Update app.py

app.py

@@ -9,7 +9,7 @@ from typing import Dict, Any, Union, List
 
 
 # ---------------- Initialize ----------------
-load_dotenv()
+
 app = FastAPI(title="LLM Model API + Gradio UI", version="4.0")
 
 GEMINI_API_KEY='AIzaSyC0XU6yLCILZFUVhKoIcqoy2k5qwQmnDsc'
@@ -39,7 +39,7 @@ class BiomarkerRequest(BaseModel):
     weight: float = Field(default=70)
 
 
-# ---------------- Utilities ----------------
+# ---------------- Utility ----------------
 def clean_json(data: Union[Dict, List, str]) -> Union[Dict, List, str]:
     if isinstance(data, str):
         text = re.sub(r"-{3,}", "", data)
@@ -53,85 +53,7 @@ def clean_json(data: Union[Dict, List, str]) -> Union[Dict, List, str]:
     return data
 
 
-# ---------------- Parser ----------------
-def parse_medical_report(text: str):
-    def clean_line(line: str) -> str:
-        return re.sub(r"[\-\*\u2022]+\s*", "", line.strip())
-
-    def parse_bold_entities(block: str) -> Dict[str, str]:
-        entities = {}
-        pattern = re.compile(r"\*\*(.*?)\*\*(.*?)(?=\*\*|###|$)", re.S)
-        for match in pattern.finditer(block):
-            key = match.group(1).strip().strip(":")
-            val = match.group(2).strip().replace("\n", " ")
-            val = re.sub(r"\s+", " ", val)
-            if key:
-                entities[key] = val
-        return entities
-
-    data = {
-        "executive_summary": {"top_priorities": [], "key_strengths": []},
-        "system_analysis": {},
-        "personalized_action_plan": {},
-        "interaction_alerts": [],
-        "normal_ranges": {},
-        "biomarker_table": []
-    }
-
-    exec_match = re.search(r"###\s*Executive Summary(.*?)(?=###|$)", text, re.S | re.I)
-    if exec_match:
-        block = exec_match.group(1)
-        priorities = re.findall(r"\d+\.\s*(.*?)\n", block)
-        if priorities:
-            data["executive_summary"]["top_priorities"] = [clean_line(p) for p in priorities]
-        strengths_match = re.search(r"\*\*Key Strengths:\*\*(.*)", block, re.S)
-        if strengths_match:
-            strengths_text = strengths_match.group(1)
-            strengths = [clean_line(s) for s in strengths_text.splitlines() if clean_line(s)]
-            data["executive_summary"]["key_strengths"] = strengths
-
-    sys_match = re.search(r"###\s*System[- ]Specific Analysis(.*?)(?=###|$)", text, re.S | re.I)
-    if sys_match:
-        sys_block = sys_match.group(1)
-        data["system_analysis"] = parse_bold_entities(sys_block)
-
-    plan_match = re.search(r"###\s*Personalized Action Plan(.*?)(?=###|$)", text, re.S | re.I)
-    if plan_match:
-        plan_block = plan_match.group(1)
-        data["personalized_action_plan"] = parse_bold_entities(plan_block)
-
-    alerts_match = re.search(r"###\s*Interaction Alerts(.*?)(?=###|$)", text, re.S | re.I)
-    if alerts_match:
-        alerts_block = alerts_match.group(1)
-        alerts = [clean_line(a) for a in alerts_block.splitlines() if clean_line(a)]
-        data["interaction_alerts"] = alerts
-
-    normal_match = re.search(r"###\s*Normal Ranges(.*?)(?=###|$)", text, re.S | re.I)
-    if normal_match:
-        normal_block = normal_match.group(1)
-        for match in re.findall(r"-\s*([^:]+):\s*([^\n]+)", normal_block):
-            biomarker, rng = match
-            data["normal_ranges"][biomarker.strip()] = rng.strip()
-
-    table_match = re.search(r"###\s*Tabular Mapping(.*)", text, re.S | re.I)
-    if table_match:
-        table_block = table_match.group(1)
-        table_pattern = r"\|\s*([^|]+)\s*\|\s*([^|]+)\s*\|\s*([^|]+)\s*\|\s*([^|]+)\s*\|\s*([^|]+)\s*\|"
-        for biomarker, value, status, insight, ref in re.findall(table_pattern, table_block):
-            if not any([biomarker, value, status, insight, ref]):
-                continue
-            data["biomarker_table"].append({
-                "biomarker": biomarker.strip(),
-                "value": value.strip(),
-                "status": status.strip(),
-                "insight": insight.strip(),
-                "reference_range": ref.strip(),
-            })
-
-    return data
-
-
-# ---------------- Prediction Core ----------------
+# ---------------- Core Gemini Logic ----------------
 def generate_report(data: BiomarkerRequest) -> str:
     """Main logic — uses Gemini to generate markdown medical report"""
     prompt = """
@@ -212,7 +134,7 @@ Biomarkers:
 - WBC: {data.wbc} ×10^3/μL
 - Lymphocytes: {data.lymphocytes} %
 - Hemoglobin: {data.hb} g/dL
-- Plasma Volume (PV): {data.pv} mL
+- Plasma Volume (PV): {data.pv} L
 """
 
     model = genai.GenerativeModel(MODEL_ID)
@@ -224,7 +146,7 @@ Biomarkers:
     return response.text.strip()
 
