Update app.py

app.py

@@ -48,7 +48,6 @@ SYSTEM_PROMPT = "You are a seasoned stock market analyst. Your task is to list t
 
 
 def print_gpu_utilization():
-    
     nvmlInit()
     handle = nvmlDeviceGetHandleByIndex(0)
     info = nvmlDeviceGetMemoryInfo(handle)
@@ -56,37 +55,31 @@ def print_gpu_utilization():
 
 
 def get_curday():
-    
     return date.today().strftime("%Y-%m-%d")
 
 
 def n_weeks_before(date_string, n):
-    
     date = datetime.strptime(date_string, "%Y-%m-%d") - timedelta(days=7*n)
-
     return date.strftime("%Y-%m-%d")
 
 
 def get_stock_data(stock_symbol, steps):
-
     stock_data = yf.download(stock_symbol, steps[0], steps[-1])
     if len(stock_data) == 0:
         raise gr.Error(f"Failed to download stock price data for symbol {stock_symbol} from yfinance!")
     
-#     print(stock_data)
-    
     dates, prices = [], []
-    available_dates = stock_data.index.format()
+    available_dates = stock_data.index.astype(str).tolist()
     
     for date in steps[:-1]:
         for i in range(len(stock_data)):
             if available_dates[i] >= date:
-                prices.append(stock_data['Close'][i])
+                prices.append(stock_data['Close'].iloc[i])
                 dates.append(datetime.strptime(available_dates[i], "%Y-%m-%d"))
                 break
 
     dates.append(datetime.strptime(available_dates[-1], "%Y-%m-%d"))
-    prices.append(stock_data['Close'][-1])
+    prices.append(stock_data['Close'].iloc[-1])
     
     return pd.DataFrame({
         "Start Date": dates[:-1], "End Date": dates[1:],
@@ -95,13 +88,12 @@ def get_stock_data(stock_symbol, steps):
 
 
 def get_news(symbol, data):
-    
     news_list = []
     
-    for end_date, row in data.iterrows():
+    for _, row in data.iterrows():
         start_date = row['Start Date'].strftime('%Y-%m-%d')
         end_date = row['End Date'].strftime('%Y-%m-%d')
-        time.sleep(1) # control qpm
+        time.sleep(1)  # control qpm
         weekly_news = finnhub_client.company_news(symbol, _from=start_date, to=end_date)
         if len(weekly_news) == 0:
             raise gr.Error(f"No company news found for symbol {symbol} from finnhub!")
@@ -121,7 +113,6 @@ def get_news(symbol, data):
 
 
 def get_company_prompt(symbol):
-
     profile = finnhub_client.company_profile2(symbol=symbol)
     if not profile:
         raise gr.Error(f"Failed to find company profile for symbol {symbol} from finnhub!")
@@ -142,7 +133,7 @@ def get_prompt_by_row(symbol, row):
         start_date, end_date, symbol, term, row['Start Price'], row['End Price'])
     
     news = row["News"]
-    news = ["[Headline]: {}\n[Summary]: {}\n".format(
+    news_formatted = ["[Headline]: {}\n[Summary]: {}\n".format(
         n['headline'], n['summary']) for n in news if n['date'][:8] <= end_date.replace('-', '') and \
         not n['summary'].startswith("Looking for stock market analysis and research with proves results?")]
 
@@ -153,20 +144,19 @@ def get_prompt_by_row(symbol, row):
     else:
         basics = "[Basic Financials]:\n\nNo basic financial reported."
     
