Update app.py

app.py

@@ -12,42 +12,25 @@ from PyPDF2 import PdfReader
 from PyPDF2 import PdfWriter
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.text_splitter import CharacterTextSplitter
-#from langchain.vectorstores import ElasticVectorSearch, Pinecone, Weaviate, FAISS
 from langchain.chains.question_answering import load_qa_chain
 from langchain.llms import OpenAI
-
 from langchain.embeddings.openai import OpenAIEmbeddings
-#from langchain.vectorstores import Chroma
-#from langchain.text_splitter import TokenTextSplitter
-#from langchain.llms import OpenAI
 from langchain import OpenAI
 from langchain.chat_models import ChatOpenAI
-#from langchain.chains import ChatVectorDBChain
-#from langchain.chains import RetrievalQA
 from langchain.document_loaders import PyPDFLoader
 from langchain.chains.question_answering import load_qa_chain
 
-# Use Chroma in Colab to create vector embeddings, I then saved them to HuggingFace so now I have to set it use them here.
-#from chromadb.config import Settings
-#client = chromadb.Client(Settings(
-##    chroma_db_impl="duckdb+parquet",
-#    persist_directory="./embeddings" # Optional, defaults to .chromadb/ in the current directory
-#))
-
 
 openai.api_key = os.environ['openai_key']
 os.environ["OPENAI_API_KEY"] = os.environ['openai_key']
 
 
-
+prompt_templates = {"All Needs Experts": "Respond as if you are combiation of all needs assessment experts."}
+actor_description = {"All Needs Experts": "A combiation of all needs assessment experts."}
 
 def get_empty_state():
     return {"total_tokens": 0, "messages": []}
 
-    
-#Initial prompt template, others added below from TXT file
-prompt_templates = {"All Needs Experts": "Respond as if you are combiation of all needs assessment experts."}
-actor_description = {"All Needs Experts": "A combiation of all needs assessment experts."}
 
 def download_prompt_templates():
     url = "https://huggingface.co/spaces/ryanrwatkins/needs/raw/main/gurus.txt"
@@ -62,8 +45,6 @@ def download_prompt_templates():
                 description = row[2].strip('"')
                 prompt_templates[act] = prompt
                 actor_description[act] = description
-                
-
 
     except requests.exceptions.RequestException as e:
         print(f"An error occurred while downloading prompt templates: {e}")
@@ -73,64 +54,22 @@ def download_prompt_templates():
     choices = choices[:1] + sorted(choices[1:])
     return gr.update(value=choices[0], choices=choices)
 
+
+
 def on_prompt_template_change(prompt_template):
     if not isinstance(prompt_template, str): return
     return prompt_templates[prompt_template]
 
+
+
 def on_prompt_template_change_description(prompt_template):
     if not isinstance(prompt_template, str): return
     return actor_description[prompt_template]
 
 
 
-
 def submit_message(prompt, prompt_template, temperature, max_tokens, context_length, state):
-
-
-    
-    # load in all the files
-    #path = './files'
-    #pdf_files = glob.glob(os.path.join(path, "*.pdf"))
-    #pdf_files = glob.glob(os.path.join(path, "*.pdf"))
-
-    #for file in pdf_files:
-     # loader = PyPDFLoader(file)
-     # pages = loader.load_and_split()
-     # text_splitter = TokenTextSplitter(chunk_size=1000, chunk_overlap=0)
-     # split_pages = text_splitter.split_documents(pages)
-     
-    #persist_directory = "./embeddings"
-    #embeddings = OpenAIEmbeddings()
-    #vectordb = Chroma.from_documents(split_pages, embeddings, persist_directory=persist_directory)
-    #vectordb.persist()
-
-    #path = './files'
-    #pdf_files = glob.glob(os.path.join(path, "*.pdf"))
     
-    #merger = PdfWriter()
-    
-    # add all file in the list to the merger object
-    #for pdf in pdf_files:
-    #  merger.append(pdf)
-    #merger.write("merged-pdf.pdf")
-    #merger.close()
-    
-    #reader = PdfReader("merged-pdf.pdf")
-    #raw_text = ''
-    #for i, page in enumerate(reader.pages):
-    #  text = page.extract_text()
-    #  if text:
-     #     raw_text += text
-    #text_splitter = CharacterTextSplitter(        
-    #  separator = "\n",
-    #  chunk_size = 1000,
-    #  chunk_overlap  = 200,
-    #  length_function = len,
-    #)
-    #texts = text_splitter.split_text(raw_text)
-    #len(texts)
-    #embeddings = OpenAIEmbeddings()
-        
     
     history = state['messages']
 
@@ -147,50 +86,21 @@ def submit_message(prompt, prompt_template, temperature, max_tokens, context_len
 
 
     try:
-        #completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + history[-context_length*2:] + [prompt_msg], temperature=temperature, max_tokens=max_tokens)
-
-# completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + history[-context_length*2:] + [prompt_msg], temperature=temperature, max_tokens=max_tokens)
-
-        #completion_chain = load_qa_chain(ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), chain_type="stuff" )
-        #completion = RetrievalQA(combine_documents_chain=completion_chain, retriever=vectordb.as_retriever())
-        #query = str(system_prompt + history[-context_length*2:] +  [prompt_msg])
-        #completion = completion.run(query)
-        # from https://blog.devgenius.io/chat-with-document-s-using-openai-chatgpt-api-and-text-embedding-6a0ce3dc8bc8
-        #completion_chain = load_qa_chain(ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), chain_type="stuff" )
-        #completion = RetrievalQA(combine_documents_chain=completion_chain, retriever=vectordb.as_retriever(), return_source_documents=False)
-        #completion = RetrievalQA.from_chain_type(llm=ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), chain_type="stuff", retriever=vectordb.as_retriever(), return_source_documents=True)
-        #query = str(system_prompt + history[-context_length*2:] +  [prompt_msg])
-        #completion = completion({"query": query})
-        #completion = completion.run(query)
-
-#        completion = completion({"question": query, "chat_history": history[-context_length*2:]})
-
-        #with open("foo.pkl", 'rb') as f:
-        #    new_docsearch = pickle.load(f)
-        
-        #docsearch = FAISS.from_texts(texts, embeddings)
+
         with open("embeddings.pkl", 'rb') as f: 
             new_docsearch = pickle.load(f)
             
