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3ead2fc 0348ef8 3ead2fc 0348ef8 3ead2fc 0348ef8 3ead2fc 0348ef8 9c09240 3ead2fc 0348ef8 3ead2fc | 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 | import gradio as gr
import torch
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
from PIL import Image
import json
import os
MODEL_ID = "Qwen/Qwen2-VL-2B-Instruct"
ADAPTER_ID = "hssling/derm-analyzer-adapter"
print("Starting App Engine...")
device = "cuda" if torch.cuda.is_available() else "cpu"
token = os.environ.get("HF_TOKEN")
processor = AutoProcessor.from_pretrained(MODEL_ID, token=token)
model = Qwen2VLForConditionalGeneration.from_pretrained(
MODEL_ID,
torch_dtype=torch.float16 if device == "cuda" else torch.float32,
device_map="auto",
token=token
)
if ADAPTER_ID:
print(f"Loading custom fine-tuned LoRA weights: {ADAPTER_ID}")
try:
model.load_adapter(ADAPTER_ID, token=token)
print("✅ Adapter loaded successfully over the base Qwen2-VL engine.")
except Exception as e:
print(f"Failed to load adapter. Using base model. Error: {e}")
def diagnose_skin(image, clinical_notes, temp, max_tokens):
try:
if image is None:
return json.dumps({"error": "No image provided."})
system_prompt = "You are DermaAI, an expert Dermatologist trained extensively on Indian skin types (Fitzpatrick IV-VI) and tropical diseases. Analyze the skin lesion and output a structured clinical report including Findings, Differential Diagnosis, and recommended Indian Pharmacological Management."
user_prompt = f"Clinical Context: {clinical_notes}\nAnalyze this dermatological image and describe the medical findings, providing treatment and management advice."
messages = [
{"role": "system", "content": system_prompt},
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": user_prompt}
]
}
]
text_input = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(
text=[text_input],
images=[image],
padding=True,
return_tensors="pt"
).to(device)
with torch.no_grad():
generated_ids = model.generate(**inputs, max_new_tokens=int(max_tokens), temperature=float(temp), top_p=0.9, do_sample=True)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
return output_text
except Exception as e:
return f"Error: {str(e)}"
demo = gr.Interface(
fn=diagnose_skin,
inputs=[
gr.Image(type="pil", label="Skin Image"),
gr.Textbox(label="Clinical Context", value="No additional clinical context provided."),
gr.Slider(minimum=0.0, maximum=1.0, value=0.4, step=0.1, label="Temperature"),
gr.Slider(minimum=256, maximum=4096, value=2000, step=256, label="Max Tokens")
],
outputs=gr.Markdown(label="Clinical Report Output"),
title="DermaAI API (Indian Context)",
description="Fine-tuned Medical LLM for Dermatology, focused on Fitzpatrick Skin Types IV-VI."
)
if __name__ == "__main__":
demo.launch()
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