deleted bload and updated judge sim and gitignore

.gitignore

@@ -9,3 +9,4 @@ judge_output.txt
 .vscode/
 .idea/
 *.log
+expanded/

judge_simulator.py

@@ -24,19 +24,19 @@ Author: magicpin AI Challenge Team
 BOT_URL = "http://localhost:8080"
 
 # Choose your LLM provider: "openai", "anthropic", "gemini", "deepseek", "groq", "ollama", "openrouter"
-LLM_PROVIDER = "openai"
+LLM_PROVIDER = "ollama"
 
 # Your API key (paste your key here)
 LLM_API_KEY = ""  # <-- PUT YOUR API KEY HERE
 
 # Model to use (leave empty for default, or specify like "gpt-4o", "claude-3-5-sonnet-20241022", etc.)
-LLM_MODEL = ""  # <-- Optional: specify model or leave empty for default
+LLM_MODEL = "llama3.2:latest"  # <-- Optional: specify model or leave empty for default
 
 # For Ollama only: local server URL
 OLLAMA_URL = "http://localhost:11434"
 
 # Which test to run by default
-TEST_SCENARIO = "all"
+TEST_SCENARIO = "full_evaluation"
 
 # =============================================================================
 # ██████  END OF CONFIGURATION - DON'T EDIT BELOW THIS LINE ██████

knowledge.md

@@ -1,26 +0,0 @@
-# Knowledge Base: Vera Lead Solver
-
-## Failure Pattern Audit (2026-05-02)
-
-### 1. Metric Fabrication (Grounding Failure)
-- **Pattern**: Bot invented "45 directions" and "0.18% view increase" to sound specific.
-- **Deduction**: Specificity score dropped to 6/10.
-- **Fix**: Sanitize context and provide a "Whitelist of Grounded Metrics". Warn against inventing secondary metrics.
-
-### 2. Language Imbalance
-- **Pattern**: When Hinglish is requested, the bot writes "mostly in Hindi", which the judge penalizes for not being a proper "mix".
-- **Fix**: Instruct bot to use 60% English / 40% Hindi for Hinglish.
-
-### 3. Abrupt Tones
-- **Pattern**: Short messages often sound too formal or clinical without enough "warmth" or "persuasion".
-- **Fix**: Use specific persuasive triggers: loss aversion ("losing 5% weekly"), peer proof ("other clinics in [Locality]"), or scarcity.
-
-### 4. Decision Quality Voids
-- **Pattern**: Bot mentions the trigger but doesn't explain the *financial* or *business* impact.
-- **Fix**: Prompt bot to explicitly state "WHY ACT NOW" (e.g., "Non-compliance risks clinic closure" or "Recall improves LTV").
-
-## Technical Strategy for 50/50
-- **Deterministic Metrics**: Only allow metrics explicitly found in `perf` or `payload`.
-- **Character Buffer**: Hard-coded pruning to 300 chars to avoid truncation penalty.
-- **Persona**: "Elite Growth Strategist" - peer of the merchant, but authoritative.
-- **Citations**: Verbatim inclusion of sources is mandatory.

knowledge.py

@@ -1,20 +0,0 @@
-
-# High-Score Block (9/10)
-# Trigger: performance update
-# Message: Dr. Meera, your clinic had 2410 views & 18 calls in 30 days. Fluoride research is surging. Act now to capture demand.
-
-# High-Score Block (9/10)
-# Trigger: performance update
-# Message: Dr. Meera, your clinic had 2410 views, 18 calls & 45 directions in 30 days. You're losing 5% calls weekly. Launch Fluoride offer now.
-
-# High-Score Block (9/10)
-# Trigger: performance update
-# Message: Dr. Meera, your clinic had 2410 views, 18 calls & 45 directions in 30 days. You're losing 5% calls weekly. Fluoride research is surging with 18% increase in views.
-
-# High-Score Block (9/10)
-# Trigger: performance update
-# Message: Dr. Meera, your clinic had 2410 views, 18 calls & 45 directions in 30 days. You're losing 5% calls weekly.
-
-# High-Score Block (9/10)
-# Trigger: performance update
-# Message: Dr. Meera, your clinic had 2410 views, 18 calls & 45 directions in 30 days. You're losing 5% calls weekly with an 18% surge in views.

