Prompts updated. llm finetuned model used

app/ads1/ads_analyst.py

@@ -3,6 +3,7 @@ import json
 import pandas as pd
 
 from app.recs.generate import TARGET_CPL, generate_explanation, is_bad_llm_output
+from app.ads1.prompt_templates import ads_analyst_prompt
 
 # -------------------------
 # 1. DATA BUILDERS
@@ -133,11 +134,7 @@ def build_ads_analyst_context(dfs: dict, campaign_name: str | None = None) -> di
 def build_ads_analyst_prompt(context: dict) -> str:
     payload = json.dumps(context, indent=2, default=str)
     name = context.get("campaign_name", "this campaign")
-    return (
-        f"Write 3 to 5 bullet points of actionable Google Ads insights for {name}.\n"
-        "Use simple language. One insight per bullet. Start each line with '- '. No intro sentence.\n\n"
-        f"Data (JSON):\n{payload}"
-    )
+    return ads_analyst_prompt(name, payload)
 
 
 def rule_based_insights(context: dict) -> str:

app/ads1/budget_optimizer.py

@@ -1,6 +1,4 @@
-import json
 import pandas as pd
-from app.recs.generate import generate_explanation, is_bad_llm_output
 
 def build_budget_features(df: pd.DataFrame) -> pd.DataFrame:
     df = df.copy()
@@ -75,18 +73,6 @@ def build_budget_optimizer_context(dfs: dict, campaign_name: str | None = None):
         "budget_actions": action_rows.round(2).to_dict("records")
     }
 
-def build_budget_optimizer_prompt(context: dict) -> str:
-    payload = json.dumps(context, indent=2, default=str)
-    name = context.get("campaign_name", "this account")
-
-    return (
-        f"Write 3 to 5 bullet points of actionable budget optimization insights for {name}.\n"
-        "Use the budget_actions list only. Each bullet must mention the campaign or keyword name, the budget action, and the evidence.\n"
-        "Use simple language. One self-contained budget action per bullet. Start each line with '- '. No intro sentence. Do not quote JSON values by themselves.\n\n"
-        f"Data (JSON):\n{payload}"
-    )
-
-
 def rule_based_budget_actions(context: dict) -> str:
     rows = context.get("budget_actions", [])
     if not rows:
@@ -124,18 +110,5 @@ def run_budget_optimizer(dfs: dict, campaign_name: str | None = None) -> str:
         return "⚠️ No campaign data available."
 
     context = build_budget_optimizer_context(dfs, campaign_name)
-
     print("🧠 [budget_optimizer] context built", flush=True)
-
-    prompt = build_budget_optimizer_prompt(context)
-
-    print("✍️ [budget_optimizer] prompt built", flush=True)
-
-    result = generate_explanation(prompt)
-
-    if is_bad_llm_output(result) or not result.strip().startswith("-") or result.count('"') >= 4:
-        print("⚠️ [budget_optimizer] fallback triggered", flush=True)
-        return rule_based_budget_actions(context)
-
-    print("📤 [budget_optimizer] result received", flush=True)
-    return result
+    return rule_based_budget_actions(context)

app/ads1/growth_finder.py

@@ -1,6 +1,4 @@
-import json
 import pandas as pd
-from app.recs.generate import generate_explanation, is_bad_llm_output
 
 def build_growth_finder_features(df: pd.DataFrame) -> pd.DataFrame:
     df = df.copy()
@@ -60,19 +58,6 @@ def build_growth_finder_context(dfs: dict, campaign_name: str | None = None):
         "growth_candidates": df[keep_cols].round(2).to_dict("records")
     }
 
-def build_growth_finder_prompt(context: dict) -> str:
-    payload = json.dumps(context, indent=2, default=str)
-    name = context.get("campaign_name", "this account")
-
-    return (
-        f"Write 3 to 5 bullet points of actionable growth opportunities for {name}.\n"
-        "Use the growth_candidates list only. Suggest ways to scale winners, expand related intent, or increase budget on efficient areas.\n"
-        "Each bullet must mention the keyword, the growth action, and the evidence. Do not list weak keywords or diagnose poor performance.\n"
-        "Use simple language. One self-contained growth opportunity per bullet. Start each line with '- '. No intro sentence. Do not quote JSON values by themselves.\n\n"
-        f"Data (JSON):\n{payload}"
-    )
-
-
 def rule_based_growth_actions(context: dict) -> str:
     rows = context.get("growth_candidates", [])
     if not rows:
@@ -102,20 +87,5 @@ def run_growth_finder(dfs: dict, campaign_name: str | None = None) -> str:
         return "⚠️ No keyword data available."
 
