Upload microMKIA.py

microMKIA.py

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+from silence_tensorflow import silence_tensorflow # import and call silence_tensor_flow to make tensorflow shutup about files it thinks I need but don't
+silence_tensorflow()
+import logging
+logging.disable(logging.WARNING) # disable logging warnings to get rid of warnings about things that aren't really errors
+from langchain import HuggingFacePipeline,PromptTemplate # import the stuff for setting up langchain with huggingface
+from langchain.memory import ConversationBufferMemory # import stuff langchain uses to remember stuff
+from langchain.chains import ConversationChain # import stuff it uses in relation to conversations
+from transformers import pipeline # import the main pipeline from transformers
+import readline # import readline for a slightly nicer and slightly easier to us interface
+from transformers import GenerationConfig # import stuff to configure for text generation
+import re # import re to match regular expression
+
+modelPath = "LittleMKIA" # local path to the language model
+mode1 = "text2text-generation" # task we want the transformers pipeline to perform
+
+config = GenerationConfig.from_pretrained(modelPath) # set up the configuration object
+
+pipe = pipeline(task= mode1, model=modelPath,min_length = 20,max_new_tokens = 200,temperature = 0.7,early_stopping = True,
+no_repeat_ngram_size=3,do_sample = True,top_k = 150,generation_config=config) # set up the pipeline
+
+llm = HuggingFacePipeline(pipeline=pipe) # make transformers pipeline usable by langchain
+
+# create a template for the prompt
+template  = '''
+{history}
+You are MKIA an intelligent companion and assistent.
+User: {input}'''
+
+# create the prompt from the template
+prompt = PromptTemplate(
+    input_variables=[ "input","history"],
+    template=template)
+
+# set up a memory object
+mem = ConversationBufferMemory(k = 1000,memory_key = "history",return_messase = False,ai_prefix = "MKIA")
+
+# make a conversation chain and pass all the necessary parameters to it, tell it we don't want verbose so we only get regular output
+chat_chain = ConversationChain(
+    llm=llm,
+    prompt = prompt,
+    memory= mem,
+    verbose=False
+)
+#create a function that will act as the program's main loop
+def loop():
+    while 1: # python is optimize for while 1: not while True: so I will use while 1:
+        In = input('User > ') # ask for input
+        if re.match('think[:] (.*)|think[:](.*)|Think[:] (.*)|Think[:](.*)',In) != None:
+            # if the input text matches the pattern then we will bypass langchain
+
+            In2 = re.sub('think[:]|Think[:]','',In).strip()
+            # remove the prefix at the begining
+
+            out= pipe(In2)[0]['generated_text']
+            # get the output directly from the language model
+
+            print(out)
+
+        elif In == 'quit':
+            break
+
+        else:
+           out1 = llm.predict(In)
+           mem.chat_memory.add_user_message(In)
+           #mem.chat_history.add_ai_message(out1)
+
+           print(f'MKIA-model > {out1}\n')
+           out2 = chat_chain.run(input=In+' '+out1)      # we feed the input to langchain and get the result
+           mem.chat_memory.add_ai_message(out1+ ' '+out2)
+           #mem.chat_memory.add_ai_message(out2)
+
+           print(f'MKIA-bot > {out2}\n') # let the user know what MKIA said and that she said it
+           print('\n\n') # print 2 newlines to help output be prettier
+
+loop()