Update app.py

app.py

@@ -3,6 +3,7 @@ from transformers import pipeline
 from huggingface_hub import login
 from openai import OpenAI
 import os
+import json
 
 # --- 1. SETUP ---
 hf_token = os.getenv("HF_TOKEN")
@@ -32,37 +33,53 @@ def detect_depression_risk(text: str) -> dict:
     """Analyzes text using Mental-Longformer (eRisk 2025)."""
     
     # --- THESIS LOGIC: AGGREGATION ---
-    # Strictly preserving your requirement for double newlines
-    processed_text = text.replace("\n", "\n\n")
+    # 1. We replace single newlines with double newlines for the MODEL
+    processed_text = text.replace("\n", "\n\n") 
     
     results = classifier(processed_text)[0]
     prob = next((r['score'] for r in results if r['label'] == 'LABEL_1'), 0)
     
+    # Thesis Thresholds (Figure 4.15)
     if prob < 0.40: 
-        level = "Low Risk (Isolated Pattern)"
+        level = "Low Risk"
+        biomarker = "Healthy External Focus"
+        desc = "Matches 'Isolated Control' group. High lexical diversity, focus on hobbies/events."
+        color = "#10b981" # Green
     elif 0.40 <= prob < 0.60: 
-        level = "Moderate Risk (Interactive Echo Chamber Pattern)"
+        level = "Moderate Risk"
+        biomarker = "Echo Chamber Interaction"
+        desc = "Matches 'Interactive Non-Depressed' group. Engaging in support forums but likely not clinically depressed."
+        color = "#f59e0b" # Yellow
     else: 
-        level = "High Risk (Depressed Pattern)"
+        level = "High Risk"
+        biomarker = "Nocturnal & High-Effort"
+        desc = "Matches 'Depressed' cohort. Indicators: Nocturnal posting spikes (00-05 UTC), high-effort/low-frequency posting."
+        color = "#ef4444" # Red
         
-    return {"probability": prob, "risk_level": level, "word_count": len(processed_text.split())}
+    return {
+        "probability": prob, 
+        "risk_level": level, 
+        "biomarker": biomarker,
+        "description": desc,
+        "color": color,
+        "word_count": len(processed_text.split())
+    }
 
-# --- 4. AGENT REASONING (SambaNova/Nebius) ---
+# --- 4. AGENT REASONING (SambaNova + Nebius Kimi) ---
 def agent_reasoning(text, risk_data, provider="SambaNova"):
     """
     Uses Sponsor APIs to generate the analysis report.
     """
     client = None
-    model = None
+    model_id = None
     
     # SYSTEM PROMPT
     system_prompt = f"""
     You are 'Dr. Longformer', an empathetic Clinical AI Research Agent.
     
-    CLINICAL DATA (Mental-Longformer):
-    - Risk Level: {risk_data['risk_level']}
-    - Probability: {risk_data['probability']:.1%}
-    - Input Length: {risk_data['word_count']} words
+    CLINICAL DATA:
+    - Risk Level: {risk_data['risk_level']} ({risk_data['probability']:.1%})
+    - Biomarker: {risk_data['biomarker']}
     
     USER TEXT SNIPPET: "{text[:800]}..."
     
@@ -73,38 +90,44 @@ def agent_reasoning(text, risk_data, provider="SambaNova"):
     """
 
     try:
-        # --- SPONSOR 1: NEBIUS (Kimi K2) ---
+        # --- SPONSOR 1: NEBIUS (Kimi K2 Thinking) ---
         if provider == "Nebius (Kimi K2)":
             api_key = os.getenv("NEBIUS_API_KEY")
             if not api_key: return "⚠️ Nebius API Key missing."
             
-            client = OpenAI(base_url="https://api.tokenfactory.nebius.com/v1/", api_key=api_key)
-            model = "moonshotai/Kimi-K2-Thinking" 
+            client = OpenAI(
+                base_url="https://api.tokenfactory.nebius.com/v1/", 
+                api_key=api_key
+            )
+            model_id = "moonshotai/Kimi-K2-Thinking" 
 
         # --- SPONSOR 2: SAMBANOVA (Llama 3.3) ---
         elif provider == "SambaNova":
             api_key = os.getenv("SAMBANOVA_API_KEY")
             if not api_key: return "⚠️ SambaNova API Key missing."
             
