Update app.py

app.py

@@ -360,6 +360,7 @@ def create_cluster_dataframes(processed_df):
 
 from transformers import GPTNeoForCausalLM, GPT2Tokenizer
 def generate_project_proposal(problem_descriptions, location, problem_domain):
+    print("Trying to access gpt-neo-1.3B")
     model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
     tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
 
@@ -379,6 +380,7 @@ def generate_project_proposal(problem_descriptions, location, problem_domain):
         temperature=0.75)
 
     proposal = tokenizer.decode(output[0], skip_special_tokens=True)
+    print("Successfully accessed gpt-neo-1.3B and returning")
     return proposal
 
 def create_project_proposals(budget_cluster_df, problem_cluster_df, location_clusters, problem_clusters):
@@ -467,9 +469,13 @@ def nlp_pipeline(original_df):
     print("Clustering Done...")
     # return processed_df, budget_cluster_df, problem_cluster_df, location_clusters, problem_clusters
 
+    print("location_clusters: ", location_clusters)
+    print("problem_clusters: ", problem_clusters)
     # # Generate project proposals
     location_clusters = dict(enumerate(processed_df['Location_Category_Words'].unique()))
     problem_clusters = dict(enumerate(processed_df['Problem_Category_Words'].unique()))
+    print("location_clusters: ", location_clusters)
+    print("problem_clusters: ", problem_clusters)
     project_proposals = create_project_proposals(budget_cluster_df, problem_cluster_df, location_clusters, problem_clusters)
 
     console_messages.append("NLP pipeline completed.")