Update app.py

app.py

@@ -1,59 +1,117 @@
 import os
 import gradio as gr
-from openai import OpenAI
-
-# --- Initialize client securely ---
-HF_TOKEN = os.getenv("HF_TOKEN")
-
-if not HF_TOKEN:
-    raise ValueError("❌ HF_TOKEN not found. Please set it in your Hugging Face Space secrets.")
-
-client = OpenAI(
-    base_url="https://router.huggingface.co/v1",
-    api_key=HF_TOKEN,
-)
-
-# --- 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})
+import google.generativeai as genai
+from dotenv import load_dotenv
 
+# --- Load API Key ---
+load_dotenv()
+api_key = os.getenv("AIzaSyB1l7od1mPJ3CdN1AVF509xtHx1unYqnww")
+
+if not api_key:
+    raise ValueError("❌ GEMINI_API_KEY not found. Please set it in your .env or Hugging Face secrets.")
+
+# --- Configure Gemini ---
+genai.configure(api_key=api_key)
+MODEL_ID = "gemini-1.5-pro"  # or gemini-1.5-flash for faster inference
+
+# ---------------- AI Response Function ----------------
+def respond(albumin, creatinine, glucose, crp, mcv, rdw, alp, wbc, lymphocytes, age, gender, height, weight):
+    system_message = (
+        "You are an AI health assistant that only analyzes lab reports based on the given Levine Biomarkers "
+        "and generates clear, structured, and patient-friendly summaries.\n"
+        "Your role is to transform raw lab values into a structured medical report with actionable insights "
+        "but never recommend medicine and never calculate anything else.\n"
+        "Follow this exact output format:\n\n"
+        "Tabular Mapping\n"
+        "- This section must always include a Markdown table.\n"
+        "- The table must contain exactly four columns:\n"
+        "| Biomarker | Value | Status (Low/Normal/High) | AI-Inferred Insight |\n"
+        "- Include ALL 9 Levine biomarkers (Albumin, Creatinine, Glucose, CRP, MCV, RDW, ALP, WBC, Lymphocytes).\n"
+        "- The first row after the header must begin directly with 'Albumin'.\n"
+        "- Do NOT add any index numbers or empty rows.\n"
+        "- Each biomarker must appear exactly once as a separate row.\n\n"
+        "Executive Summary\n"
+        "- List Top 3 Priorities.\n"
+        "- Highlight Key Strengths.\n\n"
+        "System-Specific Analysis\n"
+        "- Status: “Optimal” | “Monitor” | “Needs Attention”.\n"
+        "- Write a 2–3 sentence explanation in plain language.\n\n"
+        "Personalized Action Plan\n"
+        "- Nutrition, Lifestyle, Medical, Testing.\n\n"
+        "Interaction Alerts\n"
+        "- Note possible interactions between lab markers.\n\n"
+        "Constraints:\n"
+        "- Never provide direct diagnosis, prescriptions, or medical treatment.\n"
+        "- Never give anything that isn't present in the input.\n"
+        "- Always recommend consulting a doctor.\n"
+        "- Don't show input in output.\n"
+        "- Also give normal reference ranges.\n"
+        "- Keep the language simple, clear, and supportive."
+    )
+
+    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} %"
+    )
+
+    # --- Generate response ---
     try:
-        response = client.chat.completions.create(
-            model="openai/gpt-oss-120b:fireworks-ai",
-            messages=messages,
-            temperature=0.7,
+        model = genai.GenerativeModel(MODEL_ID)
+        response = model.generate_content(
+            [system_message, user_message],
+            generation_config=genai.GenerationConfig(
+                temperature=0.2,
+                top_p=0.9,
+                max_output_tokens=2000
+            ),
         )
-        reply = response.choices[0].message.content
+        return response.text.strip()
     except Exception as e:
-        reply = f"⚠️ Error: {str(e)}"
+        return f"⚠️ Error: {str(e)}"
+
+# ---------------- Gradio UI ----------------
+with gr.Blocks() as demo:
+    gr.Markdown("## 🧪 AI Health Assistant (Levine Biomarkers via Google Gemini 1.5 Pro)")
+
+    with gr.Row():
+        with gr.Column():
+            albumin = gr.Textbox(label="Albumin (g/dL)", value="4.5")
+            creatinine = gr.Textbox(label="Creatinine (mg/dL)", value="1.5")
+            glucose = gr.Textbox(label="Glucose (mg/dL, fasting)", value="160")
+            crp = gr.Textbox(label="CRP (mg/L)", value="2.5")
+            mcv = gr.Textbox(label="MCV (fL)", value="150")
+            rdw = gr.Textbox(label="RDW (%)", value="15")
+            alp = gr.Textbox(label="ALP (U/L)", value="146")
+            wbc = gr.Textbox(label="WBC (10^3/μL)", value="10.5")
+            lymphocytes = gr.Textbox(label="Lymphocytes (%)", value="38")
+
+        with gr.Column():
+            age = gr.Textbox(label="Age (years)", value="30")
+            gender = gr.Dropdown(choices=["Male", "Female"], label="Gender", value="Male")
+            height = gr.Textbox(label="Height (cm)", value="170")
+            weight = gr.Textbox(label="Weight (kg)", value="60")
 
-    return reply
+    output = gr.Textbox(label="AI Health Report", lines=30)
+    btn = gr.Button("Generate Report")
 
-# --- 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
-)
+    btn.click(
+        respond,
+        inputs=[albumin, creatinine, glucose, crp, mcv, rdw, alp, wbc, lymphocytes, age, gender, height, weight],
+        outputs=output
+    )
 
 if __name__ == "__main__":
-    chatbot_ui.launch()
+    demo.launch()