-            client = OpenAI(base_url="https://api.sambanova.ai/v1", api_key=api_key)
-            model = "Meta-Llama-3.3-70B-Instruct" 
+            client = OpenAI(
+                base_url="https://api.sambanova.ai/v1", 
+                api_key=api_key
+            )
+            model_id = "Meta-Llama-3.3-70B-Instruct" 
 
         # EXECUTE
         response = client.chat.completions.create(
-            model=model,
+            model=model_id,
             messages=[
                 {"role": "system", "content": system_prompt},
                 {"role": "user", "content": "Analyze this."}
             ],
             temperature=0.6,
-            max_tokens=250
+            max_tokens=300
         )
         return response.choices[0].message.content
 
     except Exception as e:
         return f"Reasoning Error ({provider}): {str(e)}"
 
-# --- 5. PIPELINE (No Chatbot, Just Report) ---
+# --- 5. PIPELINE ---
 def full_analysis_pipeline(user_text, location, provider):
     if not user_text.strip(): return "Please enter text."
     
@@ -121,6 +144,7 @@ def full_analysis_pipeline(user_text, location, provider):
     color = "green" if risk_data['probability'] < 0.4 else "orange" if risk_data['probability'] < 0.6 else "red"
     
     # 5. Build HTML/Markdown Report
+    # FIX: Added 'white-space: pre-wrap' to preserve newlines in display
     return f"""
     <div style="border-left: 6px solid {color}; padding-left: 15px; background-color: {color}10; border-radius: 5px; padding: 20px;">
         <h2 style="color:{color}; margin:0;">{risk_data['risk_level']}</h2>
@@ -128,7 +152,9 @@ def full_analysis_pipeline(user_text, location, provider):
     </div>
     
     ### 🧠 Agent Analysis ({provider})
+    <div style="white-space: pre-wrap; font-family: sans-serif;">
     {explanation}
+    </div>
     
     ---
     ### 🆘 Recommended Resources
@@ -137,15 +163,21 @@ def full_analysis_pipeline(user_text, location, provider):
 
 # --- 6. EXAMPLES ---
 example_low = """The new update for the Linux kernel (6.8) finally addressed the driver issues I was seeing with Realtek cards. I read the changelog on kernel.org and noticed they patched the module specific to the rtl8821ce chipset.
+
 On a different note, the defensive stats for the basketball team have improved significantly. Allowing 15% fewer points per possession is a game changer.
+
 I also spent some time woodworking. I'm building a walnut coffee table and the grain is tricky to plane. I might switch to a polyurethane finish for durability."""
 
 example_mod = """Things have been pretty busy at work. We're in the middle of a big project and deadlines are tight. Staying a bit later than usual but that's just how it goes sometimes. Team is handling it well overall.
+
 Sleep could be better. Been staying up too late scrolling my phone. Need to work on that. Usually feel okay once I get moving in the morning though.
-Went hiking with some friends last weekend which was nice. Good to get outside and move around."""
+
+Went hiking with some friends last weekend which was nice. Good to get outside and move around. We're talking about doing another trip next month. Weather should be better by then."""
 
 example_high = """I don't know why I even bother getting out of bed anymore. I slept for 12 hours yesterday and I'm still exhausted. It feels like my limbs weigh a thousand pounds.
+
 I've been avoiding my friends for weeks. They keep texting me, but I can't bring myself to reply. The thought of socializing is terrifying.
+
 Everything feels like a shade of grey. I can't concentrate on my work. I feel like I'm drowning while everyone else is breathing fine. I was up until 4 AM again last night just staring at the ceiling."""
 
 # --- 7. UI ---
@@ -171,7 +203,7 @@ with gr.Blocks(title="Depression Risk Agent") as demo:
             gr.Markdown("### 3. Agent Settings")
             with gr.Row():
                 # RENAMED to be clearer
-                loc_dropdown = gr.Dropdown(["Global", "US", "Malaysia"], value="Global", label="Crisis Resource Region")
+                loc_dropdown = gr.Dropdown(["Global", "US", "Malaysia"], value="Malaysia", label="Crisis Resource Region")
                 provider_dropdown = gr.Dropdown(["SambaNova", "Nebius (Kimi K2)"], value="SambaNova", label="Reasoning Brain")
             
             submit = gr.Button("🚀 Run Analysis Agent", variant="primary", size="lg")