added llm choice per method and fixed typo in anthropic model name

app.py

@@ -4,42 +4,42 @@ from tools.summarize import MAPREDUCE, STUFF, summarize_podcast
 from tools.answer_bot import answer_question
 from tools.aspect_and_sentiment_extraction import extract_aspects_and_sentiment
 
-def get_answer_for(user_question, transcript_file_name, llm_choice):
+def get_answer_for(user_question, transcript_file_name, question_answer_llm_choice):
     if transcript_file_name is None:
         answer_text = "No Transcript Uploaded, Upload RTF File First", ""
-    elif not llm_choice:
+    elif not question_answer_llm_choice:
         answer_text = "No LLM Selected, select one"
     elif not user_question:
         answer_text = "Question Not Given"
     else:    
         # Answer the user's question using the question-answering model
-        answer_text = answer_question(question=user_question, transcript_file_name=transcript_file_name, llm_choice=llm_choice)
+        answer_text = answer_question(question=user_question, transcript_file_name=transcript_file_name, llm_choice=question_answer_llm_choice)
 
     return answer_text.lstrip(), transcript_file_name, llm_choice
 
-def summarize(uploaded_file, transcript_file_name, summarization_method, llm_choice):
+def summarize(uploaded_file, transcript_file_name, summarization_method, summarization_llm_choice):
     if transcript_file_name is None:
         summary = "No Transcript Uploaded, Upload RTF File First", ""
-    elif not llm_choice:
+    elif not summarization_llm_choice:
         summary = "No LLM Selected, select one"
     elif not summarization_method:
         summary = "No Summarization Method Selected , select one"
     else:        
         # Summarize the content
-        summary = summarize_podcast(transcript_file_name=transcript_file_name, summarization_method=summarization_method, llm_choice=llm_choice).lstrip()
+        summary = summarize_podcast(transcript_file_name=transcript_file_name, summarization_method=summarization_method, llm_choice=summarization_llm_choice).lstrip()
 
     return summary, transcript_file_name, summarization_method, llm_choice
 
-def generate_aspects_and_sentiments(uploaded_file, transcript_file_name, llm_choice):
+def generate_aspects_and_sentiments(uploaded_file, transcript_file_name, sentiment_analysis_llm_choice):
     if transcript_file_name is None:
         sentiment = "No Transcript Uploaded, Upload RTF File First", ""
-    elif not llm_choice:
+    elif not sentiment_analysis_llm_choice:
         sentiment = "No LLM Selected, select one"
     else:
         # Aspect-Based Sentiment Analysis
-        sentiment = extract_aspects_and_sentiment(transcript_file_name=transcript_file_name, llm_choice=llm_choice).lstrip()
+        sentiment = extract_aspects_and_sentiment(transcript_file_name=transcript_file_name, llm_choice=sentiment_analysis_llm_choice).lstrip()
 
-    return sentiment, transcript_file_name, llm_choice
+    return sentiment, transcript_file_name, sentiment_analysis_llm_choice
 
 def setup_rtf_file_handle(uploaded_file, transcript_file_name):
     if not uploaded_file:
@@ -49,20 +49,20 @@ def setup_rtf_file_handle(uploaded_file, transcript_file_name):
         status = "Upload Success"
     return status, transcript_file_name
 
-def setup_summarization_llm(choice, llm_choice):
+def setup_summarization_llm(choice, summarization_llm_choice):
     set_summarization_llm(choice)
-    llm_choice = choice
-    return choice, llm_choice
+    summarization_llm_choice = choice
+    return choice, summarization_llm_choice
 
-def setup_sentiment_analysis_llm(choice, llm_choice):
+def setup_sentiment_analysis_llm(choice, sentiment_analysis_llm_choice):
     set_sentiment_analysis_llm(choice)
-    llm_choice = choice
-    return choice, llm_choice
+    sentiment_analysis_llm_choice = choice
+    return choice, sentiment_analysis_llm_choice
 
