCN/vit_tumor_radius_detection_finetuned"
).to(self.device)
self.size_processor = AutoImageProcessor.from_pretrained(
"SIATCN/vit_tumor_radius_detection_finetuned"
)
# Load Hymba model
print("Loading Hymba model...")
repo_name = "nvidia/Hymba-1.5B-Base"
self.tokenizer = AutoTokenizer.from_pretrained(repo_name, trust_remote_code=True)
self.llm = AutoModelForCausalLM.from_pretrained(repo_name, trust_remote_code=True)
self.llm = self.llm.to(self.device).to(torch.bfloat16)
print("System ready!")
def process_sinogram(self, image):
if isinstance(image, str):
image = Image.open(image)
if image.mode != 'RGB':
image = image.convert('RGB')
return image.resize((224, 224))
@torch.no_grad()
def analyze_sinogram(self, processed_image):
# Detect tumor
inputs = self.tumor_processor(processed_image, return_tensors="pt").to(self.device)
outputs = self.tumor_classifier(**inputs)
tumor_present = outputs.logits.softmax(dim=-1)[0].cpu()
has_tumor = tumor_present[1] > tumor_present[0]
# Assess size
size_inputs = self.size_processor(processed_image, return_tensors="pt").to(self.device)
size_outputs = self.size_classifier(**size_inputs)
size_pred = size_outputs.logits.softmax(dim=-1)[0].cpu()
sizes = ["no-tumor", "0.5", "1.0", "1.5"]
tumor_size = sizes[size_pred.argmax().item()]
return has_tumor, tumor_size
def generate_report(self, tumor_present, tumor_size):
prompt = f"""As a medical professional, provide a brief analysis of these sinogram findings:
Findings:
- Tumor Detection: {'Positive' if tumor_present else 'Negative'}
- Tumor Size: {tumor_size} cm
Please provide:
1. Brief interpretation
2. Clinical recommendations
3. Follow-up plan"""
# Generate response using Hymba
inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
outputs = self.llm.generate(
**inputs,
max_length=512,
do_sample=True,
temperature=0.7,
use_cache=True
)
response = self.tokenizer.decode(
outputs[0][inputs['input_ids'].shape[1]:],
skip_special_tokens=True
)
return response.strip()
def analyze_image(self, image):
try:
# Process sinogram
processed = self.process_sinogram(image)
tumor_present, tumor_size = self.analyze_sinogram(processed)
# Generate medical report
report = self.generate_report(tumor_present, tumor_size)
# Format results
return f"""
SINOGRAM ANALYSIS:
• Tumor Detection: {'Positive' if tumor_present else 'Negative'}
• Size Assessment: {tumor_size} cm
MEDICAL REPORT:
{report}
"""
except Exception as e:
return f"Error during analysis: {str(e)}"
def create_interface():
system = SinogramAnalysisSystem()
iface = gr.Interface(
fn=system.analyze_image,
inputs=[
gr.Image(type="pil", label="Upload Sinogram")
],
outputs=[
gr.Textbox(label="Analysis Results", lines=15)
],
title="Sinogram Analysis System",
description="Upload a sinogram for tumor detection and medical assessment."
)
return iface
if __name__ == "__main__":
interface = create_interface()
interface.launch(debug=True, share=True) |