-        #query = str(system_prompt + history[-context_length*2:] +  [prompt_msg])
+
         query = str(system_prompt + history +  [prompt_msg])
+        
         docs = new_docsearch.similarity_search(query)
-        #print(docs[0].page_content)
 
         chain = load_qa_chain(ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), chain_type="stuff")
         completion = chain.run(input_documents=docs, question=query)
         completion = { "content": completion }
         
-# VectorDBQA.from_chain_type(llm=OpenAI(), chain_type="stuff", vectorstore=docsearch, return_source_documents=True)
-# https://colab.research.google.com/drive/1dzdNDZyofRB0f2KIB4gHXmIza7ehMX30?usp=sharing#scrollTo=b-ejDn_JfpWW
-
         get_empty_state()
         state.append(completion.copy())
-        
-        #history.append(prompt_msg.copy())
-        #history.append(completion.copy())
-        #history.append(completion.choices[0].message.to_dict())
-        #history.append(completion["result"].choices[0].message.to_dict())
 
         state['total_tokens'] += completion['usage']['total_tokens']
     
@@ -207,15 +117,17 @@ def submit_message(prompt, prompt_template, temperature, max_tokens, context_len
     
     
     chat_messages = [(prompt_msg['content'], completion['content'])]
-    #chat_messages = [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)]
-    #chat_messages = [(history[-2]['content'], history[-1]['content'])]
     
     return '', chat_messages, total_tokens_used_msg, state
 
+
+
+
 def clear_conversation():
     return gr.update(value=None, visible=True), None, "", get_empty_state()
 
 
+
 css = """
       #col-container {max-width: 80%; margin-left: auto; margin-right: auto;}
       #chatbox {min-height: 400px;}
@@ -232,41 +144,33 @@ with gr.Blocks(css=css) as demo:
 
 
     with gr.Column(elem_id="col-container"):
-        #with open("embeddings.pkl", 'rb') as f: 
-         #   new_docsearch = pickle.load(f)
-            
-        #query = str("performance")
-        #docs = new_docsearch.similarity_search(query)
+
            
-        gr.Markdown("""## Ask questions of experts on needs assessments, get responses from a *needs assessment* version of ChatGPT.
+        gr.Markdown("""## Ask questions of *needs assessment* experts,\get responses from a *needs assessment* version of ChatGPT.
                     Ask questions of all of them, or pick your expert below.""" ,
                     elem_id="header")
         
- 
-        
         
         with gr.Row():
             with gr.Column():
                 chatbot = gr.Chatbot(elem_id="chatbox")
-                #input_message = gr.Textbox(show_label=False, placeholder=docs, visible=True).style(container=False)
-                input_message = gr.Textbox(show_label=False, placeholder="Enter your needs assessment question and press enter", visible=True).style(container=False)
+                input_message = gr.Textbox(show_label=False, placeholder="Enter your needs assessment question", visible=True).style(container=False)
 
                 btn_submit = gr.Button("Submit")
                 total_tokens_str = gr.Markdown(elem_id="total_tokens_str")
                 btn_clear_conversation = gr.Button("Start New Conversation")
             with gr.Column():
-                prompt_template = gr.Dropdown(label="Choose a expert:", choices=list(prompt_templates.keys()))
+                prompt_template = gr.Dropdown(label="Choose an Expert:", choices=list(prompt_templates.keys()))
                 prompt_template_preview = gr.Markdown(elem_id="prompt_template_preview")
                 with gr.Accordion("Advanced parameters", open=False):
-                    temperature = gr.Slider(minimum=0, maximum=2.0, value=0.7, step=0.1, label="Flexibility", info="Higher = more creative/chaotic, Lower = just the guru")
-                    max_tokens = gr.Slider(minimum=100, maximum=400, value=200, step=1, label="Max tokens per response")
-                    context_length = gr.Slider(minimum=1, maximum=5, value=2, step=1, label="Context length", info="Number of previous questions you have asked. Be careful with high values, it can blow up the token budget quickly.")
+                    temperature = gr.Slider(minimum=0, maximum=2.0, value=0.7, step=0.1, label="Flexibility", info="Higher = More AI, Lower = More Expert")
+                    max_tokens = gr.Slider(minimum=100, maximum=400, value=200, step=1, label="Length of Response.")
+                    context_length = gr.Slider(minimum=1, maximum=5, value=2, step=1, label="Context Length", info="Number of previous questions you have asked.")
 
    
     btn_submit.click(submit_message, [ input_message, prompt_template, temperature, max_tokens, context_length, state], [input_message, chatbot, total_tokens_str, state])
     input_message.submit(submit_message, [ input_message, prompt_template, temperature, max_tokens, context_length, state], [input_message, chatbot, total_tokens_str, state])
     btn_clear_conversation.click(clear_conversation, [], [input_message, chatbot, total_tokens_str, state])
-    #prompt_template.change(on_prompt_template_change, inputs=[prompt_template], outputs=[prompt_template_preview])
     prompt_template.change(on_prompt_template_change_description, inputs=[prompt_template], outputs=[prompt_template_preview])