main.py

@@ -1,188 +0,0 @@
-# BEST SCORE: 41/50 (82%) - EXCELLENT
-# Model: Gemini-2.0-Flash-001 (via OpenRouter)
-# Tested on: 2026-05-02
-# Features: Strict grounding, Trigger primacy, Hinglish support, Source citations.
-
-import os
-import json
-import logging
-import asyncio
-import time
-import re
-from typing import List, Dict, Any, Optional
-from fastapi import FastAPI
-from pydantic import BaseModel
-import httpx
-from dotenv import load_dotenv
-
-load_dotenv()
-
-# Lead AI Problem Solver: Sequential Reinforcement Architecture
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger("VeraLeadSolver")
-
-app = FastAPI()
-
-class ContextPayload(BaseModel):
-    scope: str
-    context_id: str
-    version: int
-    payload: Dict[Any, Any]
-
-class TickRequest(BaseModel):
-    available_triggers: List[str]
-
-# Distributed Credential Strategy: Round-Robin Key Pool
-OPENROUTER_KEYS = [
-    os.getenv("OPENROUTER_KEY_1"),
-    os.getenv("OPENROUTER_KEY_2"),
-    os.getenv("OPENROUTER_KEY_3"),
-    os.getenv("OPENROUTER_KEY_4"),
-    os.getenv("OPENROUTER_KEY_5"),
-    os.getenv("OPENROUTER_KEY_6")
-]
-OPENROUTER_KEYS = [k for k in OPENROUTER_KEYS if k]
-
-class KeyRotator:
-    def __init__(self, keys: List[str]):
-        self.keys = keys
-        self.index = 0
-        self.lock = asyncio.Lock()
-        
-    async def get_key(self) -> str:
-        if not self.keys:
-            return ""
-        async with self.lock:
-            key = self.keys[self.index]
-            self.index = (self.index + 1) % len(self.keys)
-            return key
-
-or_rotator = KeyRotator(OPENROUTER_KEYS)
-
-contexts = {"category": {}, "merchant": {}, "customer": {}, "trigger": {}}
-semaphore = asyncio.Semaphore(1) # STRICT SEQUENTIAL
-
-async def call_llm(prompt: str, system: str = "", temperature: float = 0.0) -> Optional[Dict]:
-    key = await or_rotator.get_key()
-    url = "https://openrouter.ai/api/v1/chat/completions"
-    headers = {
-        "Authorization": f"Bearer {key}",
-        "Content-Type": "application/json",
-        "HTTP-Referer": "https://vera.ai",
-        "X-Title": "Vera Lead Solver"
-    }
-    # Using openrouter/free to get the best free model
-    payload = {
-        "model": "openrouter/free",
-        "messages": [
-            {"role": "system", "content": system + "\n\nCRITICAL: Return ONLY valid JSON. No markdown, no preambles."},
-            {"role": "user", "content": prompt}
-        ],
-        "temperature": temperature,
-        "max_tokens": 1000
-    }
-    
-    async with httpx.AsyncClient() as client:
-        try:
-            response = await client.post(url, headers=headers, json=payload, timeout=60.0)
-            if response.status_code == 200:
-                resp_json = response.json()
-                content = resp_json["choices"][0]["message"].get("content")
-                if not content:
-                    return None
-                content = re.sub(r'```json\s*|\s*```', '', content).strip()
-                start = content.find('{')
-                end = content.rfind('}')
-                if start != -1 and end != -1:
-                    content = content[start:end+1]
-                return json.loads(content)
-            else:
-                logger.error(f"LLM Error {response.status_code}: {response.text}")
-                return None
-        except Exception as e:
-            logger.error(f"LLM Exception: {e}")
-            return None
-
-async def compose(category: dict, merchant: dict, trigger: dict, customer: Optional[dict] = None) -> dict:
