Update marketing_graph.py

marketing_graph.py

@@ -1,7 +1,7 @@
-# marketing_graph.py
 import os
 from langgraph.graph import StateGraph, START, END
 from langchain_core.tracers.context import tracing_v2_enabled
+from duckduckgo_search import DDGS  # 🦆 for web search
 
 # ----------------------------
 # LangSmith / LangGraph setup
@@ -10,16 +10,33 @@ os.environ["LANGCHAIN_TRACING_V2"] = "true"
 os.environ["LANGCHAIN_PROJECT"] = "web_Scrapping_agent21"
 os.environ["LANGCHAIN_API_KEY"] = "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"
 
+# ----------------------------
+# Helper: DuckDuckGo Search
+# ----------------------------
+def duckduckgo_search(query, max_results=3):
+    """Fetch top web snippets using DuckDuckGo."""
+    try:
+        with DDGS() as ddgs:
+            results = list(ddgs.text(query, max_results=max_results))
+        if not results:
+            return "No results found."
+        return "\n\n".join([f"• {r['title']}\n{r['body']}\nURL: {r['href']}" for r in results])
+    except Exception as e:
+        return f"[DuckDuckGo Error] {e}"
+
 # ----------------------------
 # Node functions
 # ----------------------------
 def market_research(state):
     product_info = state.get("input_data", "")
+    search_query = f"market trends, competitors, and insights for {product_info}"
+    web_data = duckduckgo_search(search_query, max_results=5)
+
     return {
         "MarketResearch": (
-            f"Research for: {product_info}\n"
-            "Competitors: FitTrack, SmartPulse, ZenBand\n"
-            "Trends: AI fitness, wearable analytics, app integration."
+            f"### Research for: {product_info}\n"
+            f"🔍 Web Insights:\n{web_data}\n\n"
+            "📊 Summary: The wearable tech market is rapidly expanding with focus on AI-driven health analytics."
         )
     }
 
@@ -29,11 +46,21 @@ def content_planner(state):
 
 def post_generator(state):
     plan_output = state.get("ContentPlanner", "")
-    return {"PostGenerator": "Generated posts for campaign: 'Get Fit with SmartPulse!'"}
+    return {
+        "PostGenerator": (
+            f"Generated posts for campaign: 'Get Fit with SmartPulse!'\n"
+            f"Based on: {plan_output[:100]}..."
+        )
+    }
 
 def analytics_agent(state):
     post_output = state.get("PostGenerator", "")
-    return {"AnalyticsAgent": "Predicted engagement: Instagram > LinkedIn > Twitter. Suggestion: add CTA."}
+    return {
+        "AnalyticsAgent": (
+            f"Predicted engagement: Instagram > LinkedIn > Twitter.\n"
+            f"Suggestion: add CTA and emojis in posts.\n\nBased on content:\n{post_output[:120]}..."
+        )
+    }
 
 # ----------------------------
 # Safe executor wrapper
@@ -85,4 +112,5 @@ if __name__ == "__main__":
         result = compiled_graph.invoke(state)
 
         # Print final result (errors included)
+        print("\n\n=== FINAL RESULT ===")
         print(result)