Update logs

app.py

@@ -268,10 +268,15 @@ def text_to_text_generation(verbose, prompt):
         match response_num:
             case 0:
                 prompt = f"[INST] {prompt}\n Lets think step by step. [/INST] {start_template}"
-
+                print(prompt)
             case 1:
                 if retriever == "semantic_search":
                     question = prompt
+                    print(
+                        (
+                        f"You are a helpful kubernetes professional. [INST] Use the following documentation, if it is relevant to answer the question below. [/INST]\nDocumentation: [RETRIEVED_RESULTS_FROM_BOOK] [INST] Answer the following question: {question} [/INST]\nAnswer:  \n")
+
+                    )
                     retrieved_results = semantic_search(prompt)
                     prompt = f"You are a helpful kubernetes professional. [INST] Use the following documentation, if it is relevant to answer the question below. [/INST]\nDocumentation: {retrieved_results} </s>\n<s> [INST] Answer the following question: {prompt} [/INST]\nAnswer: "
 
@@ -288,6 +293,12 @@ def text_to_text_generation(verbose, prompt):
                     question = prompt
                     prompt = f"You are a helpful kubernetes professional. [INST] Use the following documentation, if it is relevant to answer the question below. [/INST]\nDocumentation: {retrieved_results} </s>\n<s> [INST] Answer the following question: {prompt} [/INST]\nAnswer: "
 
+                    print(
+                        (
+                            f"You are a helpful kubernetes professional. [INST] Use the following documentation, if it is relevant to answer the question below. [/INST]\nDocumentation: [RETRIEVED_RESULTS_FROM_GOOGLE] [INST] Answer the following question: {question} [/INST]\nAnswer:  \n" 
+                        )
+                    )
+
                     md = (
                         f"### Step 1: Preparing prompt for additional documentation\n"
                         f"You are a helpful kubernetes professional. [INST] Use the following documentation, if it is relevant to answer the question below. [/INST]\nDocumentation:\n"
@@ -298,9 +309,11 @@ def text_to_text_generation(verbose, prompt):
                     )
                 else:
                     prompt = f"[INST] Answer the following question: {prompt} [/INST]\nAnswer: "
+                    print(prompt)
 
             case _:
                 prompt = f"[INST] {prompt} [/INST]"
+                print(prompt)
 
         return prompt, md
 
@@ -341,13 +354,12 @@ def text_to_text_generation(verbose, prompt):
 
     if response_num == 2:
         prompt, md = create_generation_prompt(response_num, prompt, False)
-        print('Prompt given to model:\n' + prompt + '\n')
         original, new = generate_batch(prompt)[0]
         prompt, response = cleanup(original, new)
         if verbose:
             return (
                 f"# 📚KubeWizard📚\n"
-                f"A helpful Kubernetes Assistant powered by Component Soft\n"
+                f"#### A helpful Kubernetes Assistant powered by Component Soft\n"
                 f"--------------------------------------------\n"
                 f"# Classified your prompt as:\n"
                 f"{modes}\n\n" 
@@ -358,7 +370,7 @@ def text_to_text_generation(verbose, prompt):
         else:
             return (
                 f"# 📚KubeWizard📚\n"
-                f"A helpful Kubernetes Assistant powered by Component Soft\n"
+                f"#### A helpful Kubernetes Assistant powered by Component Soft\n"
                 f"--------------------------------------------\n"
                 f"# Classified your prompt as:\n"
                 f"{modes}\n\n" 
@@ -367,14 +379,13 @@ def text_to_text_generation(verbose, prompt):
 
     if response_num == 0:
         prompt, md = create_generation_prompt(response_num, prompt, False)
-        print('Prompt given to model:\n' + prompt + '\n')
         original, new = generate_batch(prompt)[0]
         prompt, response = cleanup(original, new)
         model_response = new[len(original):].strip()
         if verbose:
             return (
                 f"# 📚KubeWizard📚\n"
-                f"A helpful Kubernetes Assistant powered by Component Soft\n"
+                f"#### A helpful Kubernetes Assistant powered by Component Soft\n"
                 f"--------------------------------------------\n"
                 f"# Classified your prompt as:\n"
                 f"{modes}\n\n"
@@ -388,7 +399,7 @@ def text_to_text_generation(verbose, prompt):
         else:
             return (
                 f"# 📚KubeWizard📚\n"
-                f"A helpful Kubernetes Assistant powered by Component Soft\n"
+                f"#### A helpful Kubernetes Assistant powered by Component Soft\n"
                 f"--------------------------------------------\n"
                 f"# Classified your prompt as:\n"
                 f"{modes}\n\n"
@@ -396,11 +407,8 @@ def text_to_text_generation(verbose, prompt):
             )
 
     res_prompt, res_md = create_generation_prompt(response_num, prompt, False)
-    print(f'Prompt given to finetuned model:\n{res_prompt}\n')
     res_semantic_search_prompt, res_semantic_search_md = create_generation_prompt(response_num, prompt, "semantic_search")
-    print(f'Prompt given to model with RAG:\n{res_semantic_search_prompt}\n')
     res_google_search_prompt, res_google_search_md = create_generation_prompt(response_num, prompt, "google_search")
-    print(f'Prompt given to model with Google search:\n{res_google_search_prompt}\n')
 
     gen_normal, gen_semantic_search, gen_google_search = generate_batch(
         res_prompt, res_semantic_search_prompt, res_google_search_prompt
@@ -413,7 +421,7 @@ def text_to_text_generation(verbose, prompt):
     if verbose:
         return (
             f"# 📚KubeWizard📚\n"
-            f"A helpful Kubernetes Assistant powered by Component Soft\n"
+            f"#### A helpful Kubernetes Assistant powered by Component Soft\n"
             f"--------------------------------------------\n"
             f"# Classified your prompt as:\n"
             f"{modes}\n\n"
@@ -439,7 +447,7 @@ def text_to_text_generation(verbose, prompt):
     else:
         return (
             f"# 📚KubeWizard📚\n"
-            f"A helpful Kubernetes Assistant powered by Component Soft\n"
+            f"#### A helpful Kubernetes Assistant powered by Component Soft\n"
             f"--------------------------------------------\n"
             f"# Classified your prompt as:\n"
             f"{modes}\n\n"