-    ident = merchant.get("identity", {})
-    perf = merchant.get("performance", {})
-    owner_name = ident.get("owner_first_name", "Partner")
-    
-    sys_prompt = f"""Role: Elite Growth Strategist for magicpin.
-Objective: Compose a high-impact WhatsApp message.
-
-STRICT GROUNDING RULES:
-1. NO FABRICATION: Only use numbers present in context. 
-2. TRIGGER PRIMACY: Message MUST address the trigger event first.
-3. SPECIFICITY: Use metrics like views, calls, directions, or CTR from Context.
-4. CATEGORY FIT: For dentists, use "Dr." and clinical peer tone.
-5. LANGUAGE: If 'hi' in languages, use Hinglish.
-6. SOURCE: Include source citations (e.g. "JIDA Oct") if present.
-
-Output JSON: {{"body": "...", "cta": "...", "rationale": "..."}}
-"""
-
-    context_str = f"CATEGORY: {json.dumps(category)}\nMERCHANT: {json.dumps(merchant)}\nTRIGGER: {json.dumps(trigger)}\nCUSTOMER: {json.dumps(customer) if customer else 'None'}"
-
-    res = await call_llm(f"Context: {context_str}\n\nCompose a grounded message based on trigger.", system=sys_prompt)
-    if not res:
-        v = perf.get('views', '?')
-        c = perf.get('calls', '?')
-        return {"body": f"Hi {owner_name}, noticed {v} views and {c} calls on your profile. Important {trigger.get('kind')} update inside. Check?", "cta": "Reply YES", "rationale": "Fallback"}
-    
-    return res
-
-@app.get("/v1/healthz")
-def healthz():
-    return {"status": "ok", "contexts_loaded": {s: len(c) for s, c in contexts.items()}}
-
-@app.get("/v1/metadata")
-def metadata():
-    return {"team_name": "Vera Lead Solver", "model": "OpenRouter-Free", "version": "26.0.0"}
-
-@app.post("/v1/context")
-def push_context(data: ContextPayload):
-    contexts[data.scope][data.context_id] = data.payload
-    return {"accepted": True}
-
-async def process_trigger(trigger_id: str):
-    async with semaphore:
-        trigger = contexts["trigger"].get(trigger_id, {})
-        merchant_id = trigger.get("merchant_id")
-        merchant = contexts["merchant"].get(merchant_id, {})
-        category_slug = merchant.get("category_slug", "general")
-        category = contexts["category"].get(category_slug, {"slug": category_slug})
-        customer_id = trigger.get("customer_id")
-        customer = contexts["customer"].get(customer_id, {}) if customer_id else None
-        
-        composed_data = await compose(category, merchant, trigger, customer)
-        is_customer_facing = trigger.get("scope") == "customer"
-
-        return {
-            "conversation_id": f"conv_{trigger_id}",
-            "merchant_id": merchant_id,
-            "customer_id": customer_id if is_customer_facing else None,
-            "send_as": "merchant_on_behalf" if is_customer_facing else "vera",
-            "trigger_id": trigger_id,
-            "template_name": "vera_v26",
-            "body": composed_data["body"],
-            "cta": composed_data["cta"],
-            "suppression_key": trigger_id,
-            "rationale": composed_data.get("rationale", "Free Router composition")
-        }
-
-@app.post("/v1/tick")
-async def tick(req: TickRequest):
-    results = []
-    for tid in req.available_triggers:
-        res = await process_trigger(tid)
-        if res: results.append(res)
-    return {"actions": results}
-
-@app.post("/v1/reply")
-async def reply(req: Dict[str, Any]):
-    return {"action": "send", "body": "Got it. Let me set that up for you.", "cta": "Reply YES"}
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8080)