-def setup_question_answer_llm(choice, llm_choice):
+def setup_question_answer_llm(choice, question_answer_llm_choice):
     set_question_answer_llm(choice)
-    llm_choice = choice
-    return choice, llm_choice
+    question_answer_llm_choice = choice
+    return choice, question_answer_llm_choice
 
 def setup_summarization_method(choice, summarization_method):
     summarization_method = choice
@@ -76,7 +76,9 @@ summarize_method_choices = [MAPREDUCE, STUFF]
 with gr.Blocks() as demo:
     transcript_file_name = gr.State()
     summarization_method = gr.State()
-    llm_choice = gr.State()
+    question_answer_llm_choice = gr.State()
+    sentiment_analysis_llm_choice = gr.State()
+    summarization_llm_choice = gr.State()
     with gr.Group("Upload RTF File"):
         rtf_file = gr.File(label="Podcast Transcript RTF file")
         submit_button = gr.Button("Upload File")
@@ -86,15 +88,15 @@ with gr.Blocks() as demo:
         with gr.Row():
             choice = gr.Radio(label="Summarization LLM", choices=summarization_llm_choices)
             output = gr.Textbox(label="")
-            choice.change(setup_summarization_llm, inputs=[choice,llm_choice], outputs=[output,llm_choice])
+            choice.change(setup_summarization_llm, inputs=[choice,summarization_llm_choice], outputs=[output,summarization_llm_choice])
         with gr.Row():
             choice = gr.Radio(label="Sentiment Analysis LLM", choices=sentiment_analysis_llm_choices)
             output = gr.Textbox(label="")
-            choice.change(setup_summarization_llm, inputs=[choice,llm_choice], outputs=[output,llm_choice])
+            choice.change(setup_sentiment_analysis_llm, inputs=[choice,sentiment_analysis_llm_choice], outputs=[output,sentiment_analysis_llm_choice])
         with gr.Row():
             choice = gr.Radio(label="Question/Answer LLM", choices=question_answer_llm_choices)
             output = gr.Textbox(label="")
-            choice.change(setup_summarization_llm, inputs=[choice,llm_choice], outputs=[output,llm_choice])
+            choice.change(set_question_answer_llm, inputs=[choice,question_answer_llm_choice], outputs=[output,question_answer_llm_choice])
     with gr.Group("Summarization Method"):
         choice = gr.Radio(label="Summarization Method", choices=summarize_method_choices)
         output = gr.Textbox(label="")
@@ -102,16 +104,16 @@ with gr.Blocks() as demo:
     with gr.Group("Summarize Podcast"):     
         summary = gr.Textbox(label="Summary of Podcast")
         submit_button = gr.Button("Generate Summary")
-        submit_button.click(summarize, inputs=[rtf_file, transcript_file_name, summarization_method, llm_choice], outputs=[summary, transcript_file_name, summarization_method, llm_choice])    
+        submit_button.click(summarize, inputs=[rtf_file, transcript_file_name, summarization_method, summarization_llm_choice], outputs=[summary, transcript_file_name, summarization_method, summarization_llm_choice])    
     with gr.Group("Aspects and Sentiment of Podcast"):     
         sentiment = gr.Textbox(label="Aspect Based Sentiments")
         submit_button = gr.Button("Generate Aspects and Summary")
-        submit_button.click(generate_aspects_and_sentiments, inputs=[rtf_file, transcript_file_name, llm_choice], outputs=[sentiment, transcript_file_name, llm_choice])    
+        submit_button.click(generate_aspects_and_sentiments, inputs=[rtf_file, transcript_file_name, sentiment_analysis_llm_choice], outputs=[sentiment, transcript_file_name, sentiment_analysis_llm_choice])    
     with gr.Group("Question/Answer"):
         gr.Markdown("Question/Answer")
         question = gr.Textbox(label="Question")
         answer = gr.Textbox(label="Answer")
         answer_button = gr.Button("Answer Question")
-        answer_button.click(get_answer_for, inputs=[question, transcript_file_name, llm_choice], outputs=[answer, transcript_file_name, llm_choice])
+        answer_button.click(get_answer_for, inputs=[question, transcript_file_name, question_answer_llm_choice], outputs=[answer, transcript_file_name, question_answer_llm_choice])
 
 demo.launch()

helpers/model_utils.py

@@ -20,7 +20,7 @@ def _set_llm_based_on_choice(choice):
         model_name = "gpt-4o"
         llm = OpenAI(model=model_name, temperature=0, api_key=openai_api_key)
     elif choice == ANTHROPIC:
-        model_name = "clause-2.1"
+        model_name = "claude-2.1"
         llm = ChatAnthropic(model_name=model_name, anthropic_api_key=anthropic_api_key)
     elif choice == LLAMA3:
         model_name = "llama3"