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ryanrwatkins
commited on
Commit
•
20f289e
1
Parent(s):
56d9758
Update app.py
Browse files
app.py
CHANGED
@@ -608,10 +608,7 @@ memory.save_context(
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DTC can handle a variety of e-commerce products and can generate images using in-the-wild images & references.
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It is superior to existing zero-shot personalization methods, especially in preserving the fine-grained details of items."""}
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)
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inputs={"question":"what does Vit-all stand for?"},
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outputs={"answer":"Virtual Try-All"}
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)
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memory.load_memory_variables({})
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@@ -712,8 +709,8 @@ chain = ConversationalRetrievalChain.from_llm(
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# let's invoke the chain
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response = chain.invoke({"question":"what does Google stand for?"})
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print(response)
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chain.memory.load_memory_variables({})
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@@ -794,7 +791,7 @@ def submit_message(prompt, prompt_template, temperature, max_tokens, context_len
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prompt_template = prompt_templates[prompt_template]
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#chain = load_qa_chain(ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), chain_type="stuff")
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#completion = chain.run(input_documents=docs, question=query)
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DTC can handle a variety of e-commerce products and can generate images using in-the-wild images & references.
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It is superior to existing zero-shot personalization methods, especially in preserving the fine-grained details of items."""}
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)
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memory.load_memory_variables({})
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# let's invoke the chain
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#response = chain.invoke({"question":"what does Google stand for?"})
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#print(response)
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chain.memory.load_memory_variables({})
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prompt_template = prompt_templates[prompt_template]
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completion = chain.invoke({"question":prompt})
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#chain = load_qa_chain(ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), chain_type="stuff")
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#completion = chain.run(input_documents=docs, question=query)
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