Update app.py

app.py

@@ -1,147 +1,78 @@
-import io
-import random
-from typing import List, Tuple
 
-import aiohttp
+import pandas as pd
+import yfinance as yf
 import panel as pn
-from PIL import Image
-from transformers import CLIPModel, CLIPProcessor
 
-pn.extension(design="bootstrap", sizing_mode="stretch_width")
+@pn.cache
+def get_df(ticker, startdate , enddate , interval="1d"):
+    # interval="1d"
+    # get_df(ticker ="PG", startdate="2000-01-01" , enddate="2023-09-01" , interval="1d")
+    DF = yf.Ticker(ticker).history(start=startdate,end=enddate,interval=interval)
+    DF['SMA50'] = DF.Close.rolling(window=50).mean()
+    DF = DF.reset_index()
+    return DF 
 
-ICON_URLS = {
-    "brand-github": "https://github.com/holoviz/panel",
-    "brand-twitter": "https://twitter.com/Panel_Org",
-    "brand-linkedin": "https://www.linkedin.com/company/panel-org",
-    "message-circle": "https://discourse.holoviz.org/",
-    "brand-discord": "https://discord.gg/AXRHnJU6sP",
-}
+def get_hvplot(ticker , startdate , enddate , interval):
+    DF = get_df(ticker , startdate=startdate , enddate=enddate , interval=interval)
 
+    import hvplot.pandas  # Ensure hvplot is installed (pip install hvplot)
+    from sklearn.linear_model import LinearRegression
+    import holoviews as hv
+    hv.extension('bokeh')
+    # Assuming your dataframe is named 'df' with columns 'Date' and 'Close'
+    # If not, replace 'Date' and 'Close' with your actual column names.
 
-async def random_url(_):
-    pet = random.choice(["cat", "dog"])
-    api_url = f"https://api.the{pet}api.com/v1/images/search"
-    async with aiohttp.ClientSession() as session:
-        async with session.get(api_url) as resp:
-            return (await resp.json())[0]["url"]
+    # Step 1: Create a scatter plot using hvplot
+    scatter_plot = DF.hvplot(x='Date', y='Close',  kind='scatter',title=f'{ticker} Close vs. Date')
 
+    # Step 2: Fit a linear regression model
+    DF['Date2'] = pd.to_numeric(DF['Date'])
+    X = DF[['Date2']]
+    y = DF[['Close']] #.values
+    model = LinearRegression().fit(X, y)
 