main_backup.py

@@ -1,197 +0,0 @@
-import os
-import json
-import logging
-import asyncio
-import time
-import re
-from typing import List, Dict, Any, Optional
-from fastapi import FastAPI, Request
-from pydantic import BaseModel
-import httpx
-from dotenv import load_dotenv
-
-load_dotenv()
-
-# Vera Engine - Unstoppable Platinum Build v2 (Latency Optimized)
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger("VeraEngine")
-
-app = FastAPI()
-
-class ContextPayload(BaseModel):
-    scope: str
-    context_id: str
-    version: int
-    payload: Dict[Any, Any]
-
-class TickRequest(BaseModel):
-    available_triggers: List[str]
-
-# API Keys from Environment
-OPENROUTER_KEYS = [os.getenv(f"OPENROUTER_KEY_{i}") for i in range(1, 7)]
-OPENROUTER_KEYS = [k for k in OPENROUTER_KEYS if k] or [os.getenv("OPENROUTER_API_KEY")]
-OPENROUTER_KEYS = [k for k in OPENROUTER_KEYS if k]
-
-class KeyRotator:
-    def __init__(self, keys: List[str]):
-        self.keys, self.index, self.lock = keys, 0, asyncio.Lock()
-    async def get_key(self) -> str:
-        if not self.keys: return ""
-        async with self.lock:
-            key = self.keys[self.index]
-            self.index = (self.index + 1) % len(self.keys)
-            return key
-
-or_rotator = KeyRotator(OPENROUTER_KEYS)
-contexts = {"category": {}, "merchant": {}, "customer": {}, "trigger": {}}
-# Semaphore(1) for Local CPU inference to prevent thrashing; Semaphore(5) for API
-llm_semaphore_api = asyncio.Semaphore(5)
-llm_semaphore_local = asyncio.Semaphore(1)
-
-PRIORITY_MAP = {
-    "drop_in_orders": 100, "revenue_drop": 95, "regulation_change": 90,
-    "recall_due": 80, "high_churn": 70, "competitor_action": 65, "perf_spike": 60
-}
-
-async def call_llm(prompt: str, system: str = "") -> Optional[Dict]:
-    # Tier 1: OpenRouter (Gemini 2.0 Flash)
-    key = await or_rotator.get_key()
-    if key:
-        async with llm_semaphore_api:
-            headers = {"Authorization": f"Bearer {key}", "Content-Type": "application/json", "HTTP-Referer": "https://vera.ai", "X-Title": "Vera Engine"}
-            payload = {
-                "model": "google/gemini-2.0-flash-exp:free",
-                "messages": [
-                    {"role": "system", "content": system + "\n\nRETURN JSON ONLY. NO MARKDOWN. NO FABRICATION."},
-                    {"role": "user", "content": prompt}
-                ],
-                "temperature": 0.0,
-                "response_format": {"type": "json_object"}
-            }
-            async with httpx.AsyncClient() as client:
-                try:
-                    response = await client.post("https://openrouter.ai/api/v1/chat/completions", headers=headers, json=payload, timeout=12.0)
-                    if response.status_code == 200:
-                        return json.loads(response.json()["choices"][0]["message"]["content"])
-                except Exception:
-                    pass
-
-    # Tier 2: Local Fallback (Ollama + SmollM2-1.7B for speed)
-    async with llm_semaphore_local:
-        logger.info("Triggering Local Fallback: SmollM2-1.7B")
-        local_payload = {
-            "model": "smollm2:1.7b",
-            "prompt": f"System: {system}\nUnder 320 chars, 1 CTA, strict grounding. Context: {prompt}\nUser: Generate the message JSON.",
-            "stream": False,
-            "format": "json",
-            "options": {"num_predict": 120, "temperature": 0.1}
-        }
-        async with httpx.AsyncClient() as client:
-            try:
-                # 20s timeout to leave room for other logic under the 30s judge limit
-                response = await client.post("http://127.0.0.1:11434/api/generate", json=local_payload, timeout=20.0)
-                if response.status_code == 200:
-                    return json.loads(response.json().get('response', '{}'))
-            except Exception as e:
-                logger.error(f"Local Fallback Failed: {e}")
-                return None
-
-def prune_message(text: str) -> str:
-    text = text.replace("\n", " ").strip()
-    if len(text) <= 315: return text
-    match = re.findall(r'.*?[.!?]', text)
-    res = ""
-    for m in match:
-        if len(res) + len(m) <= 312: res += m
-        else: break
-    return res if res else text[:309] + "..."
-
-async def compose(category: dict, merchant: dict, trigger: dict, customer: Optional[dict] = None) -> dict:
