Update app.py

app.py

@@ -7,7 +7,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 MODEL_ID = "Muhammadidrees/my-gpt-oss"
 
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
-model = AutoModelForCausalLM.from_pretrained(MODEL_ID, device_map="auto")
+model = AutoModelForCausalLM.from_pretrained(MODEL_ID)  # works on CPU & GPU
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
 
@@ -25,7 +25,6 @@ def analyze(
     except Exception:
         bmi = "N/A"
 
-    # Fixed Instruction (no estimation, structured format only)
     system_prompt = """
 You are a professional AI Medical Assistant.
 You are analyzing patient demographics and Levine biomarker panel values.
@@ -60,7 +59,6 @@ Output MUST strictly follow this structured format:
    - Predictive longevity risk profile
 """
 
-    # Patient Data
     patient_input = f"""
 Patient Profile:
 - Age: {age}
@@ -83,25 +81,21 @@ Lab Values:
 
     prompt = system_prompt + "\n" + patient_input
 
-    # Call LLM
     result = pipe(
         prompt,
-        max_new_tokens=2000,
+        max_new_tokens=1000,
         do_sample=True,
         temperature=0.3,
-        top_p=0.9,
-        return_full_text=False
+        top_p=0.9
     )
 
     text = result[0]["generated_text"].strip()
 
-    # Split into left (sections 1–3) and right (sections 5–6)
-    left_sections = []
-    right_sections = []
+    # Robust split into left (1–4) and right (5–6)
+    left_sections, right_sections = [], []
     capture_left = True
-
     for line in text.splitlines():
-        if line.strip().startswith("5. Tabular Mapping"):
+        if "Tabular Mapping" in line:
             capture_left = False
         if capture_left:
             left_sections.append(line)
@@ -124,23 +118,23 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     with gr.Row():
         with gr.Column(scale=1):
             gr.Markdown("### 👤 Demographics")
-            age = gr.Number(label="Age", value=30)
+            age = gr.Number(label="Age", value=45)
             gender = gr.Dropdown(["Male", "Female"], label="Gender", value="Male")
             height = gr.Number(label="Height (cm)", value=174)
             weight = gr.Number(label="Weight (kg)", value=75)
             
             gr.Markdown("### 🩸 Blood Panel")
-            wbc = gr.Number(label="WBC (K/uL)", value=1.0)
-            lymph = gr.Number(label="Lymphocytes (%)", value=2)
+            wbc = gr.Number(label="WBC (K/uL)", value=6.5)
+            lymph = gr.Number(label="Lymphocytes (%)", value=30)
             mcv = gr.Number(label="MCV (fL)", value=88)
             rdw = gr.Number(label="RDW (%)", value=13)
         
         with gr.Column(scale=1):
             gr.Markdown("### 🧬 Chemistry Panel")
-            albumin = gr.Number(label="Albumin (g/dL)", value=3.0)
+            albumin = gr.Number(label="Albumin (g/dL)", value=4.2)
             creatinine = gr.Number(label="Creatinine (mg/dL)", value=0.9)
-            glucose = gr.Number(label="Glucose (mg/dL)", value=90)
-            crp = gr.Number(label="CRP (mg/L)", value=0.5)
+            glucose = gr.Number(label="Glucose (mg/dL)", value=92)
+            crp = gr.Number(label="CRP (mg/L)", value=1.0)
             alp = gr.Number(label="ALP (U/L)", value=70)
             
             analyze_btn = gr.Button("🔬 Generate Report", variant="primary")
@@ -164,5 +158,6 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         "*⚠️ Disclaimer: This AI output is for educational purposes only and not a substitute for professional medical advice.*"
     )
 
-# Launch app
-demo.launch()
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)