k3ybladewielder commited on
Commit
00f6446
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verified ·
1 Parent(s): ad73f05

Update app.py

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Files changed (1) hide show
  1. app.py +2 -8
app.py CHANGED
@@ -1,6 +1,3 @@
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- # !pip install uv
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- # !uv pip install -r requirements.txt
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-
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  import os
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  import yaml
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  import gradio as gr # Importe o Gradio
@@ -34,7 +31,6 @@ os.makedirs(VS_BASE, exist_ok=True)
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  # --- CONFIGURAÇÕES DE MODELOS ---
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  LLM_MODEL = 'google/gemma-3-4b-it'
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- # LLM_MODEL = 'google/gemma-3-1b-it'
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  EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
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  # ------------ criando vs -----------------
@@ -69,14 +65,12 @@ url_list = [
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  # Carregue o conteúdo da página web como documentos LangChain
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  loader = WebBaseLoader(web_paths=url_list)
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  docs = loader.load()
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- print(f"Total de páginas carregadas: {len(docs)}")
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  text_splitter = CharacterTextSplitter(chunk_size=1500, chunk_overlap=100)
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  split_docs = text_splitter.split_documents(docs)
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- embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL,
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- cache_folder=VS_BASE)
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  vector_store = FAISS.from_documents(split_docs, embeddings)
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- # vs_base = "../vector_store/vs_base"
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  os.makedirs(VS_BASE, exist_ok=True)
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  vector_store.save_local(VS_BASE)
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  print(f"vs_base salva em {VS_BASE}")
 
 
 
 
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  import os
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  import yaml
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  import gradio as gr # Importe o Gradio
 
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  # --- CONFIGURAÇÕES DE MODELOS ---
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  LLM_MODEL = 'google/gemma-3-4b-it'
 
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  EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
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  # ------------ criando vs -----------------
 
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  # Carregue o conteúdo da página web como documentos LangChain
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  loader = WebBaseLoader(web_paths=url_list)
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  docs = loader.load()
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+ #print(f"Total de páginas carregadas: {len(docs)}")
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  text_splitter = CharacterTextSplitter(chunk_size=1500, chunk_overlap=100)
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  split_docs = text_splitter.split_documents(docs)
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+ embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL)
 
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  vector_store = FAISS.from_documents(split_docs, embeddings)
 
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  os.makedirs(VS_BASE, exist_ok=True)
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  vector_store.save_local(VS_BASE)
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  print(f"vs_base salva em {VS_BASE}")