Update app.py
Browse files
app.py
CHANGED
@@ -15,77 +15,19 @@ ensemble_retriever = EnsembleRetriever(
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retrievers=[bm25_retriever, retriever], weights=[0.5, 0.5]
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tokenizer = AutoTokenizer.from_pretrained("ShynBui/vie_qa", token=os.environ.get("HF_TOKEN"))
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model = AutoModelForQuestionAnswering.from_pretrained("ShynBui/vie_qa", token=os.environ.get("HF_TOKEN"))
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headers = {
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"Accept": "application/json",
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"Authorization": "Bearer " + os.environ.get("HF_TOKEN"),
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"Content-Type": "application/json"
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}
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def query(payload):
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response = requests.post(os.environ.get("API_URL"), headers=headers, json=payload)
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return response.json()
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def greet(quote):
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sources = []
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answers = []
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scores = []
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ids = []
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docs = ensemble_retriever.get_relevant_documents(quote)
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for i in docs:
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context = ViTokenizer.tokenize(i.page_content)
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question = ViTokenizer.tokenize(quote)
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print("source:", i.metadata['source'])
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sources.append(i.metadata['source'])
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output = query({
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"inputs": {
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"question": question,
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"context": context[:256]
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},
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})
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while "error" in output:
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# print('fail')
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time.sleep(1)
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output = query({
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"inputs": {
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"question": question,
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"context": context[:256]
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},
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})
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answers.append(output['answer'])
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return answers
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def greet2(quote):
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answers = []
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docs = ensemble_retriever.get_relevant_documents(quote)
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return docs
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for i in docs:
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context = ViTokenizer.tokenize(i.page_content)
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question = ViTokenizer.tokenize(quote)
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inputs = tokenizer(question, context, return_tensors="pt")
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end_index = torch.argmax(outputs.end_logits) + 1
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tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][start_index:end_index]))
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return
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if __name__ == "__main__":
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retrievers=[bm25_retriever, retriever], weights=[0.5, 0.5]
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)
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def greet2(quote):
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qa_chain = get_qachain(retriever=ensemble_retriever)
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prompt = os.environ['PROMPT']
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qa_chain.combine_documents_chain.llm_chain.prompt.messages[0].prompt.template = prompt
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llm_response = qa_chain(quote)
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return llm_response['result']
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if __name__ == "__main__":
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