     context = build_growth_finder_context(dfs, campaign_name)
-    if not context.get("growth_candidates"):
-        return rule_based_growth_actions(context)
-
     print("🧠 [growth_finder] context built", flush=True)
-
-    prompt = build_growth_finder_prompt(context)
-
-    print("✍️ [growth_finder] prompt built", flush=True)
-
-    result = generate_explanation(prompt)
-
-    if is_bad_llm_output(result) or not result.strip().startswith("-") or result.count('"') >= 4:
-        print("⚠️ [growth_finder] fallback triggered", flush=True)
-        return rule_based_growth_actions(context)
-
-    print("📤 [growth_finder] result received", flush=True)
-    return result
+    return rule_based_growth_actions(context)

app/ads1/keyword_inspector.py

@@ -2,6 +2,7 @@ import json
 import pandas as pd
 
 from app.recs.generate import generate_explanation, is_bad_llm_output
+from app.ads1.prompt_templates import keyword_inspector_prompt
 
 
 # -------------------------
@@ -27,12 +28,7 @@ def build_keyword_features(df: pd.DataFrame) -> pd.DataFrame:
 def build_keyword_prompt(context: dict) -> str:
     payload = json.dumps(context, indent=2, default=str)
 
-    return (
-        "Write 3 to 5 bullet points of actionable keyword performance insights.\n"
-        "Classify individual keywords as winning, wasted spend, or scaling opportunities using CTR, cost, and conversions.\n"
-        "Use simple language. One insight per bullet. Start each line with '- '. No intro sentence.\n\n"
-        f"Data (JSON):\n{payload}"
-    )
+    return keyword_inspector_prompt(payload)
 # -------------------------
 # Main runner
 # -------------------------

app/ads1/prompt_templates.py

@@ -0,0 +1,25 @@
+def ads_analyst_prompt(name: str, payload: str) -> str:
+    return (
+        f"Write 3 to 5 bullet points of actionable Google Ads insights for {name}.\n"
+        "Use simple language. One insight per bullet. Start each line with '- '. No intro sentence.\n\n"
+        f"Data (JSON):\n{payload}"
+    )
+
+
+def keyword_inspector_prompt(payload: str) -> str:
+    return (
+        "Write 3 to 5 bullet points of actionable keyword performance insights.\n"
+        "Classify individual keywords as winning, wasted spend, or scaling opportunities using CTR, cost, and conversions.\n"
+        "Use simple language. One insight per bullet. Start each line with '- '. No intro sentence.\n\n"
+        f"Data (JSON):\n{payload}"
+    )
+
+
+def search_term_cleaner_prompt(name: str, payload: str) -> str:
+    return (
+        f"Write 3 to 5 bullet points of actionable search term cleanup insights for {name}.\n"
+        "Classify search terms as wasted spend, high intent, scale, or negative keyword candidates using total_cost, clicks, conversions, CPA, CVR, and CPC.\n"
+        "Use simple language. One search term action per bullet. Start each line with '- '. No intro sentence.\n\n"
+        f"Data (JSON):\n{payload}"
+    )
+

app/ads1/search_term_optimizer.py

@@ -1,6 +1,7 @@
 import json
 import pandas as pd
 from app.recs.generate import generate_explanation, is_bad_llm_output
+from app.ads1.prompt_templates import search_term_cleaner_prompt
 
 # -------------------------
 # Feature engineering only
@@ -65,12 +66,7 @@ def build_search_optimizer_prompt(context: dict) -> str:
     payload = json.dumps(context, indent=2, default=str)
     name = context.get("campaign_name", "this campaign")
 
-    return (
-        f"Write 3 to 5 bullet points of actionable search term cleanup insights for {name}.\n"
-        "Use the search_terms list only. total_cost is total spend for that search term; cpc is cost per click.\n"
-        "Each bullet must mention the search term, the action, and the evidence. Use simple language. Start each line with '- '. No intro sentence.\n\n"
-        f"Data (JSON):\n{payload}"
-    )
+    return search_term_cleaner_prompt(name, payload)
 
 
 # -------------------------

app/models/llm.py

@@ -6,8 +6,11 @@ from typing import Any
 
 from huggingface_hub import hf_hub_download
 
-HF_REPO = os.getenv("LLAMA_HF_REPO", "openbmb/MiniCPM5-1B-GGUF")
-HF_FILENAME = os.getenv("LLAMA_HF_FILENAME", "MiniCPM5-1B-Q4_K_M.gguf")
+# HF_REPO = os.getenv("LLAMA_HF_REPO", "openbmb/MiniCPM5-1B-GGUF")
+# HF_FILENAME = os.getenv("LLAMA_HF_FILENAME", "MiniCPM5-1B-Q4_K_M.gguf")
+
+LLAMA_HF_REPO = os.getenv("LLAMA_HF_REPO", "ps1811/advisor-minicpm-finetuned-gguf")
+LLAMA_HF_FILENAME= os.getenv("LLAMA_HF_FILENAME", "advisor-minicpm-q4_k_m.gguf")
 
 _model: Any = None
 _init_lock = threading.Lock()