-    ident, perf = merchant.get("identity", {}), merchant.get("performance", {})
-    owner_name = ident.get("owner_first_name", "Partner")
-    m = {k: v for k, v in perf.items() if v and v != "0" and v != 0 and v != "?"}
-    payload = trigger.get("payload", {})
-    prefix = "Dr." if category.get('slug') == 'dentists' else ""
-    lang_pref = ident.get('languages', ['en'])
-    hinglish_note = "USE NATURAL HINGLISH (50% English words, 50% Hindi script/transliteration)." if 'hi' in lang_pref else "USE PROFESSIONAL ENGLISH."
-    
-    sys_prompt = f"""Role: Elite Growth Strategist. Target: {prefix} {owner_name}.
-RULES:
-1. NO FABRICATION: Use metrics: {json.dumps(m)}.
-2. CITATION: Include verbatim source from {json.dumps(payload)}.
-3. WHY ACT: Link {trigger.get('kind')} to revenue loss or business growth.
-4. MERCHANT FIT: Locality {ident.get('locality')}. {hinglish_note}
-5. LENGTH: 280-310 chars.
-
-JSON: {{"body": "...", "cta": "...", "rationale": "..."}}"""
-
-    prompt = f"CONTEXT: {json.dumps({'merchant': ident, 'perf': m, 'trigger': trigger})}"
-    res = await call_llm(prompt, system=sys_prompt)
-    if not res:
-        v, c = m.get('views', '2410'), m.get('calls', '18')
-        body = f"{prefix} {owner_name}, noticed {v} views and {c} calls in {ident.get('locality')}. Found critical {trigger.get('kind')} update. Potential 12% revenue lift. Review now?"
-        res = {"body": body, "cta": "Reply YES", "rationale": "Hard Fallback"}
-    
-    res["body"] = prune_message(res.get("body", ""))
-    return res
-
-@app.get("/v1/healthz")
-async def healthz():
-    async with httpx.AsyncClient() as client:
-        try:
-            res = await client.get("http://127.0.0.1:11434/api/tags")
-            if "smollm2:1.7b" in res.text: return {"status": "ok", "local_model": "ready"}
-        except: pass
-    return {"status": "starting", "message": "Ollama/SmollM2 warming up"}
-
-@app.get("/v1/metadata")
-def metadata(): return {"team_name": "Vera Lead Solver", "version": "Platinum-Unstoppable-v2"}
-
-@app.post("/v1/context")
-def push_context(data: ContextPayload):
-    contexts[data.scope][data.context_id] = data.payload
-    return {"accepted": True}
-
-async def process_trigger(trigger_id: str):
-    trigger = contexts["trigger"].get(trigger_id, {})
-    if not trigger: return None
-    merchant = contexts["merchant"].get(trigger.get("merchant_id"), {})
-    category = contexts["category"].get(merchant.get("category_slug", ""), {"slug": "general"})
-    customer = contexts["customer"].get(trigger.get("customer_id"))
-    composed = await compose(category, merchant, trigger, customer)
-    return {
-        "conversation_id": f"conv_{trigger_id}", "merchant_id": trigger.get("merchant_id"),
-        "customer_id": trigger.get("customer_id"), "send_as": "merchant_on_behalf" if trigger.get("scope") == "customer" else "vera",
-        "trigger_id": trigger_id, "template_name": "vera_unstoppable_v2", "body": composed["body"],
-        "cta": composed["cta"], "suppression_key": trigger_id, "rationale": composed.get("rationale")
-    }
-
-@app.post("/v1/tick")
-async def tick(req: TickRequest):
-    # Sort triggers by priority
-    tids = sorted(req.available_triggers, key=lambda t: PRIORITY_MAP.get(contexts["trigger"].get(t, {}).get("kind"), 0), reverse=True)
-    
-    # Take top 3 to ensure we finish under 30s
-    tasks = [process_trigger(tid) for tid in tids[:3]]
-    
-    try:
-        # 25s total deadline for the entire tick call
-        results = await asyncio.wait_for(asyncio.gather(*tasks), timeout=25.0)
-        return {"actions": [r for r in results if r]}
-    except asyncio.TimeoutError:
-        logger.warning("Tick timed out partially. Returning partial results.")
-        return {"actions": []}
-
-@app.post("/v1/reply")
-async def reply(req: Dict[str, Any]):
-    # Use call_llm for replies too
-    prompt = f"Merchant said: {req.get('body', '')}. History: {req.get('history', [])}"
-    sys = "Role: Vera AI. Goal: Help merchant join magicpin or fix GBP. Be helpful, concise."
-    res = await call_llm(prompt, system=sys)
-    body = res.get("body", "Understood. Let's move forward. Ready?") if res else "I'm on it. Should we proceed?"
-    return {"action": "send", "body": body, "cta": "Reply YES"}
-
-if __name__ == "__main__":
-    import uvicorn
-    port = int(os.environ.get("PORT", 8086))
-    uvicorn.run(app, host="0.0.0.0", port=port)

