app.py

app.py

@@ -1,9 +1,9 @@
 #importing dependencies
 import os
-from keys import token
+from keys import apikey
 
 import streamlit as st
-from langchain import HuggingFaceHub
+from langchain.llms import OpenAI
 from langchain.prompts import PromptTemplate
 from langchain.chains import LLMChain
 from langchain.tools import WikipediaQueryRun
@@ -12,8 +12,7 @@ from langchain.utilities import WikipediaAPIWrapper
 
 wikipedia = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
 
-os.environ["HUGGINGFACEHUB_API_TOKEN"] = token
-repo_id = "tiiuae/falcon-40b"
+os.environ["OPENAI_API_KEY"] = apikey
 
 #app framework
 st.title("🛕Gurubot AI") 
@@ -64,7 +63,7 @@ elif church == "Science":
     scripture = "Wikipedia, precisely the explanations you find in {wikipedia_results}"
 
 context = PromptTemplate(
-    input_variables = ["persona", "scripture", "prompt"],
+    input_variables = ["persona", "scripture", "topic"],
     template="The user will provide you with an input text \
     in form of a question or \
     in form of a description of a situation or \
@@ -73,13 +72,13 @@ context = PromptTemplate(
     and answer the user's question or give advice to the user. \
     Choose your words in accordance to the teachings of {scripture}. \
     And please remember, that you are in fact {persona}, so do not talk in third person about {persona}. \
-    Input text: {prompt}"
+    Input text: {topic}"
 )
 
 find_keyword = PromptTemplate(
-    input_variables = ['prompt'],
+    input_variables = ['topic'],
     template="The user will provide you with an input text. \
-    Please return the main keyword of this input text. Input text: {prompt}"
+    Please return the main keyword of this input text. Input text: {topic}"
 )
 
 yoda_grammar = PromptTemplate(
@@ -94,30 +93,24 @@ yoda_grammar = PromptTemplate(
 
 
 #llms
-#llm = OpenAI(temperature=0.9)
-#llm_facts = OpenAI(temperature=0)
-llm = HuggingFaceHub(
-    repo_id=repo_id, model_kwargs={"temperature": 0.9, "max_length": 50}
-)
-llm_facts = HuggingFaceHub(
-    repo_id=repo_id, model_kwargs={"temperature": 0.0, "max_length": 50}
-)
-main_chain = LLMChain(prompt=context, llm=llm)
-yoda_grammar_chain = LLMChain(prompt=yoda_grammar, llm=llm_facts)
-keyword_chain = LLMChain(prompt=find_keyword, llm=llm_facts)
-wiki_chain = LLMChain(prompt=context, llm=llm_facts)
+llm = OpenAI(temperature=0.9)
+llm_facts = OpenAI(temperature=0)
+main_chain = LLMChain(llm=llm, prompt=context, verbose=True)
+yoda_grammar_chain = LLMChain(llm=llm_facts, prompt=yoda_grammar)
+keyword_chain = LLMChain(llm=llm_facts, prompt=find_keyword)
+wiki_chain = LLMChain(llm=llm_facts, prompt=context, verbose=True)
 
 
 #answer on screen if prompt is entered
 if prompt:
     if church == "Science":
-        keyword = keyword_chain.run(prompt=prompt)
+        keyword = keyword_chain.run(topic=prompt)
         wikipedia_results = wikipedia.run(keyword)
-        response = wiki_chain.run(persona=persona, scripture=scripture, prompt=prompt)
+        response = wiki_chain.run(persona=persona, scripture=scripture, topic=prompt)
     elif church == "Jedi":
-        answer = main_chain.run(persona=persona, scripture=scripture, prompt=prompt)
+        answer = main_chain.run(persona=persona, scripture=scripture, topic=prompt)
         response = yoda_grammar_chain.run(answer=answer)
         
     else:
-        response = main_chain.run(persona=persona, scripture=scripture, prompt=prompt)
+        response = main_chain.run(persona=persona, scripture=scripture, topic=prompt)
     st.write(response)