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import json
import gradio as gr
import spacy
spacy.cli.download('en_core_web_sm')
nlp = spacy.load('en_core_web_sm')
import nltk
nltk.download('stopwords')
nltk.download('punkt')
from rake_nltk import Rake
r = Rake()
import wikipediaapi
wiki_wiki = wikipediaapi.Wikipedia('Organika ([email protected])', 'en')
## ctransformers disabled for now
# from ctransformers import AutoModelForCausalLM
# model = AutoModelForCausalLM.from_pretrained(
# "Colby/StarCoder-3B-WoW-JSON",
# model_file="StarCoder-3B-WoW-JSON-ggml.bin",
# model_type="gpt_bigcode"
# )
# Use a pipeline as a high-level helper
from transformers import pipeline
topic_model = pipeline("zero-shot-classification", model="valhalla/distilbart-mnli-12-9")
model = pipeline("text-generation", model="Colby/StarCoder-3B-WoW-JSON")
def merlin_chat(message, history):
chat_text = ""
chat_json = ""
for turn in history:
chat_text += f"USER: {turn[0]}\n\nASSISTANT: {turn[1]}\n\n"
chat_json += json.dumps({"role": "user", "content": turn[0]})
chat_json += json.dumps({"role": "assistant", "content": turn[1]})
chat_text += f"USER: {message}\n"
doc = nlp(chat_text)
ents_found = []
if doc.ents:
for ent in doc.ents:
if len(ents_found) == 3:
break
if ent.text.isnumeric() or ent.label in ["DATE","TIME","PERCENT","MONEY","QUANTITY","ORDINAL","CARDINAL"]:
continue
if ent.text in ents_found:
continue
ents_found.append(ent.text.title())
r.extract_keywords_from_text(chat_text)
ents_found = ents_found + r.get_ranked_phrases()[:3]
context = ""
scores = topic_model(chat_text, ents_found, multi_label=True)['scores']
if ents_found:
max_score = 0
for k in range(len(ents_found)):
if scores[k] < 0.5:
continue
entity = ents_found[k]
if scores[k] > max_score:
max_score = scores[k]
max_topic = entity
print(f'# Looking up {entity} on Wikipedia... ', end='')
wiki_page = wiki_wiki.page(entity)
if wiki_page.exists():
print("page found... ")
entsum = wiki_page.summary
if "may refer to" in entsum or "may also refer to" in entsum:
print(" ambiguous, skipping.")
continue
else:
context += entsum + '\n\n'
context
system_msg = {
'role': 'system', 'content': context + f'\n\nThe following is a conversation about {max_topic}.'
}
user_msg = {'role': 'user', 'content': message}
prompt = "[" + json.dumps(system_msg) + chat_json + json.dumps(user_msg) + "{'role': 'assistant, 'content': '*recalls \""
for attempt in range(3):
result = model(
prompt,
return_full_text=False,
max_length=250,
handle_long_generation="hole"
)
response = result[0]['generated_text']
start = 0
end = 0
cleanStr = response.lstrip()
start = cleanStr.find('{') # this should skip over whatever it recalls to what it says next
if start<=0:
continue
cleanStr = cleanStr[start:]
end = cleanStr.find('}') + 1
if end<=0:
continue
cleanStr = cleanStr[:end]
message = json.loads(cleanStr)
if message['role'] != 'assistant':
continue
return message['content']
return "🤔"
gr.ChatInterface(merlin_chat).launch() |