Update main.py

main.py

@@ -3,7 +3,7 @@ from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
 from langchain_groq import ChatGroq
-from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.messages import SystemMessage, HumanMessage
 from groq import Groq
 import os
 import re
@@ -61,6 +61,7 @@ if GROQ_API_KEY:
         available_ids = [m.id for m in models_data]
         print(f"📋 Groq models accessible with this key: {available_ids}")
 
+        # Filter out audio, guards/safeguards, and third-party models requiring terms
         chat_models = [
             m for m in available_ids 
             if not any(k in m.lower() for k in ["whisper", "guard", "safeguard", "canopylabs", "orpheus"])
@@ -198,13 +199,11 @@ Task:
 2. If a User Rule exists, explicitly state if this clause violates it.
 3. If it is risky, suggest a 1-sentence edit to make it safer."""
 
-                prompt = ChatPromptTemplate.from_messages([
-                    ("system", system_msg),
-                    ("human", "{clause_text}")
-                ])
-
                 try:
-                    ai_response = (prompt | llm).invoke({"clause_text": clause})
+                    ai_response = llm.invoke([
+                        SystemMessage(content=system_msg),
+                        HumanMessage(content=clause)
+                    ])
                     explanation = ai_response.content
                 except Exception as e:
                     cause = getattr(e, '__cause__', None)
@@ -311,16 +310,16 @@ Only return valid, parseable JSON. Do not include markdown code block backticks.
 --- ACCUSED TESTIMONY ---
 {final_accused_text[:6000]}"""
 
-    prompt = ChatPromptTemplate.from_messages([
-        ("system", system_prompt),
-        ("human", "{comparison_input}")
-    ])
-
     try:
-        ai_response = (prompt | llm).invoke({"comparison_input": user_prompt})
+        # Direct message passing avoids template string bracket errors
+        messages = [
+            SystemMessage(content=system_prompt),
+            HumanMessage(content=user_prompt)
+        ]
+        ai_response = llm.invoke(messages)
         raw_content = ai_response.content.strip()
 
-        # Strip accidental code fences if returned by the LLM
+        # Remove markdown code fences if returned by the LLM
         if "```" in raw_content:
             raw_content = re.sub(r"^```(?:json)?\s*", "", raw_content, flags=re.MULTILINE)
             raw_content = re.sub(r"\s*```$", "", raw_content, flags=re.MULTILINE)
@@ -331,7 +330,6 @@ Only return valid, parseable JSON. Do not include markdown code block backticks.
 
     except json.JSONDecodeError as jde:
         print(f"❌ Failed to decode JSON from Groq: {jde} | Raw content: {raw_content[:200]}")
-        # Safe fallback matching the interface
         return {
             "client_timeline": {"party": "Client", "events": []},
             "accused_timeline": {"party": "Accused", "events": []},