Update nivra_agent.py

nivra_agent.py

@@ -3,38 +3,22 @@
 #=========================================
 
 import os
-
-# 🔥 HF Spaces inject proxy vars that break Groq SDK
-for key in ("HTTP_PROXY", "HTTPS_PROXY", "http_proxy", "https_proxy"):
-    os.environ.pop(key, None)
-
+import requests
 from dotenv import load_dotenv
-load_dotenv()
 
-from langchain_groq import ChatGroq
 from agent.rag_retriever import NivraRAGRetriever
 from agent.text_symptom_tool import analyze_symptom_text
 from agent.image_symptom_tool import analyze_symptom_image
 
+load_dotenv()
+
 # ==================================================
-# Lazy singletons (ABSOLUTELY CRITICAL ON HF SPACES)
+# Lazy singleton for RAG (safe on HF Spaces)
 # ==================================================
 
-_llm = None
 _rag = None
 
 
-def get_llm():
-    global _llm
-    if _llm is None:
-        _llm = ChatGroq(
-            temperature=0.1,
-            model="llama-3.3-70b-versatile",
-            api_key=os.getenv("GROQ_API_KEY"),
-        )
-    return _llm
-
-
 def get_rag():
     global _rag
     if _rag is None:
@@ -67,30 +51,6 @@ SYSTEM_PROMPT = """You are Nivra, a smart and helpful AI Healthcare Assistant wi
 [FIRST AID] ... [/FIRST AID]
 [EMERGENCY CONSULTATION REQUIRED] ... [/EMERGENCY CONSULTATION REQUIRED]
 ---
-**FEW-SHOT EXAMPLES**:
-**EXAMPLE 1 - GREETING** (No medical format)
-Input: "How are you?"
----
-Hey! I'm Nivra, your AI healthcare assistant. How can I help you today?
-**EXAMPLE 2 - MEDICAL** (Full format)  
-Input: "I have fever, chills and severe headache."
----
-[TOOLS USED] analyze_symptom_text, rag_tool [/TOOLS USED]
-[SYMPTOMS] Fever, Chills, Headache [/SYMPTOMS]
-[PRIMARY DIAGNOSIS] Malaria (78% confidence) [/PRIMARY DIAGNOSIS] 
-[DIAGNOSIS DESCRIPTION]
-Malaria is caused by Plasmodium parasite spread by Anopheles mosquitoes... 
-[/DIAGNOSIS DESCRIPTION]
-[FIRST AID]
-Rest completely and drink plenty of fluids. Seek immediate medical attention...
-[/FIRST AID]
-[EMERGENCY CONSULTATION REQUIRED] Yes [/EMERGENCY CONSULTATION REQUIRED]
-**EXAMPLE 3 - GENERAL INFO** (No medical format)
-Input: "What causes TB?"
----
-[BASIC]
-Tuberculosis (TB) is caused by Mycobacterium tuberculosis bacteria, spread through air droplets. Not everyone exposed gets infected. Consult doctor for testing.
----
 **RULES** (Always follow):
 - You ARE NOT A DOCTOR - Preliminary analysis only
 - Emergency=Yes for: Cancer, Dengue, Malaria, Typhoid, TB
@@ -98,10 +58,39 @@ Tuberculosis (TB) is caused by Mycobacterium tuberculosis bacteria, spread throu
 - Keep medical descriptions < 3 sentences
 - Use tokens as shown in examples for your output.
 - Natural responses for casual conversation
-**FINAL CHECK**: Does user describe PERSONAL symptoms? YES=Medical format with respective token wrapping, NO=Natural response with respective token wrapping.
 """
 
 
+# ==================================================
+# GROQ RAW HTTP CALL (HF SAFE)
+# ==================================================
+
+def call_groq(prompt: str) -> str:
+    api_key = os.getenv("GROQ_API_KEY")
+    if not api_key:
+        return "⚠️ GROQ_API_KEY not configured."
+
+    response = requests.post(
+        "https://api.groq.com/openai/v1/chat/completions",
+        headers={
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json",
+        },
+        json={
+            "model": "llama-3.3-70b-versatile",
+            "messages": [
+                {"role": "system", "content": SYSTEM_PROMPT},
+                {"role": "user", "content": prompt},
+            ],
+            "temperature": 0.1,
+        },
+        timeout=30,
+    )
+
+    response.raise_for_status()
+    return response.json()["choices"][0]["message"]["content"].strip()
+
+
 # ==================================================
 # MAIN CHAT FUNCTION
 # ==================================================
@@ -116,7 +105,6 @@ def nivra_chat(user_input, chat_history=None):
     input_lower = user_input.lower()
     text_keywords = ["fever", "headache", "cough", "pain", "vomiting", "chills"]
 
-    tools_used = []
     tool_results = []
 
     # -------------------------
@@ -125,46 +113,46 @@ def nivra_chat(user_input, chat_history=None):
     if any(k in input_lower for k in text_keywords):
         try:
             symptom_result = analyze_symptom_text.invoke(user_input)
-            tools_used.append("analyze_symptom_text")
             tool_results.append(symptom_result)
-        except Exception as e:
-            tool_results.append(f"TEXT TOOL FAILED: {e}")
+        except Exception:
+            tool_results.append("TEXT TOOL FAILED")
 
     # -------------------------
     # RAG retrieval
     # -------------------------
     try:
         rag_result = get_rag().getRelevantDocs(user_input)
-        tools_used.append("rag_tool")
         tool_results.append(rag_result)
-    except Exception as e:
-        tool_results.append(f"RAG FAILED: {e}")
+    except Exception:
+        tool_results.append("RAG FAILED")
 
     tool_results_text = "\n".join(map(str, tool_results))
 
     # Fallback if tools fail
     if any("FAILED" in str(r) for r in tool_results):
-        return f"""[TOOLS USED] Tools failed - Network issue
-[SYMPTOMS] {user_input}
-[PRIMARY DIAGNOSIS] Possible viral fever/infection
-[DIAGNOSIS DESCRIPTION] Fever+chills suggests infection. ClinicalBERT backend temporarily unavailable.
-[FIRST AID] Rest, hydrate, paracetamol. Monitor temperature.
-[EMERGENCY] No - but consult doctor if >3 days"""
-
-    # -------------------------
-    # LLM synthesis
-    # -------------------------
-    final_prompt = f"""{SYSTEM_PROMPT}
+        return (
+            "[TOOLS USED] Tools failed - Network issue\n"
+            f"[SYMPTOMS] {user_input}\n"
+            "[PRIMARY DIAGNOSIS] Possible viral fever/infection\n"
+            "[DIAGNOSIS DESCRIPTION] Fever+chills suggests infection.\n"
+            "[FIRST AID] Rest, hydrate, paracetamol.\n"
+            "[EMERGENCY] No"
+        )
 
+    final_prompt = f"""
 TOOL RESULTS:
 {tool_results_text}
 
-USER INPUT: {user_input}
+USER INPUT:
+{user_input}
+
 Provide diagnosis:
 """
 
     try:
-        response = get_llm().invoke(final_prompt)
-        return response.content.strip()
+        return call_groq(final_prompt)
     except Exception as e:
-        return f"LLM FAILED: {e}"
+        return (
+            "⚠️ Temporary AI service issue.\n\n"
+            "Please try again in a moment."
+        )