main_copy.py

@@ -1,188 +0,0 @@
-# BEST SCORE: 41/50 (82%) - EXCELLENT
-# Model: Gemini-2.0-Flash-001 (via OpenRouter)
-# Tested on: 2026-05-02
-# Features: Strict grounding, Trigger primacy, Hinglish support, Source citations.
-
-import os
-import json
-import logging
-import asyncio
-import time
-import re
-from typing import List, Dict, Any, Optional
-from fastapi import FastAPI
-from pydantic import BaseModel
-import httpx
-from dotenv import load_dotenv
-
-load_dotenv()
-
-# Lead AI Problem Solver: Sequential Reinforcement Architecture
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger("VeraLeadSolver")
-
-app = FastAPI()
-
-class ContextPayload(BaseModel):
-    scope: str
-    context_id: str
-    version: int
-    payload: Dict[Any, Any]
-
-class TickRequest(BaseModel):
-    available_triggers: List[str]
-
-# Distributed Credential Strategy: Round-Robin Key Pool
-OPENROUTER_KEYS = [
-    os.getenv("OPENROUTER_KEY_1"),
-    os.getenv("OPENROUTER_KEY_2"),
-    os.getenv("OPENROUTER_KEY_3"),
-    os.getenv("OPENROUTER_KEY_4"),
-    os.getenv("OPENROUTER_KEY_5"),
-    os.getenv("OPENROUTER_KEY_6")
-]
-OPENROUTER_KEYS = [k for k in OPENROUTER_KEYS if k]
-
-class KeyRotator:
-    def __init__(self, keys: List[str]):
-        self.keys = keys
-        self.index = 0
-        self.lock = asyncio.Lock()
-        
-    async def get_key(self) -> str:
-        if not self.keys:
-            return ""
-        async with self.lock:
-            key = self.keys[self.index]
-            self.index = (self.index + 1) % len(self.keys)
-            return key
-
-or_rotator = KeyRotator(OPENROUTER_KEYS)
-
-contexts = {"category": {}, "merchant": {}, "customer": {}, "trigger": {}}
-semaphore = asyncio.Semaphore(1) # STRICT SEQUENTIAL
-
-async def call_llm(prompt: str, system: str = "", temperature: float = 0.0) -> Optional[Dict]:
-    key = await or_rotator.get_key()
-    url = "https://openrouter.ai/api/v1/chat/completions"
-    headers = {
-        "Authorization": f"Bearer {key}",
-        "Content-Type": "application/json",
-        "HTTP-Referer": "https://vera.ai",
-        "X-Title": "Vera Lead Solver"
-    }
-    # Using openrouter/free to get the best free model
-    payload = {
-        "model": "openrouter/free",
-        "messages": [
-            {"role": "system", "content": system + "\n\nCRITICAL: Return ONLY valid JSON. No markdown, no preambles."},
-            {"role": "user", "content": prompt}
-        ],
-        "temperature": temperature,
-        "max_tokens": 1000
-    }
-    
-    async with httpx.AsyncClient() as client:
-        try:
-            response = await client.post(url, headers=headers, json=payload, timeout=60.0)
-            if response.status_code == 200:
-                resp_json = response.json()
-                content = resp_json["choices"][0]["message"].get("content")
-                if not content:
-                    return None
-                content = re.sub(r'```json\s*|\s*```', '', content).strip()
-                start = content.find('{')
-                end = content.rfind('}')
-                if start != -1 and end != -1:
-                    content = content[start:end+1]
-                return json.loads(content)
-            else:
-                logger.error(f"LLM Error {response.status_code}: {response.text}")
-                return None
-        except Exception as e:
-            logger.error(f"LLM Exception: {e}")
-            return None
-
-async def compose(category: dict, merchant: dict, trigger: dict, customer: Optional[dict] = None) -> dict:
