File size: 3,556 Bytes
068368f c16d73a 5ad4e4d 8a0a2e7 b3886de 8a0a2e7 5ad4e4d 01c0727 068368f 3dff682 6dc69b7 e771190 3dff682 b5316f9 3dff682 c16d73a 3dff682 b3886de 8a0a2e7 b3886de 8a0a2e7 3dff682 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 |
import streamlit as st
import time
import pandas as pd
import numpy as np
import torch
import requests
from transformers import pipeline
st.set_page_config(page_title="Samuel Portfolio", page_icon="📈")
with st.sidebar:
st.image("https://www.onepointltd.com/wp-content/uploads/2020/03/inno2.png")
st.title("Samuel's Portfolio")
choice = st.radio("Navigation", ["About Sam","Uber Project", "Plotting", "Attached files", "Contact" ])
st.info("This project application helps you understand more about Samuel and his capabilities in detail😊.")
if choice == "About Sam":
st.title("Hi am sam")
if choice == "Uber Project":
st.title('Uber pickups in NYC')
DATE_COLUMN = 'date/time'
DATA_URL = ('https://s3-us-west-2.amazonaws.com/'
'streamlit-demo-data/uber-raw-data-sep14.csv.gz')
@st.cache_data
def load_data(nrows):
data = pd.read_csv(DATA_URL, nrows=nrows)
lowercase = lambda x: str(x).lower()
data.rename(lowercase, axis='columns', inplace=True)
data[DATE_COLUMN] = pd.to_datetime(data[DATE_COLUMN])
return data
# Create a text element and let the reader know the data is loading.
data_load_state = st.text('Loading data...')
# Load 10,000 rows of data into the dataframe.
data = load_data(10000)
# Notify the reader that the data was successfully loaded.
data_load_state.text('Loading data...done!')
data_load_state.text("Done! (using st.cache_data)")
if st.checkbox('Show raw data'):
st.subheader('Raw data')
st.write(data)
st.subheader('Number of pickups by hour')
hist_values = np.histogram(
data[DATE_COLUMN].dt.hour, bins=24, range=(0,24))[0]
st.bar_chart(hist_values)
hour_to_filter = st.slider('hour', 0, 23, 17) # min: 0h, max: 23h, default: 17h
filtered_data = data[data[DATE_COLUMN].dt.hour == hour_to_filter]
st.subheader(f'Map of all pickups at {hour_to_filter}:00')
st.map(filtered_data)
if choice == "Plotting":
st.markdown("# Plotting Demo")
st.sidebar.header("Plotting Demo")
st.write(
"""This demo illustrates a combination of plotting and animation with
Streamlit. We're generating a bunch of random numbers in a loop for around
5 seconds. Enjoy!"""
)
progress_bar = st.sidebar.progress(0)
status_text = st.sidebar.empty()
last_rows = np.random.randn(1, 1)
chart = st.line_chart(last_rows)
for i in range(1, 101):
new_rows = last_rows[-1, :] + np.random.randn(5, 1).cumsum(axis=0)
status_text.text("%i%% Complete" % i)
chart.add_rows(new_rows)
progress_bar.progress(i)
last_rows = new_rows
time.sleep(0.05)
progress_bar.empty()
# Streamlit widgets automatically run the script from top to bottom. Since
# this button is not connected to any other logic, it just causes a plain
# rerun.
st.button("Re-run")
if choice == "Contact":
st.title("You can contact me via:")
API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta"
headers = {"Authorization": "Bearer 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"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
output = query({
"inputs": "Can you please let us know more details about your ",
})
if choice == "Attached files":
st.title("Download final project report here") |