feat: implement backend agent modules for relationship analysis, strategy, guardrails, perspective detection, and OCR services

backend/agents/analyst.py

@@ -1,5 +1,8 @@
 from backend.llm.factory import get_llm
 from backend.utils import safe_invoke
+from backend.core.logger import get_logger
+
+logger = get_logger(__name__)
 
 ANALYST_PROMPT = """
 You are Velra, an elite relationship profiler. You must distinguish between "Playful Tension" and "Hostile Dismissiveness" using behavioral evidence, NOT names.
@@ -48,6 +51,14 @@ Return ONLY valid JSON.
 """
 
 def analyze_conversation(chat, feelings=""):
-    llm = get_llm()
-    prompt = ANALYST_PROMPT.format(chat=chat, feelings=feelings)
-    return safe_invoke(llm, prompt)
+    try:
+        logger.info("Starting conversation analysis")
+        llm = get_llm()
+        prompt = ANALYST_PROMPT.format(chat=chat, feelings=feelings)
+        response = safe_invoke(llm, prompt)
+        logger.info(f"LLM Response: {response}")
+        return response
+    except Exception as e:
+        logger.error(f"Error in analyze_conversation: {str(e)}")
+        return f"[ERROR] {str(e)}"
+

backend/agents/gaurdrail.py

@@ -1,107 +1,116 @@
-RELATIONSHIP_KEYWORDS = [
-
-    # relationships
-    "boyfriend",
-    "girlfriend",
-    "dating",
-    "relationship",
-    "situationship",
-    "partner",
-    "ex",
-
-    # emotional communication
-    "love",
-    "feelings",
-    "emotion",
-    "sad",
-    "hurt",
-    "confused",
-    "mixed signals",
-    "ghosting",
-    "dry texting",
-    "attachment",
-
-    # texting/chat dynamics
-    "text",
-    "chat",
-    "message",
-    "reply",
-    "left on read",
-    "ignored",
-
-    # conflict/emotional tension
-    "argument",
-    "fight",
-    "conflict",
-    "distance",
-    "cold",
-    "avoidant",
-    "anxious",
-
-    # common conversational indicators
-    "hey",
-    "okay",
-    "sorry",
-    "why are you",
-    "are you",
-    "miss you",
-    "coming",
-    "meeting",
-]
-
-BLOCKED_KEYWORDS = [
-
-    "python",
-    "javascript",
-    "coding",
-    "programming",
-    "math",
-    "physics",
-    "chemistry",
-    "politics",
-    "religion",
-    "hacking",
-    "sql",
-    "docker",
-    "kubernetes",
-    "algorithm",
-]
-
-
-def validate_domain(user_input: str):
-
-    text = user_input.lower()
-
-    # -----------------------------------
-    # BLOCK CLEARLY TECHNICAL INPUTS
-    # -----------------------------------
-
-    for word in BLOCKED_KEYWORDS:
-
-        if word in text:
-            return "OUT_OF_SCOPE"
-
-    # -----------------------------------
-    # ALLOW RELATIONSHIP / CHAT INPUTS
-    # -----------------------------------
-
-    for word in RELATIONSHIP_KEYWORDS:
-
-        if word in text:
-            return "ALLOWED"
-
-    # -----------------------------------
-    # HEURISTIC:
-    # RAW CHAT DETECTION
-    # -----------------------------------
-
-    colon_count = text.count(":")
-
-    if colon_count >= 2:
-        return "ALLOWED"
-
-    # -----------------------------------
-    # DEFAULT
-    # -----------------------------------
-
-    return "ALLOWED"
+from backend.core.logger import get_logger
+
+logger = get_logger(__name__)
+
+RELATIONSHIP_KEYWORDS = [
+
+    # relationships
+    "boyfriend",
+    "girlfriend",
+    "dating",
+    "relationship",
+    "situationship",
+    "partner",
+    "ex",
+
+    # emotional communication
+    "love",
+    "feelings",
+    "emotion",
+    "sad",
+    "hurt",
+    "confused",
+    "mixed signals",
+    "ghosting",
+    "dry texting",
+    "attachment",
+
+    # texting/chat dynamics
+    "text",
+    "chat",
+    "message",
+    "reply",
+    "left on read",
+    "ignored",
+
+    # conflict/emotional tension
+    "argument",
+    "fight",
+    "conflict",
+    "distance",
+    "cold",
+    "avoidant",
+    "anxious",
+
+    # common conversational indicators
+    "hey",
+    "okay",
+    "sorry",
+    "why are you",
+    "are you",
+    "miss you",
+    "coming",
+    "meeting",
+]
+
+BLOCKED_KEYWORDS = [
+
+    "python",
+    "javascript",
+    "coding",
+    "programming",
+    "math",
+    "physics",
+    "chemistry",
+    "politics",
+    "religion",
+    "hacking",
+    "sql",
+    "docker",
+    "kubernetes",
+    "algorithm",
+]
+
+
+def validate_domain(user_input: str):
+    try:
+        logger.info(f"Validating domain for input of length {len(user_input)}")
+        text = user_input.lower()
+
+        # -----------------------------------
+        # BLOCK CLEARLY TECHNICAL INPUTS
+        # -----------------------------------
+
+        for word in BLOCKED_KEYWORDS:
+            if word in text:
+                logger.info(f"Blocked keyword detected: {word}")
+                return "OUT_OF_SCOPE"
+
+        # -----------------------------------
+        # ALLOW RELATIONSHIP / CHAT INPUTS
+        # -----------------------------------
+
+        for word in RELATIONSHIP_KEYWORDS:
+            if word in text:
+                logger.info(f"Relationship keyword detected: {word}")
+                return "ALLOWED"
+
+        # -----------------------------------
+        # HEURISTIC:
+        # RAW CHAT DETECTION
+        # -----------------------------------
+
+        colon_count = text.count(":")
+
+        if colon_count >= 2:
+            logger.info(f"Heuristic match: {colon_count} colons found")
+            return "ALLOWED"
+
+        # -----------------------------------
+        # DEFAULT
+        # -----------------------------------
+
+        return "ALLOWED"
+    except Exception as e:
+        logger.error(f"Error in validate_domain: {str(e)}")
+        return "ALLOWED" # Default to allowed on error to avoid blocking users

