Update app.py

app.py

@@ -4,58 +4,45 @@ from huggingface_hub import login
 from openai import OpenAI
 import os
 
-# --- 1. AUTHENTICATION & SETUP ---
-# Authenticate Hugging Face (for your private Mental-Longformer)
+# --- 1. SETUP ---
 hf_token = os.getenv("HF_TOKEN")
 if hf_token:
     login(token=hf_token)
 
-# --- 2. LOAD THE SPECIALIZED TOOL ---
+# --- 2. TOOL LOADING ---
 print("Loading Mental-Longformer...")
 model_name = "avtak/erisk-longformer-depression-v1"
 classifier = pipeline("text-classification", model=model_name, truncation=True, max_length=4096, top_k=None)
 
-# --- 3. DEFINE MCP TOOLS ---
+# --- 3. MCP TOOLS ---
 
 def get_crisis_resources(location: str = "Global") -> str:
-    """
-    MCP Tool: Returns mental health resources based on location.
-    """
+    """Returns mental health resources based on location."""
     resources = {
-        "US": "🇺🇸 **US Resources:**\n- Crisis Text Line: Text HOME to 741741\n- Suicide Lifeline: Call 988",
-        "Malaysia": "🇲🇾 **Malaysia Resources:**\n- Befrienders KL: 03-76272929\n- Talian Kasih: 15999",
-        "Global": "🌍 **International:**\n- Find a helpline: [befrienders.org](https://www.befrienders.org)"
+        "US": "🇺🇸 **US:** Crisis Text Line: 741741 | Suicide Lifeline: 988",
+        "Malaysia": "🇲🇾 **Malaysia:** Befrienders KL: 03-76272929 | Talian Kasih: 15999",
+        "Global": "🌍 **International:** [befrienders.org](https://www.befrienders.org)"
     }
+    # Fallback logic
     for key in resources:
-        if key.lower() in location.lower():
+        if location and key.lower() in location.lower():
             return resources[key]
     return resources["Global"]
 
 def detect_depression_risk(text: str) -> dict:
-    """
-    MCP Tool: Analyzes text using Mental-Longformer (eRisk 2025).
-    Returns probability, risk level, and word count.
-    """
-    # Thesis Logic: User-Level Aggregation simulation
+    """Analyzes text using Mental-Longformer (eRisk 2025)."""
+    # Thesis Aggregation Logic
     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 Control Pattern)"
-    elif 0.40 <= prob < 0.60: 
-        level = "Moderate Risk (Interactive Echo Chamber Pattern)"
-    else: 
-        level = "High Risk (Depressed Cohort Pattern)"
+    if prob < 0.40: level = "Low Risk (Isolated Pattern)"
+    elif 0.40 <= prob < 0.60: level = "Moderate Risk (Echo Chamber Pattern)"
+    else: level = "High Risk (Depressed Pattern)"
         
-    return {
-        "probability": prob, 
-        "risk_level": level, 
-        "word_count": len(processed_text.split())
-    }
+    return {"probability": prob, "risk_level": level, "word_count": len(processed_text.split())}
 
-# --- 4. AGENT REASONING (MULTI-SPONSOR) ---
+# --- 4. AGENT REASONING (UPDATED MODELS) ---
 def agent_reasoning(text, risk_data, provider="SambaNova"):
     """
     Uses Sponsor APIs to interpret the clinical data.
@@ -63,21 +50,21 @@ def agent_reasoning(text, risk_data, provider="SambaNova"):
     client = None
     model = None
     
-    # SYSTEM PROMPT
+    # SYSTEM PROMPT (Thesis-Informed)
     system_prompt = f"""
-    You are an empathetic Clinical AI Assistant.
+    You are an empathetic Clinical AI Agent.
     
     CLINICAL DATA (Mental-Longformer):
     - Risk Level: {risk_data['risk_level']}
     - Probability: {risk_data['probability']:.1%}
     - Input Length: {risk_data['word_count']} words
     
-    USER TEXT: "{text[:1000]}..."
+    USER TEXT SNIPPET: "{text[:800]}..."
     
     INSTRUCTIONS:
-    1. Explain the result using terms like 'Nocturnal Posting', 'High Effort' (if High) or 'Supportive Responder' (if Moderate).
-    2. Be compassionate but objective.
-    3. Max 100 words.
+    1. Acknowledge the user's situation based on the text.
+    2. Explain the risk level using thesis terms: 'Nocturnal Posting' (High), 'Supportive Responder' (Moderate), or 'Healthy External Focus' (Low).
+    3. Be compassionate. Max 80 words.
     """
 
     try:
@@ -87,75 +74,71 @@ def agent_reasoning(text, risk_data, provider="SambaNova"):
             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" # As requested
+            # Using the chat version of Kimi K2
+            model = "moonshotai/Kimi-K2-Chat" 
 
