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bce48eb
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1 Parent(s): 5719799

Update app.py

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Files changed (1) hide show
  1. app.py +8 -4
app.py CHANGED
@@ -1,13 +1,13 @@
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  import gradio as gr
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  from langchain_community.document_loaders import PyPDFLoader
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  from langchain.text_splitter import CharacterTextSplitter
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- from langchain_community.embeddings import HuggingFaceEmbeddings
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  from langchain_community.vectorstores import FAISS
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  from langchain.chains import RetrievalQA
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- from langchain.llms.base import LLM # Basis-Klasse, um einen Wrapper zu erstellen
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  from transformers import pipeline
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- # LeoLM-Wrapper-Klasse, die das LeoLM-Modell via Transformers-Pipeline nutzt
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  class LeoLM(LLM):
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  def __init__(self, max_new_tokens=512, temperature=0.5):
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  self.pipeline = pipeline("text-generation", model="LeoLM/leo-mistral-hessianai-7b")
@@ -21,8 +21,12 @@ class LeoLM(LLM):
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  @property
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  def _identifying_params(self):
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  return {"model": "LeoLM/leo-mistral-hessianai-7b"}
 
 
 
 
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- # PDF wird beim Start automatisch geladen
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  loader = PyPDFLoader("TrendingMedia_ChatbotBasis_FINAL.pdf")
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  documents = loader.load()
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  splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
 
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  import gradio as gr
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  from langchain_community.document_loaders import PyPDFLoader
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  from langchain.text_splitter import CharacterTextSplitter
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+ from langchain_huggingface import HuggingFaceEmbeddings
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  from langchain_community.vectorstores import FAISS
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  from langchain.chains import RetrievalQA
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+ from langchain.llms.base import LLM
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  from transformers import pipeline
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+ # LeoLM-Wrapper-Klasse, die das LeoLM-Modell über die Transformers-Pipeline nutzt
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  class LeoLM(LLM):
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  def __init__(self, max_new_tokens=512, temperature=0.5):
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  self.pipeline = pipeline("text-generation", model="LeoLM/leo-mistral-hessianai-7b")
 
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  @property
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  def _identifying_params(self):
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  return {"model": "LeoLM/leo-mistral-hessianai-7b"}
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+
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+ @property
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+ def _llm_type(self):
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+ return "custom_leolm"
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+ # PDF wird beim Start automatisch geladen und verarbeitet
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  loader = PyPDFLoader("TrendingMedia_ChatbotBasis_FINAL.pdf")
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  documents = loader.load()
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  splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)