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server/mentalHealthPatientenv_environment.py

@@ -135,12 +135,12 @@ class MentalhealthpatientenvEnvironment(Environment):
 
 
                 return MentalhealthpatientenvObservation(
-                    response=generate_response(curr_diagnosis, actual_disorders,state=self._state ),
+                    response=generate_response(curr_diagnosis, actual_disorders,state=self._state ) if self._state.step_count < self._state.max_turns else "Maximum turns reached. Ending session.",
                     clarity=random.uniform(0.0, 1.0),
                     emotional_state="neutral",
                     trust_level=self._state.trust_level,
                     risk_flag=self._state.disclosed_risk,
-                    done=False,
+                    done=True if reward >= 0.8 or self._state.step_count >= self._state.max_turns else False,
                     reward=reward,
                     metadata={"diagnosis_result": curr_diagnosis, "actual_disorders": actual_disorders, "reward": reward}
                 )
@@ -155,12 +155,12 @@ class MentalhealthpatientenvEnvironment(Environment):
                 action_sequence=self._state.action_sequence
             )
             return MentalhealthpatientenvObservation(
-                response=generate_response(action.action_type, action.message, state=self._state),
+                response=generate_response(action.action_type, action.message, state=self._state) if self._state.step_count < self._state.max_turns else "Maximum turns reached. Ending session.",
                 clarity=random.uniform(0.0, 1.0),
                 emotional_state="neutral",
                 trust_level=self._state.trust_level,
                 risk_flag=self._state.disclosed_risk,
-                done=False,
+                done=True if self._state.step_count >= self._state.max_turns else False,
                 reward=reward,  # small reward for taking an action
                 metadata={"received_action": action.action_type}
             )

server/response_generator.py

@@ -59,35 +59,35 @@ def call_llm(prompt: str) -> str:
     }
 
     payload = {
-        "model": "nvidia/nemotron-3-nano-30b-a3b:free",
+        "model": "liquid/lfm-2.5-1.2b-instruct:free",
         "messages": [
-            {"role": "system", "content": "You are a mental health patient."},
+            {"role": "system", "content": "You are a mental health patient. Keep answers short and natural."},
             {"role": "user", "content": prompt}
         ],
-        "temperature": 0.7,
-        "max_tokens": 120,
+        "temperature": 0.6,
+        "max_tokens": 50,   # 🔥 IMPORTANT (reduce latency)
         "top_p": 0.9
     }
 
     try:
-        response = requests.post(OPENROUTER_URL, headers=headers, json=payload)
+        response = requests.post(
+            OPENROUTER_URL,
+            headers=headers,
+            json=payload,
+            timeout=5   # 🔥 prevents long waiting
+        )
 
         if response.status_code != 200:
-            print("OpenRouter Error:", response.text)
-            return "I'm not sure how to respond right now..."
+            return "I'm not really sure… just feeling a bit off."
 
         result = response.json()
 
         content = result.get("choices", [{}])[0].get("message", {}).get("content")
 
-        if not content:
-            return "I don't really know how to explain it..."
+        return content.strip() if content else "I don't know… just tired."
 
-        return content.strip()
-
-    except Exception as e:
-        print("LLM Error:", e)
-        return "I don't know how to explain it..."
+    except Exception:
+        return "I don't really feel like talking much right now."
 
 
 # =========================