 
-# ---------------- Gradio Interface ----------------
+# ---------------- Gradio Function ----------------
 def gradio_interface(albumin, creatinine, glucose, crp, mcv, rdw, alp, wbc,
                      lymphocytes, hb, pv, age, gender, height, weight):
     req = BiomarkerRequest(
@@ -232,35 +154,48 @@ def gradio_interface(albumin, creatinine, glucose, crp, mcv, rdw, alp, wbc,
         mcv=mcv, rdw=rdw, alp=alp, wbc=wbc, lymphocytes=lymphocytes,
         hb=hb, pv=pv, age=int(age), gender=gender, height=height, weight=weight
     )
-    report = generate_report(req)
-    return report
-
-
-iface = gr.Interface(
-    fn=gradio_interface,
-    inputs=[
-        gr.Number(label="Albumin (g/dL)", value=3.2),
-        gr.Number(label="Creatinine (mg/dL)", value=1.4),
-        gr.Number(label="Glucose (mg/dL)", value=145),
-        gr.Number(label="CRP (mg/L)", value=12.0),
-        gr.Number(label="MCV (fL)", value=88),
-        gr.Number(label="RDW (%)", value=15.5),
-        gr.Number(label="ALP (U/L)", value=120),
-        gr.Number(label="WBC (×10³/μL)", value=11.8),
-        gr.Number(label="Lymphocytes (%)", value=20),
-        gr.Number(label="Hemoglobin (g/dL)", value=13.0),
-        gr.Number(label="Plasma Volume (L)", value=2.1),
-        gr.Number(label="Age (years)", value=52),
-        gr.Radio(["male", "female"], label="Gender", value="female"),
-        gr.Number(label="Height (cm)", value=165),
-        gr.Number(label="Weight (kg)", value=70)
-    ],
-    outputs=gr.Markdown(label="🩺 AI Medical Report"),
-    title="LLM Biomarker Analyzer",
-    description="Enter your biomarker and demographic data to generate a detailed AI-based medical report (Gemini-powered).",
-    theme="soft",
-    allow_flagging="never"
-)
+    return generate_report(req)
+
+
+# ---------------- Gradio UI (Vertical Layout) ----------------
+with gr.Blocks(theme="soft", title="LLM Biomarker Analyzer") as iface:
+    gr.Markdown("## 🧬 LLM Biomarker Analyzer")
+    gr.Markdown("Enter your biomarker and demographic data below to generate a **Gemini-powered medical insight report**:")
+
+    with gr.Column():
+        with gr.Row():
+            age = gr.Number(label="Age (years)", value=52)
+            gender = gr.Radio(["male", "female"], label="Gender", value="female")
+
+        with gr.Row():
+            height = gr.Number(label="Height (cm)", value=165)
+            weight = gr.Number(label="Weight (kg)", value=70)
+
+        gr.Markdown("### 🔬 Biomarker Values")
+
+        grid_inputs = [
+            gr.Number(label="Albumin (g/dL)", value=3.2),
+            gr.Number(label="Creatinine (mg/dL)", value=1.4),
+            gr.Number(label="Glucose (mg/dL)", value=145),
+            gr.Number(label="CRP (mg/L)", value=12.0),
+            gr.Number(label="MCV (fL)", value=88),
+            gr.Number(label="RDW (%)", value=15.5),
+            gr.Number(label="ALP (U/L)", value=120),
+            gr.Number(label="WBC (×10³/μL)", value=11.8),
+            gr.Number(label="Lymphocytes (%)", value=20),
+            gr.Number(label="Hemoglobin (g/dL)", value=13.0),
+            gr.Number(label="Plasma Volume (L)", value=2.1)
+        ]
+
+        submit_btn = gr.Button("🧠 Generate Medical Report", variant="primary")
+
+    output_md = gr.Markdown(label="AI-Generated Medical Report")
+
+    submit_btn.click(
+        fn=gradio_interface,
+        inputs=grid_inputs + [age, gender, height, weight],
+        outputs=output_md
+    )
 
 # ---------------- Launch ----------------
 if __name__ == "__main__":