new files

Dockerfile

@@ -0,0 +1,11 @@
+FROM python:3.9
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+WORKDIR $HOME/app
+COPY --chown=user . $HOME/app
+COPY ./requirements.txt ~/app/requirements.txt
+RUN pip install -r requirements.txt
+COPY . .
+CMD ["chainlit", "run", "app.py", "--port", "7860"]

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Katerina Gawthorpe
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,10 +1,22 @@
 ---
-title: ExpressMode
-emoji: 🌖
-colorFrom: blue
-colorTo: purple
+title: StockSavvy
+emoji: 📉
+colorFrom: pink
+colorTo: yellow
 sdk: docker
 pinned: false
+app_port: 7860
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## 🤖 StockSavvy
+
+![alt text](el_pic.png)
+
+> Forecast and analyze stocks and make $$$!!!. Ask me anything about stocks.
+
+##  Data from open-source data: Yahoo finance + Sentiment analysis.
+LangGraph/Langchain/RAG/Chainlit/OpenAI
+---
+
+
+> :wave: Code originates mainly from the amazing AI Makerspace Bootcamp!!! For more see   [https://github.com/sanjeevl10/StockSavvyFinal]

app.py

@@ -0,0 +1,126 @@
+from openai import OpenAI
+import streamlit as st
+import utils as u
+from langchain_openai import ChatOpenAI
+from tools import sentiment_analysis_util
+import functools
+from typing import Annotated
+import operator
+from typing import Sequence, TypedDict
+import numpy as np
+import pandas as pd
+from dotenv import load_dotenv
+import os
+import functools
+from typing import Annotated
+import operator
+
+st.set_page_config(page_title="LangChain Agent", layout="wide")
+load_dotenv()
+OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
+
+llm = ChatOpenAI(model="gpt-3.5-turbo")
+
+from langchain_core.runnables import RunnableConfig
+
+st.title("💬 ExpressMood")
+
+@st.cache_resource
+def initialize_session_state():
+    if "chat_history" not in st.session_state:
+        st.session_state["messages"] = [{"role":"system", "content":"""
+You are a sentiment analysis expert. Answer all questions related to cryptocurrency investment reccommendations. Say I don't know if you don't know.
+"""}]
+
+initialize_session_state()
+
+sideb=st.sidebar
+with st.sidebar:
+    prompt=st.text_input("Enter topic for sentiment analysis: ")
+
+check1=sideb.button(f"analyze {prompt}")
+
+if check1:
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    # Display user message in chat message container
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # ========================== Sentiment analysis
+    #Perform sentiment analysis on the cryptocurrency news & predict dominant sentiment along with plotting the sentiment breakdown chart
+    # Downloading from reddit
+
+    # Downloading from alpaca
+    if len(prompt.split(' '))<2:
+        print('here')
+        st.write('I am analyzing Google News ...')
+        news_articles = sentiment_analysis_util.fetch_news(str(prompt))
+    st.write('Now, I am analyzing Reddit ...')
+    reddit_news_articles=sentiment_analysis_util.fetch_reddit_news(prompt)
+    analysis_results = []
+
+    #Perform sentiment analysis for each product review
+    if len(prompt.split(' '))<2:
+        print('here')
+        for article in news_articles:
+            if prompt.lower()[0:6] in article['News_Article'].lower():
+                sentiment_analysis_result = sentiment_analysis_util.analyze_sentiment(article['News_Article'])
+
+                # Display sentiment analysis results
+                #print(f'News Article: {sentiment_analysis_result["News_Article"]} : Sentiment: {sentiment_analysis_result["Sentiment"]}', '\n')
+
+                result = {
+                            'News_Article': sentiment_analysis_result["News_Article"],
+                            'Sentiment': sentiment_analysis_result["Sentiment"][0]['label'],
+                            'Index': sentiment_analysis_result["Sentiment"][0]['score'],
+                            'URL': article['URL']
+                        }
+                
+                analysis_results.append(result)
+
+    articles_url=[]
+    for article in reddit_news_articles:
+        if prompt.lower()[0:6] in article.lower():
+            sentiment_analysis_result_reddit = sentiment_analysis_util.analyze_sentiment(article)
+
+            # Display sentiment analysis results
+            #print(f'News Article: {sentiment_analysis_result_reddit["News_Article"]} : Sentiment: {sentiment_analysis_result_reddit["Sentiment"]}', '\n')
+
+            result = {
+                        'News_Article': sentiment_analysis_result_reddit["News_Article"],
+                        'Index':np.round(sentiment_analysis_result_reddit["Sentiment"][0]['score'],2)
+                    }
+            analysis_results.append(np.append(result,np.append(article.split('URL:')[-1:], ((article.split('Date: ')[-1:])[0][0:10]))))
+    #pd.DataFrame(analysis_results).to_csv('analysis_results.csv')
+
+    #Generate summarized message rationalize dominant sentiment
+    summary = sentiment_analysis_util.generate_summary_of_sentiment(analysis_results) #, dominant_sentiment)
+    st.chat_message("assistant").write((summary))
+    st.session_state.messages.append({"role": "assistant", "content": summary})
+    #answers=np.append(res["messages"][-1].content,summary)
+
+client = OpenAI(api_key=OPENAI_API_KEY)
+
+if "openai_model" not in st.session_state:
+    st.session_state["openai_model"] = "gpt-3.5-turbo"
+
+if prompt := st.chat_input("Any other questions? "):
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    # Display user message in chat message container
+    with st.chat_message("user"):
+        st.markdown(prompt)
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        stream = client.chat.completions.create(
+            model=st.session_state["openai_model"],
+            messages=[
+                {"role": m["role"], "content": m["content"]}
+                for m in st.session_state.messages
+            ],
+            stream=True,
+        )
+        response = st.write_stream(stream)
+    st.session_state.messages.append({"role": "assistant", "content": response})
+

