Create app.py

app.py

@@ -0,0 +1,57 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load MedGemma
+MODEL_NAME = "google/medgemma-4b-it"  # choose the model size you want
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="auto")  # GPU if available
+
+# Function to generate response
+def medgemma_chat(prompt, max_length=200):
+    """
+    Generates medical responses from MedGemma.
+    
+    Args:
+        prompt (str): Medical question, lab results, or patient info.
+        max_length (int): Max number of tokens to generate.
+    
+    Returns:
+        str: AI-generated response.
+    """
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        output = model.generate(
+            **inputs,
+            max_length=max_length,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9
+        )
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    return response
+
+# Gradio UI
+iface = gr.Interface(
+    fn=medgemma_chat,
+    inputs=[
+        gr.Textbox(lines=5, placeholder="Enter patient's info, lab results, or medical question here...", label="Input")
+    ],
+    outputs=[
+        gr.Textbox(label="MedGemma Response")
+    ],
+    title="MedGemma Medical Assistant",
+    description=(
+        "Ask questions or provide patient information. "
+        "MedGemma generates medical insights, summaries, and guidance. "
+        "⚠️ For educational and research purposes only — not a substitute for professional medical advice."
+    ),
+    examples=[
+        ["Patient: 45-year-old male, BMI 28, blood pressure 140/90, glucose 7.5 mmol/L. Suggest possible conditions."],
+        ["Summarize the following lab report: Hemoglobin 11 g/dL, WBC 9 x10^9/L, Platelets 200 x10^9/L."]
+    ],
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    iface.launch()