-    return head, news, basics
+    return head, news_formatted, basics
+
 
 def sample_news(news, k=5):
-    
     return [news[i] for i in sorted(random.sample(range(len(news)), k))]
 
 
 def latest_news(news, k=5):
-    # Sort news by date in descending order and select the latest k items
     sorted_news = sorted(news, key=lambda x: x['date'], reverse=True)
     return sorted_news[:k]
     
-def get_current_basics(symbol, curday):
 
+def get_current_basics(symbol, curday):
     basic_financials = finnhub_client.company_basic_financials(symbol, 'all')
     if not basic_financials['series']:
         raise gr.Error(f"Failed to find basic financials for symbol {symbol} from finnhub!")
@@ -191,7 +181,6 @@ def get_current_basics(symbol, curday):
     
 
 def get_all_prompts_online(symbol, data, curday, with_basics=True):
-
     company_prompt = get_company_prompt(symbol)
 
     prev_rows = []
@@ -203,10 +192,7 @@ def get_all_prompts_online(symbol, data, curday, with_basics=True):
     prompt = ""
     for i in range(-len(prev_rows), 0):
         prompt += "\n" + prev_rows[i][0]
-        latest_news_items = latest_news(
-            prev_rows[i][1],
-            min(5, len(prev_rows[i][1]))
-        )
+        latest_news_items = latest_news(prev_rows[i][1], min(5, len(prev_rows[i][1])))
         if latest_news_items:
             prompt += "\n".join(latest_news_items)
         else:
@@ -227,8 +213,8 @@ def get_all_prompts_online(symbol, data, curday, with_basics=True):
         
     return info, prompt
 
-def construct_prompt(ticker, curday, n_weeks, use_basics):
 
+def construct_prompt(ticker, curday, n_weeks, use_basics):
     try:
         steps = [n_weeks_before(curday, n) for n in range(n_weeks + 1)][::-1]
     except Exception:
@@ -237,25 +223,20 @@ def construct_prompt(ticker, curday, n_weeks, use_basics):
     data = get_stock_data(ticker, steps)
     data = get_news(ticker, data)
     data['Basics'] = [json.dumps({})] * len(data)
-    # print(data)
     
     info, prompt = get_all_prompts_online(ticker, data, curday, use_basics)
     
     prompt = B_INST + B_SYS + SYSTEM_PROMPT + E_SYS + prompt + E_INST
-    # print(prompt)
     
     return info, prompt
 
 
 def predict(ticker, date, n_weeks, use_basics):
-
     print_gpu_utilization()
 
     info, prompt = construct_prompt(ticker, date, n_weeks, use_basics)
       
-    inputs = tokenizer(
-        prompt, return_tensors='pt', padding=False
-    )
+    inputs = tokenizer(prompt, return_tensors='pt', padding=False)
     inputs = {key: value.to(model.device) for key, value in inputs.items()}
 
     print("Inputs loaded onto devices.")
@@ -308,20 +289,8 @@ demo = gr.Interface(
             label="Response"
         )
     ],
-    title="FinGPT-Forecaster",
-    description="""FinGPT-Forecaster takes random market news and optional basic financials related to the specified company from the past few weeks as input and responds with the company's **positive developments** and **potential concerns**. Then it gives out a **prediction** of stock price movement for the coming week and its **analysis** summary.
-
-This model is finetuned on Llama2-7b-chat-hf with LoRA on the past year's DOW30 market data but **welcomes any ticker symbol**.
-Company profile & Market news & Basic financials & Stock prices are retrieved using **yfinance & finnhub**.
-For more detailed and customized implementation, refer to our FinGPT project: <https://github.com/AI4Finance-Foundation/FinGPT>
-
-This demo has been downgraded to using **T4 with 8-bit inference** due to cost considerations, speed & performance may be affected.
-
-⚠️Warning: This is just a demo showing what this model can do. During each individual inference, company news is **randomly sampled** from all the news from designated weeks, which might result in **different predictions for the same period**. 
-We suggest users deploy the [original model](https://huggingface.co/FinGPT/fingpt-forecaster_dow30_llama2-7b_lora) or clone this space and inference with more carefully selected news in their favorable ways.
-Setting do_sample=False or modifying the temperature during the generation process also helps stabilize the prediction result. 
-
-**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.**
+    title="Pro Capital",
+    description="""Implementation**
 """
 )