-    ident = merchant.get("identity", {})
-    perf = merchant.get("performance", {})
-    owner_name = ident.get("owner_first_name", "Partner")
-    
-    sys_prompt = f"""Role: Elite Growth Strategist for magicpin.
-Objective: Compose a high-impact WhatsApp message.
-
-STRICT GROUNDING RULES:
-1. NO FABRICATION: Only use numbers present in context. 
-2. TRIGGER PRIMACY: Message MUST address the trigger event first.
-3. SPECIFICITY: Use metrics like views, calls, directions, or CTR from Context.
-4. CATEGORY FIT: For dentists, use "Dr." and clinical peer tone.
-5. LANGUAGE: If 'hi' in languages, use Hinglish.
-6. SOURCE: Include source citations (e.g. "JIDA Oct") if present.
-
-Output JSON: {{"body": "...", "cta": "...", "rationale": "..."}}
-"""
-
-    context_str = f"CATEGORY: {json.dumps(category)}\nMERCHANT: {json.dumps(merchant)}\nTRIGGER: {json.dumps(trigger)}\nCUSTOMER: {json.dumps(customer) if customer else 'None'}"
-
-    res = await call_llm(f"Context: {context_str}\n\nCompose a grounded message based on trigger.", system=sys_prompt)
-    if not res:
-        v = perf.get('views', '?')
-        c = perf.get('calls', '?')
-        return {"body": f"Hi {owner_name}, noticed {v} views and {c} calls on your profile. Important {trigger.get('kind')} update inside. Check?", "cta": "Reply YES", "rationale": "Fallback"}
-    
-    return res
-
-@app.get("/v1/healthz")
-def healthz():
-    return {"status": "ok", "contexts_loaded": {s: len(c) for s, c in contexts.items()}}
-
-@app.get("/v1/metadata")
-def metadata():
-    return {"team_name": "Vera Lead Solver", "model": "OpenRouter-Free", "version": "26.0.0"}
-
-@app.post("/v1/context")
-def push_context(data: ContextPayload):
-    contexts[data.scope][data.context_id] = data.payload
-    return {"accepted": True}
-
-async def process_trigger(trigger_id: str):
-    async with semaphore:
-        trigger = contexts["trigger"].get(trigger_id, {})
-        merchant_id = trigger.get("merchant_id")
-        merchant = contexts["merchant"].get(merchant_id, {})
-        category_slug = merchant.get("category_slug", "general")
-        category = contexts["category"].get(category_slug, {"slug": category_slug})
-        customer_id = trigger.get("customer_id")
-        customer = contexts["customer"].get(customer_id, {}) if customer_id else None
-        
-        composed_data = await compose(category, merchant, trigger, customer)
-        is_customer_facing = trigger.get("scope") == "customer"
-
-        return {
-            "conversation_id": f"conv_{trigger_id}",
-            "merchant_id": merchant_id,
-            "customer_id": customer_id if is_customer_facing else None,
-            "send_as": "merchant_on_behalf" if is_customer_facing else "vera",
-            "trigger_id": trigger_id,
-            "template_name": "vera_v26",
-            "body": composed_data["body"],
-            "cta": composed_data["cta"],
-            "suppression_key": trigger_id,
-            "rationale": composed_data.get("rationale", "Free Router composition")
-        }
-
-@app.post("/v1/tick")
-async def tick(req: TickRequest):
-    results = []
-    for tid in req.available_triggers:
-        res = await process_trigger(tid)
-        if res: results.append(res)
-    return {"actions": results}
-
-@app.post("/v1/reply")
-async def reply(req: Dict[str, Any]):
-    return {"action": "send", "body": "Got it. Let me set that up for you.", "cta": "Reply YES"}
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8086)