-        # --- SPONSOR 2: SAMBANOVA (Llama 3.1 / DeepSeek) ---
+        # --- 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-3.1-70B-Instruct" # Fastest/Stable for Demo
+            # UPDATED MODEL ID
+            model = "Meta-Llama-3.3-70B-Instruct" 
 
         # EXECUTE
         response = client.chat.completions.create(
             model=model,
             messages=[
                 {"role": "system", "content": system_prompt},
-                {"role": "user", "content": "Analyze this user status."}
+                {"role": "user", "content": "Analyze this."}
             ],
             temperature=0.6,
-            max_tokens=300
+            max_tokens=200
         )
         return response.choices[0].message.content
 
     except Exception as e:
-        return f"Agent Logic Error ({provider}): {str(e)}"
+        return f"Reasoning Error ({provider}): {str(e)}"
 
-# --- 5. ORCHESTRATION ---
+# --- 5. PIPELINE ---
 def full_analysis_pipeline(user_text, location, provider):
     if not user_text.strip(): return "Please enter text.", "No data", "No data"
     
-    # 1. Tool Use (Local Model)
     risk_data = detect_depression_risk(user_text)
-    
-    # 2. Tool Use (Resources)
     resources = get_crisis_resources(location)
-    
-    # 3. Agent Reasoning (Cloud API)
     explanation = agent_reasoning(user_text, risk_data, provider)
     
+    # Color logic
+    color = "green" if risk_data['probability'] < 0.4 else "orange" if risk_data['probability'] < 0.6 else "red"
+    
     return f"""
-    # {risk_data['risk_level']}
-    ## Probability: {risk_data['probability']*100:.1f}%
+    <div style="border-left: 5px solid {color}; padding-left: 10px;">
+        <h2 style="color:{color}; margin:0;">{risk_data['risk_level']}</h2>
+        <h3 style="margin-top:0;">Probability: {risk_data['probability']*100:.1f}%</h3>
+    </div>
     
     ### 🧠 Agent Analysis ({provider})
     {explanation}
     
-    ### 🆘 Resources
+    ### 🆘 Recommended Resources
     {resources}
     """
 
-# --- 6. EXAMPLES (THESIS PATTERNS) ---
+# --- 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. We're talking about doing another trip next month. Weather should be better by then."""
+Went hiking with some friends last weekend which was nice. Good to get outside and move around."""
 
 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 (GRADIO V6) ---
+# --- 7. UI ---
 with gr.Blocks(title="Depression Risk Agent") as demo:
     gr.Markdown("# 🧠 Early Depression Detection Agent (MCP)")
     gr.Markdown("Agentic system using **Mental-Longformer** (Tool) + **Multi-Provider Reasoning** (Nebius/SambaNova).")
@@ -169,18 +152,17 @@ with gr.Blocks(title="Depression Risk Agent") as demo:
                 placeholder="[Post 1] ...\n\n[Post 2] ...\n\n(Context >100 words recommended)"
             )
             
-            # EXAMPLES
-            gr.Markdown("### 2. Try Thesis Patterns")
+            gr.Markdown("### 2. Thesis Patterns (Click to Load)")
             with gr.Row():
                 btn_low = gr.Button("🟢 Low Risk", size="sm", variant="secondary")
                 btn_mod = gr.Button("🟡 Moderate", size="sm", variant="secondary")
                 btn_high = gr.Button("🔴 High Risk", size="sm", variant="secondary")
             
-            # SETTINGS
-            gr.Markdown("### 3. Settings")
+            gr.Markdown("### 3. Agent Settings")
             with gr.Row():
-                loc_dropdown = gr.Dropdown(["US", "Malaysia", "Global"], value="Global", label="Location")
-                provider_dropdown = gr.Dropdown(["SambaNova", "Nebius (Kimi K2)"], value="SambaNova", label="Agent Brain")
+                # RENAMED to be clearer
+                loc_dropdown = gr.Dropdown(["Global", "US", "Malaysia"], value="Global", 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")
             
@@ -195,16 +177,11 @@ with gr.Blocks(title="Depression Risk Agent") as demo:
                 - `get_crisis_resources(location)`: Returns localized help.
                 """)
 
-    # HANDLERS
     submit.click(full_analysis_pipeline, inputs=[input_text, loc_dropdown, provider_dropdown], outputs=output)
     
     btn_low.click(lambda: example_low, None, input_text)
     btn_mod.click(lambda: example_mod, None, input_text)
     btn_high.click(lambda: example_high, None, input_text)
 
-# --- 8. LAUNCH ---
 if __name__ == "__main__":
-    demo.launch(
-        mcp_server=True, 
-        theme=gr.themes.Soft()
-    )
+    demo.launch(mcp_server=True, theme=gr.themes.Soft())