added some examples

Author: beyondsimulations

part-08/data/employees.xlsx

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+import streamlit as st
+import yfinance as yf
+import plotly.graph_objects as go
+from datetime import datetime
+
+st.set_page_config(page_title="Stock Analyzer", layout="wide")
+
+# Title
+st.title('Stock Price Analysis')
+
+# Sidebar controls
+with st.sidebar:
+    st.header('Settings')
+    
+    # Stock ticker input
+    ticker = st.text_input('Stock Ticker', 'REMEDY.HE')
+    
+    # Time range selector
+    timeframe = st.radio(
+        'Select Timeframe',
+        options=[
+            ('1 Month', 30),
+            ('3 Months', 90),
+            ('6 Months', 180),
+            ('1 Year', 365),
+            ('2 Years', 730)
+        ],
+        format_func=lambda x: x[0]
+    )
+
+# Fetch data
+@st.cache_data(ttl=3600)  # Cache data for 1 hour
+def load_data(ticker_symbol):
+    stock = yf.Ticker(ticker_symbol)
+    return stock.history(period="2y")
+
+try:
+    # Load the data
+    df = load_data(ticker)
+    
+    # Filter based on selected timeframe
+    filtered_df = df.iloc[-timeframe[1]:]
+    
+    # Create candlestick chart
+    fig = go.Figure()
+    
+    # Add candlestick
+    fig.add_trace(go.Candlestick(
+        x=filtered_df.index,
+        open=filtered_df['Open'],
+        high=filtered_df['High'],
+        low=filtered_df['Low'],
+        close=filtered_df['Close'],
+        name='OHLC'
+    ))
+    
+    # Add volume bars
+    fig.add_trace(go.Bar(
+        x=filtered_df.index,
+        y=filtered_df['Volume'],
+        name='Volume',
+        yaxis='y2',
+        marker_color='rgba(128,128,128,0.5)'
+    ))
+    
+    # Update layout
+    fig.update_layout(
+        title=f'{ticker} Stock Price',
+        yaxis_title='Stock Price',
+        yaxis2=dict(
+            title='Volume',
+            overlaying='y',
+            side='right'
+        ),
+        xaxis_rangeslider_visible=False,
+        template='plotly_white',
+        height=600
+    )
+    
+    # Display the chart
+    st.plotly_chart(fig, use_container_width=True)
+    
+    # Display additional information
+    col1, col2, col3 = st.columns(3)
+    
+    with col1:
+        st.metric(
+            "Current Price",
+            f"${filtered_df['Close'][-1]:.2f}",
+            f"{((filtered_df['Close'][-1] - filtered_df['Close'][-2]) / filtered_df['Close'][-2] * 100):.2f}%"
+        )
+    
+    with col2:
+        st.metric(
+            "Highest Price",
+            f"${filtered_df['High'].max():.2f}"
+        )
+    
+    with col3:
+        st.metric(
+            "Lowest Price",
+            f"${filtered_df['Low'].min():.2f}"
+        )
+    
+    # Display raw data if desired
+    if st.checkbox('Show Raw Data'):
+        st.dataframe(filtered_df)
+
+except Exception as e:
+    st.error(f"Error loading data for {ticker}. Please check if the ticker symbol is correct.")
+    st.exception(e)
+
+# Footer
+st.markdown("---")
+st.markdown("Data provided by Yahoo Finance")

part-09/dashboards/stocks_streamlit-2.py

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+import cv2
+from deepface import DeepFace
+import numpy as np
+
+def analyze_webcam():
+    # Initialize the webcam
+    cap = cv2.VideoCapture(0)
+    
+    while True:
+        # Read frame from webcam
+        ret, frame = cap.read()
+        if not ret:
+            break
+            
+        try:
+            # Analyze the frame using DeepFace
+            analysis = DeepFace.analyze(frame, 
+                                      actions=['age', 'gender', 'emotion', 'race'],
+                                      enforce_detection=False)
+            
+            # Extract results
+            age = analysis[0]['age']
+            gender = analysis[0]['gender']
+            emotion = analysis[0]['dominant_emotion']
+            race = analysis[0]['dominant_race']
+            
+            # Add text to frame
+            font = cv2.FONT_HERSHEY_SIMPLEX
+            cv2.putText(frame, f'Age: {age}', (10, 30), font, 1, (0, 255, 0), 2)
+            cv2.putText(frame, f'Gender: {gender}', (10, 70), font, 1, (0, 255, 0), 2)
+            cv2.putText(frame, f'Emotion: {emotion}', (10, 110), font, 1, (0, 255, 0), 2)
+            cv2.putText(frame, f'Race: {race}', (10, 150), font, 1, (0, 255, 0), 2)
+            
+        except Exception as e:
+            # If no face is detected or other error occurs, continue to next frame
+            pass
+        
+        # Display the frame
+        cv2.imshow('Webcam Analysis', frame)
+        
+        # Break loop on 'q' press
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+    
+    # Release resources
+    cap.release()
+    cv2.destroyAllWindows()
+
+if __name__ == "__main__":
+    analyze_webcam()

part-10/projects/deep_face.py

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+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "infly/OpenCoder-1.5B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(model_name,
+                                             torch_dtype=torch.bfloat16,
+                                             device_map="auto",
+                                             #device_map=torch.device("cpu"),
+                                             trust_remote_code=True)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)