Update app.py

app.py

@@ -2,7 +2,7 @@ import os
 import gradio as gr
 from openai import OpenAI
 
-# --- Initialize client securely ---
+# --- Initialize Hugging Face router client ---
 HF_TOKEN = os.getenv("HF_TOKEN")
 
 if not HF_TOKEN:
@@ -14,31 +14,56 @@ client = OpenAI(
 )
 
 # --- Chat handler ---
-def chat_with_model(message, history):
-    # Build messages list safely
-    messages = []
-
-    if history:
-        for msg in history:
-            # Handle both dict and tuple formats
-            if isinstance(msg, dict):
-                # Keep only allowed keys
-                messages.append({
-                    "role": msg.get("role", "user"),
-                    "content": msg.get("content", "")
-                })
-            elif isinstance(msg, (list, tuple)) and len(msg) == 2:
-                messages.append({"role": "user", "content": msg[0]})
-                messages.append({"role": "assistant", "content": msg[1]})
-
-    # Add latest user message
-    messages.append({"role": "user", "content": message})
+def generate_report(age, gender, height, weight, albumin, creatinine, glucose, crp, mcv, rdw, alp, wbc, lymphocytes, hb, pv):
+    # --- System Prompt ---
+    system = """You are an advanced Medical Insight Generation AI trained to analyze clinical biomarkers, urine analysis, and lab test results.
+Your goal is to generate a medically accurate, empathetic, and client-friendly health report in the following structured format:
+
+1. Executive Summary
+2. System-Specific Analysis
+3. Personalized Action Plan
+4. Interaction Alerts
+5. Longevity Metrics
+6. Tabular Mapping
+7. Enhanced AI Insight
+8. AI Insights & Longitudinal Risk Assessment
+9. Predictive Longevity Risk Profile
+10. Actionable Next Steps
+
+Maintain a professional, compassionate tone and explain medical reasoning in accessible language.
+"""
+
+    # --- User message ---
+    user_message = (
+        f"Patient Info:\n"
+        f"- Age: {age}\n"
+        f"- Gender: {gender}\n"
+        f"- Height: {height} cm\n"
+        f"- Weight: {weight} kg\n\n"
+        f"Biomarkers:\n"
+        f"- Albumin: {albumin} g/dL\n"
+        f"- Creatinine: {creatinine} mg/dL\n"
+        f"- Glucose: {glucose} mg/dL\n"
+        f"- CRP: {crp} mg/L\n"
+        f"- MCV: {mcv} fL\n"
+        f"- RDW: {rdw} %\n"
+        f"- ALP: {alp} U/L\n"
+        f"- WBC: {wbc} x10^3/μL\n"
+        f"- Lymphocytes: {lymphocytes} %\n"
+        f"- Hemoglobin: {hb} g/dL\n"
+        f"- Plasma (PV): {pv} mL\n"
+    )
 
     try:
+        # --- Send request to model ---
         response = client.chat.completions.create(
             model="openai/gpt-oss-120b:fireworks-ai",
-            messages=messages,
+            messages=[
+                {"role": "system", "content": system},
+                {"role": "user", "content": user_message},
+            ],
             temperature=0.7,
+            max_tokens=2000,
         )
         reply = response.choices[0].message.content
     except Exception as e:
@@ -46,14 +71,57 @@ def chat_with_model(message, history):
 
     return reply
 
-# --- Gradio Interface ---
-chatbot_ui = gr.ChatInterface(
-    fn=chat_with_model,
-    title="🧠 GPT-OSS 120B (Fireworks)",
-    description="Chat with the OSS 120B model hosted via Hugging Face router.",
-    examples=["Hello!", "Tell me a joke.", "Explain AI in simple terms."],
-    type="messages",  # required
-)
 
-if __name__ == "__main__":
-    chatbot_ui.launch()
+# --- Gradio UI ---
+with gr.Blocks(title="🧬 Biomarker Medical Insight Chatbot") as demo:
+    gr.Markdown(
+        """
+        ## 🧠 AI-Powered Biomarker Report Generator  
+        Enter the patient details and biomarkers below.  
+        The AI will generate a **comprehensive medical report** with insights, risk assessment, and personalized recommendations.
+        """
+    )
+
+    with gr.Row():
+        age = gr.Number(label="Age", value=45)
+        gender = gr.Radio(["Male", "Female"], label="Gender", value="Male")
+
+    with gr.Row():
+        height = gr.Number(label="Height (cm)", value=175)
+        weight = gr.Number(label="Weight (kg)", value=72)
+
+    gr.Markdown("### 🧫 Biomarker Inputs (Demo Values Pre-filled)")
+
+    with gr.Row():
+        albumin = gr.Number(label="Albumin (g/dL)", value=4.2)
+        creatinine = gr.Number(label="Creatinine (mg/dL)", value=1.1)
+        glucose = gr.Number(label="Glucose (mg/dL)", value=98)
+
+    with gr.Row():
+        crp = gr.Number(label="CRP (mg/L)", value=2.5)
+        mcv = gr.Number(label="MCV (fL)", value=90.5)
+        rdw = gr.Number(label="RDW (%)", value=13.2)
+
+    with gr.Row():
+        alp = gr.Number(label="ALP (U/L)", value=110)
+        wbc = gr.Number(label="WBC (x10^3/μL)", value=6.8)
+        lymphocytes = gr.Number(label="Lymphocytes (%)", value=35)
+
+    with gr.Row():
+        hb = gr.Number(label="Hemoglobin (g/dL)", value=14.5)
+        pv = gr.Number(label="Plasma (PV) (mL)", value=3000)
+
+    submit_btn = gr.Button("📤 Generate Medical Report")
+    output_box = gr.Textbox(label="AI-Generated Medical Report", lines=25)
+
+    submit_btn.click(
+        generate_report,
+        inputs=[
+            age, gender, height, weight,
+            albumin, creatinine, glucose, crp, mcv,
+            rdw, alp, wbc, lymphocytes, hb, pv
+        ],
+        outputs=output_box
+    )
+
+demo.launch()