marketing

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 from rag import rbc_product
 from tool import rival_product
-from graphrag import reasoning
+from graphrag import marketing
 from knowledge import graph
 from pii import derisk
 from classify import judge
@@ -48,6 +48,10 @@ Other Links:
     ================================================
     - Retrieval: Public RBC Product Data
     - Recommend: RBC Product 
+    
+    Potential Optimization
+    ------------
+    BM25 reranking using keyword
     """)
     in_verbatim = gr.Textbox(label="Verbatim")
     out_product = gr.Textbox(label="Product")
@@ -158,14 +162,13 @@ By framing each component as a strategic variable rather than a fixed element, b
       [
         [
           """
-Create marketing campaign that can improve customer acquisition, activation, retention and referral for this persona: 
 Low APR and great customer service. I would highly recommend if you’re looking for a great credit card company and looking to rebuild your credit. I have had my credit limit increased annually and the annual fee is very low.
     """]
       ],
       [in_question]
     )
     btn_recommend = gr.Button("Reasoning")
-    btn_recommend.click(fn=reasoning, inputs=[in_verbatim, in_question], outputs=out_product)
+    btn_recommend.click(fn=marketing, inputs=[in_verbatim, in_question], outputs=out_product)
 
     gr.Markdown("""
 Benefits of a Marketing Campaign Generator

graphrag.py

@@ -89,3 +89,29 @@ def reasoning(text, question):
     traceback.print_exc()
     return str(e)
 
+
+def marketing(text, question):
+  try:
+    print("Generate Knowledgegraph...")
+    graph, graph_documents_filtered = knowledge_graph(f"""Create
+  marketing campaign that can improve customer acquisition, activation, retention and referral for this persona: {text}""")
+
+    print("GraphQAChain...")
+    graph_rag = GraphQAChain.from_llm(
+      llm=llm,
+      graph=graph,
+      verbose=True
+    )
+
+    print("Answering through GraphQAChain...")
+    answer = graph_rag.invoke(question)
+    return answer['result']
+
+  except Exception as e:
+    print(f"An error occurred in process_text: {str(e)}")
+    import traceback
+    traceback.print_exc()
+    return str(e)
+
+if __name__=="__main__":
+  pass