backend/agents/perspective.py

@@ -1,5 +1,8 @@
 from backend.llm.factory import get_llm
 from backend.utils import safe_invoke
+from backend.core.logger import get_logger
+
+logger = get_logger(__name__)
 
 PERSPECTIVE_PROMPT = """
 You are Velra's Perspective Detection Engine.
@@ -36,9 +39,17 @@ Return ONLY the raw JSON without markdown fences.
 """
 
 def detect_perspective(chat, feelings):
-    llm = get_llm()
-    chat_block = chat if chat and str(chat).strip() else "No conversation provided."
-    feelings_block = feelings if feelings and str(feelings).strip() else "No explicit feelings provided."
-    
-    prompt = PERSPECTIVE_PROMPT.format(chat=chat_block, feelings=feelings_block)
-    return safe_invoke(llm, prompt)
+    try:
+        logger.info("Starting perspective detection")
+        llm = get_llm()
+        chat_block = chat if chat and str(chat).strip() else "No conversation provided."
+        feelings_block = feelings if feelings and str(feelings).strip() else "No explicit feelings provided."
+        
+        prompt = PERSPECTIVE_PROMPT.format(chat=chat_block, feelings=feelings_block)
+        response = safe_invoke(llm, prompt)
+        logger.info(f"LLM Response: {response}")
+        return response
+    except Exception as e:
+        logger.error(f"Error in detect_perspective: {str(e)}")
+        return f"[ERROR] {str(e)}"
+

backend/agents/psychology.py

@@ -1,5 +1,8 @@
 from backend.llm.factory import get_llm
 from backend.utils import safe_invoke
+from backend.core.logger import get_logger
+
+logger = get_logger(__name__)
 
 PSYCHOLOGY_PROMPT = """
 You are Velra, an elite psychological profiler. You calculate compatibility based on BEHAVIORAL ALIGNMENT.
@@ -45,6 +48,14 @@ Return ONLY valid JSON.
 """
 
 def analyze_psychology(chat, feelings):
-    llm = get_llm()
-    prompt = PSYCHOLOGY_PROMPT.format(chat=chat, feelings=feelings)
-    return safe_invoke(llm, prompt)
+    try:
+        logger.info("Starting psychology analysis")
+        llm = get_llm()
+        prompt = PSYCHOLOGY_PROMPT.format(chat=chat, feelings=feelings)
+        response = safe_invoke(llm, prompt)
+        logger.info(f"LLM Response: {response}")
+        return response
+    except Exception as e:
+        logger.error(f"Error in analyze_psychology: {str(e)}")
+        return f"[ERROR] {str(e)}"
+

backend/agents/strategy.py

@@ -1,5 +1,8 @@
 from backend.llm.factory import get_llm
 from backend.utils import safe_invoke
+from backend.core.logger import get_logger
+
+logger = get_logger(__name__)
 
 STRATEGY_PROMPT = """
 You are Velra, the ultimate relationship strategist.
@@ -39,6 +42,14 @@ Return ONLY valid JSON.
 """
 
 def generate_strategy(context):
-    llm = get_llm()
-    prompt = STRATEGY_PROMPT.format(context=context)
-    return safe_invoke(llm, prompt)
+    try:
+        logger.info("Starting strategy generation")
+        llm = get_llm()
+        prompt = STRATEGY_PROMPT.format(context=context)
+        response = safe_invoke(llm, prompt)
+        logger.info(f"LLM Response: {response}")
+        return response
+    except Exception as e:
+        logger.error(f"Error in generate_strategy: {str(e)}")
+        return f"[ERROR] {str(e)}"
+

backend/services/ocr_service.py

@@ -1,19 +1,25 @@
-import pytesseract
-from PIL import Image
-
-# 👇 ADD THIS LINE (VERY IMPORTANT FOR WINDOWS)
-# pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
-
-
-def extract_text_from_images(image_paths):
-    texts = []
-
-    for path in image_paths:
-        try:
-            img = Image.open(path)
-            text = pytesseract.image_to_string(img)
-            texts.append(text)
-        except:
-            texts.append("")
-
-    return "\n".join(texts)
+import pytesseract
+from PIL import Image
+from backend.core.logger import get_logger
+
+logger = get_logger(__name__)
+
+# 👇 ADD THIS LINE (VERY IMPORTANT FOR WINDOWS)
+# pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
+
+
+def extract_text_from_images(image_paths):
+    logger.info(f"Starting OCR extraction for {len(image_paths)} images")
+    texts = []
+
+    for path in image_paths:
+        try:
+            logger.info(f"Processing image: {path}")
+            img = Image.open(path)
+            text = pytesseract.image_to_string(img)
+            texts.append(text)
+        except Exception as e:
+            logger.error(f"Error processing image {path}: {str(e)}")
+            texts.append("")
+
+    return "\n".join(texts)