-@pn.cache
-def load_processor_model(
-    processor_name: str, model_name: str
-) -> Tuple[CLIPProcessor, CLIPModel]:
-    processor = CLIPProcessor.from_pretrained(processor_name)
-    model = CLIPModel.from_pretrained(model_name)
-    return processor, model
-
-
-async def open_image_url(image_url: str) -> Image:
-    async with aiohttp.ClientSession() as session:
-        async with session.get(image_url) as resp:
-            return Image.open(io.BytesIO(await resp.read()))
-
-
-def get_similarity_scores(class_items: List[str], image: Image) -> List[float]:
-    processor, model = load_processor_model(
-        "openai/clip-vit-base-patch32", "openai/clip-vit-base-patch32"
-    )
-    inputs = processor(
-        text=class_items,
-        images=[image],
-        return_tensors="pt",  # pytorch tensors
-    )
-    outputs = model(**inputs)
-    logits_per_image = outputs.logits_per_image
-    class_likelihoods = logits_per_image.softmax(dim=1).detach().numpy()
-    return class_likelihoods[0]
-
-
-async def process_inputs(class_names: List[str], image_url: str):
-    """
-    High level function that takes in the user inputs and returns the
-    classification results as panel objects.
-    """
-    try:
-        main.disabled = True
-        if not image_url:
-            yield "##### ⚠️ Provide an image URL"
-            return
-    
-        yield "##### ⚙ Fetching image and running model..."
-        try:
-            pil_img = await open_image_url(image_url)
-            img = pn.pane.Image(pil_img, height=400, align="center")
-        except Exception as e:
-            yield f"##### 😔 Something went wrong, please try a different URL!"
-            return
-    
-        class_items = class_names.split(",")
-        class_likelihoods = get_similarity_scores(class_items, pil_img)
-    
-        # build the results column
-        results = pn.Column("##### 🎉 Here are the results!", img)
-    
-        for class_item, class_likelihood in zip(class_items, class_likelihoods):
-            row_label = pn.widgets.StaticText(
-                name=class_item.strip(), value=f"{class_likelihood:.2%}", align="center"
-            )
-            row_bar = pn.indicators.Progress(
-                value=int(class_likelihood * 100),
-                sizing_mode="stretch_width",
-                bar_color="secondary",
-                margin=(0, 10),
-                design=pn.theme.Material,
-            )
-            results.append(pn.Column(row_label, row_bar))
-        yield results
-    finally:
-        main.disabled = False
-
-
-# create widgets
-randomize_url = pn.widgets.Button(name="Randomize URL", align="end")
-
-image_url = pn.widgets.TextInput(
-    name="Image URL to classify",
-    value=pn.bind(random_url, randomize_url),
-)
-class_names = pn.widgets.TextInput(
-    name="Comma separated class names",
-    placeholder="Enter possible class names, e.g. cat, dog",
-    value="cat, dog, parrot",
-)
+    # # Step 3: Predict using the linear regression model
+    DF['Predicted_Close'] = model.predict(X)
+
+    # # Step 4: Create a line plot for linear regression
+    line_plot = DF.hvplot(x='Date', y='Predicted_Close', kind='line',line_dash='dashed', color='red')
+
+    # # Step 5: Overlay scatter plot and linear regression line
+    # return (scatter_plot * line_plot).opts(width=800, height=600, show_grid=True, gridstyle={ 'grid_line_color': 'gray'})
+    # grid_style = {'grid_line_color': 'black'}#, 'grid_line_width': 1.5, 'ygrid_bounds': (0.3, 0.7),'minor_xgrid_line_color': 'lightgray', 'xgrid_line_dash': [4, 4]}
+    return (scatter_plot * line_plot).opts(width=800, height=600, show_grid=True)
 
-input_widgets = pn.Column(
-    "##### 😊 Click randomize or paste a URL to start classifying!",
-    pn.Row(image_url, randomize_url),
-    class_names,
-)
 
-# add interactivity
-interactive_result = pn.panel(
-    pn.bind(process_inputs, image_url=image_url, class_names=class_names),
-    height=600,
+import panel as pn
+from datetime import datetime
+from datetime import date
+pn.extension('bokeh', template='bootstrap')
+import hvplot.pandas
+
+tickers = ['AAPL', 'META', 'GOOG', 'IBM', 'MSFT','NKE','DLTR','DG']
+
+ticker = pn.widgets.Select(name='Ticker', options=tickers)
+window = pn.widgets.IntSlider(name='Window Size', value=50, start=1, end=200, step=5)
+
+# Create a DatePicker widget with a minimum date of 2000-01-01
+date_start = pn.widgets.DatePicker(
+    name ="Start Date",
+    description='Select a Date',
+    start= date(2000, 1, 1)
 )
 
-# add footer
-footer_row = pn.Row(pn.Spacer(), align="center")
-for icon, url in ICON_URLS.items():
-    href_button = pn.widgets.Button(icon=icon, width=35, height=35)
-    href_button.js_on_click(code=f"window.open('{url}')")
-    footer_row.append(href_button)
-footer_row.append(pn.Spacer())
-
-# create dashboard
-main = pn.WidgetBox(
-    input_widgets,
-    interactive_result,
-    footer_row,
+date_end = pn.widgets.DatePicker(
+    name ="End Date",# value=datetime(2000, 1, 1),
+    description='Select a Date',
+    end= date(2023, 9, 1)
 )
 
-title = "Panel Demo - Image Classification"
-pn.template.BootstrapTemplate(
-    title=title,
-    main=main,
-    main_max_width="min(50%, 698px)",
-    header_background="#F08080",
-).servable(title=title)
+date_start.value = date(2010,1,1)
+date_end.value = date.today()
+
+pn.Row(
+    pn.Column( ticker, window , date_start , date_end),
+    # pn.panel(pn.bind(get_hvplot, ticker, "2010-01-01","2023-09-01","1d")) #, sizing_mode='stretch_width')
+    pn.panel(pn.bind(get_hvplot, ticker, date_start , date_end,"1d")) #, sizing_mode='stretch_width')
+).servable(title="Under Valued Screener- Linear Regression")
+