Upgrade Trend Decoder prompt for better quality

core/thunderbird_engine.py

@@ -81,56 +81,59 @@ def predict_niche_trends() -> dict:
     
     return {"trend_predictions": predictions}
 
+
 def decode_market_trend(topic: str, llm_instance) -> Dict[str, str]:
     """
-    Takes a news headline or keyword and generates a strategic 'Agency Brief'.
-    Used for the Trend Decoder feature.
+    Decodes a keyword into a strategy. USES HYBRID AI + TEMPLATE LOGIC.
     """
-    print(f"🧠 [Thunderbird] Decoding Trend: {topic}")
+    print(f"🧠 [Thunderbird] Decoding Trend with Upgraded Prompt: {topic}")
     
-    # Fallback default agar LLM nahi hai
-    default_response = {
-        "summary": f"'{topic}' is currently gaining significant traction online.",
-        "impact": "Brands tapping into this can see increased organic reach.",
-        "strategy": "Create content relating to this topic immediately using your brand voice."
-    }
+    # --- 1. Smart Fallback (Same as before) ---
+    base_template = FALLBACK_STRATEGIES["Default"]
+    topic_lower = topic.lower()
+    if "ai" in topic_lower: base_template = FALLBACK_STRATEGIES["AI"]
+    elif "tiktok" in topic_lower: base_template = FALLBACK_STRATEGIES["TikTok"]
+    elif "micro" in topic_lower: base_template = FALLBACK_STRATEGIES["Micro"]
 
     if not llm_instance:
-        return default_response
+        return base_template
 
-    # Prompt Strategy: Agency Expert Persona
-    prompt = f"""[INST] You are a senior Digital Strategy Director. 
-    Analyze this trending topic: "{topic}".
+    # --- 2. THE UPGRADED PROMPT (This is the fix) ---
+    # We give it today's date and a better persona.
+    today_date = datetime.now().strftime("%Y-%m-%d")
+    prompt = f"""[INST]
+    You are "PulseAI", a Senior Digital Strategy Director providing a competitive edge.
+    Today's Date is {today_date}. All your analysis must be modern and relevant to today.
     
-    Provide a briefing for your marketing team in strictly JSON format with these 3 keys:
-    1. "summary": (What is happening? 1 sentence)
-    2. "impact": (Why should a Brand/Agency care? 1 sentence)
-    3. "strategy": (One specific actionable content idea or advice)
+    Analyze this trend: "{topic}".
+    
+    Provide a concise, expert briefing in a valid JSON format with 3 keys:
+    1. "summary": A single, sharp sentence explaining what is happening now.
+    2. "impact": A single sentence explaining why this matters for a brand's revenue or reach.
+    3. "strategy": One creative, specific content idea an agency can execute this week. Example: "A 30s reel comparing old vs. new AI tools."
 
     JSON Response:
     [/INST]"""
 
     try:
-        response = llm_instance(prompt, max_tokens=256, temperature=0.7, stop=["[INST]"], echo=False)
+        response = llm_instance(prompt, max_tokens=256, temperature=0.6, stop=["[INST]"], echo=False)
         text = response['choices'][0]['text'].strip()
         
-        try:
-            # Try finding the first { and last } to parse JSON
-            start = text.find('{')
-            end = text.rfind('}') + 1
-            if start != -1 and end != 0:
-                data = json.loads(text[start:end])
-                return {
-                    "summary": data.get("summary", default_response["summary"]),
-                    "impact": data.get("impact", default_response["impact"]),
-                    "strategy": data.get("strategy", default_response["strategy"])
-                }
-        except:
-            pass
-            
-        # Fallback if JSON parsing fails but we have text - Return a standard structure
-        return default_response
+        # Parse the JSON from the response text
+        start = text.find('{')
+        end = text.rfind('}') + 1
+        if start != -1 and end != 0:
+            data = json.loads(text[start:end])
+            # Ensure no key is empty, fall back to template if needed
+            return {
+                "summary": data.get("summary") or base_template["summary"],
+                "impact": data.get("impact") or base_template["impact"],
+                "strategy": data.get("strategy") or base_template["strategy"]
+            }
+        else:
+            # If JSON is broken, use the fallback
+            raise ValueError("LLM did not return valid JSON.")
 
     except Exception as e:
-        print(f"❌ AI Trend Decoding Failed: {e}")
-        return default_response
+        print(f"   - ⚠️ LLM Error or Bad JSON (Using Fallback): {e}")
+        return base_template