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Update app.py
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app.py
CHANGED
@@ -193,7 +193,6 @@ def get_current_basics(symbol, curday):
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def get_all_prompts_online(symbol, data, curday, with_basics=True):
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-
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company_prompt = get_company_prompt(symbol)
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prev_rows = []
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@@ -206,8 +205,8 @@ def get_all_prompts_online(symbol, data, curday, with_basics=True):
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for i in range(-len(prev_rows), 0):
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prompt += "\n" + prev_rows[i][0]
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latest_news_items = latest_news(
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prev_rows[i][1],
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min(5, len(prev_rows[i][1]))
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)
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if latest_news_items:
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prompt += "\n".join(latest_news_items)
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@@ -265,7 +264,7 @@ def predict(ticker, date, n_weeks, use_basics):
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print("Inputs loaded onto devices.")
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res = model.generate(
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**inputs, max_length=4096, do_sample=
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eos_token_id=tokenizer.eos_token_id,
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use_cache=True, streamer=streamer
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)
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@@ -312,13 +311,8 @@ demo = gr.Interface(
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label="Response"
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)
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],
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title="
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description="""
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This model is finetuned on Llama2-7b-chat-hf with LoRA on the past year's DOW30 market data. Inference in this demo uses fp16 and **welcomes any ticker symbol**.
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Company profile & Market news & Basic financials & Stock prices are retrieved using **yfinance & finnhub**.
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This is just a demo showing what this model is capable of. Results inferred from randomly chosen news can be strongly biased.
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For more detailed and customized implementation, refer to our FinGPT project: <https://github.com/AI4Finance-Foundation/FinGPT>
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**Disclaimer: Nothing herein is financial advice, and NOT a recommendation to trade real money. Please use common sense and always first consult a professional before trading or investing.**
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"""
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)
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def get_all_prompts_online(symbol, data, curday, with_basics=True):
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company_prompt = get_company_prompt(symbol)
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prev_rows = []
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for i in range(-len(prev_rows), 0):
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prompt += "\n" + prev_rows[i][0]
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latest_news_items = latest_news(
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json.loads(prev_rows[i][1]),
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min(5, len(json.loads(prev_rows[i][1])))
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)
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if latest_news_items:
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prompt += "\n".join(latest_news_items)
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print("Inputs loaded onto devices.")
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res = model.generate(
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**inputs, max_length=4096, do_sample=False,
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eos_token_id=tokenizer.eos_token_id,
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use_cache=True, streamer=streamer
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)
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label="Response"
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)
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],
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title="Pro Capital",
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description="""Pro Capital implementation.**
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"""
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)
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