Update app.py
Browse files
app.py
CHANGED
@@ -16,8 +16,9 @@ from transformers import TextIteratorStreamer
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from threading import Thread
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dataset = load_dataset("Namitg02/Test", split='train', streaming=False)
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# Returns a list of dictionaries, each representing a row in the dataset.
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print(dataset[1])
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#splitter = RecursiveCharacterTextSplitter(chunk_size=150, chunk_overlap=25,separators=["\n\n"]) # ["\n\n", "\n", " ", ""])
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@@ -35,14 +36,16 @@ embedding_model = HuggingFaceEmbeddings(model_name = "all-MiniLM-L6-v2")
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# Returns a FAISS wrapper vector store. Input is a list of strings. from_documents method used documents to Return VectorStore
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data = dataset["text"]
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print(data)
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data = data.add_faiss_index("embeddings")
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# adds a column that has a index of embeddings
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print("check1")
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question = "How can I reverse Diabetes?"
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SYS_PROMPT = """You are an assistant for answering questions.
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You are given the extracted parts of a long document and a question. Provide a conversational answer.
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from threading import Thread
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#dataset = load_dataset("Namitg02/Test", split='train', streaming=False)
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dataset = load_dataset("not-lain/wikipedia",revision = "embedded")
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# Returns a list of dictionaries, each representing a row in the dataset.
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print(dataset[1])
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#splitter = RecursiveCharacterTextSplitter(chunk_size=150, chunk_overlap=25,separators=["\n\n"]) # ["\n\n", "\n", " ", ""])
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# Returns a FAISS wrapper vector store. Input is a list of strings. from_documents method used documents to Return VectorStore
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#data = dataset["text"]
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data = dataset["train"]
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print(data)
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data = data.add_faiss_index("embeddings")
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# adds a column that has a index of embeddings
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print("check1")
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#question = "How can I reverse Diabetes?"
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SYS_PROMPT = """You are an assistant for answering questions.
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You are given the extracted parts of a long document and a question. Provide a conversational answer.
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