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app.py

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+import gradio as gr
+import torch
+from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
+from PIL import Image
+import json
+
+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"
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+    device_map="auto"
+)
+
+if ADAPTER_ID:
+    print(f"Loading custom fine-tuned LoRA weights: {ADAPTER_ID}")
+    try:
+        model.load_adapter(ADAPTER_ID)
+    except Exception as e:
+        print(f"Failed to load adapter. Using base model. Error: {e}")
+
+def diagnose_skin(image: Image.Image = None, clinical_notes: str = '', temp: float = 0.4, max_tokens: int = 2000):
+    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()