chainlit.md

@@ -0,0 +1,23 @@
+
+# 🤖 ExpressMode
+----
+ExpressMode is a powerful tool designed to perform sentiment analysis on any topic related to North American roads. This app helps you gain insights into public opinion, trends, and emotions surrounding road conditions, infrastructure, traffic, and more.
+
+### 🚀 Features
+
+- Topic Sentiment Analysis: Quickly determine the sentiment (positive, neutral, negative) of discussions about North American roads.
+- Comprehensive Data Sources: Leverage various sources including social media and news articles.
+- Real-time Updates: Get the latest sentiment analysis as soon as new data is available.
+- Customizable Filters: Focus on specific regions, road types, or timeframes for more targeted insights.
+
+### 🧑‍💻 Usage
+
+-> Enter a one-word topic related to North American roads into the left sidebar.
+-> Hit "Analyze" to view sentiment trends and detailed reports.
+-> You can ask any other related question in the main search bar.
+
+### 💬 Feedback
+
+For any questions or feedback, please contact [email protected].
+
+🚗 Try it out!

tools/sentiment_analysis_util.py

@@ -0,0 +1,221 @@
+
+import os
+from dotenv import load_dotenv
+from transformers import pipeline
+import pandas as pd
+from collections import defaultdict
+from datetime import date
+import matplotlib.pyplot as plt
+import http.client, urllib.parse
+from GoogleNews import GoogleNews
+from langchain_openai import ChatOpenAI
+import pandas as pd
+import praw
+from datetime import datetime
+import numpy as np
+
+load_dotenv()
+
+def fetch_news(topic):
+    
+    """ Fetches news articles within a specified date range.
+
+    Args:
+    - topic (str): Topic of interest
+    
+    Returns:
+    - list: A list of dictionaries containing news. """
+    
+    load_dotenv()
+    days_to_fetch_news = os.environ["DAYS_TO_FETCH_NEWS"]
+
+    googlenews = GoogleNews()
+    googlenews.set_period(days_to_fetch_news)
+    googlenews.get_news(topic)
+    news_json=googlenews.get_texts()
+    urls=googlenews.get_links()
+    
+    no_of_news_articles_to_fetch = os.environ["NO_OF_NEWS_ARTICLES_TO_FETCH"]
+    news_article_list = []
+    counter = 0
+    for article in news_json:
+        
+        if(counter >= int(no_of_news_articles_to_fetch)):
+            break
+
+        relevant_info = {
+            'News_Article': article,
+            'URL': urls[counter]
+        }
+        news_article_list.append(relevant_info)
+        counter+=1
+    return news_article_list
+
+def fetch_reddit_news(topic):
+    load_dotenv()
+    REDDIT_USER_AGENT= os.environ["REDDIT_USER_AGENT"]
+    REDDIT_CLIENT_ID= os.environ["REDDIT_CLIENT_ID"]
+    REDDIT_CLIENT_SECRET= os.environ["REDDIT_CLIENT_SECRET"]
+    #https://medium.com/geekculture/a-complete-guide-to-web-scraping-reddit-with-python-16e292317a52
+    user_agent = REDDIT_USER_AGENT
+    reddit = praw.Reddit (
+    client_id= REDDIT_CLIENT_ID,
+    client_secret= REDDIT_CLIENT_SECRET,
+    user_agent=user_agent
+    )
+
+    headlines = set ( )
+    for submission in reddit.subreddit('nova').search(topic,time_filter='week'):
+        headlines.add(submission.title + ', Date: ' +datetime.utcfromtimestamp(int(submission.created_utc)).strftime('%Y-%m-%d %H:%M:%S') + ', URL:' +submission.url)
+    
+    if len(headlines)<10:
+        for submission in reddit.subreddit('nova').search(topic,time_filter='year'):
+            headlines.add(submission.title + ', Date: ' +datetime.utcfromtimestamp(int(submission.created_utc)).strftime('%Y-%m-%d %H:%M:%S') + ', URL:' +submission.url)
+    if len(headlines)<10:
+        for submission in reddit.subreddit('nova').search(topic): #,time_filter='week'):
+            headlines.add(submission.title + ', Date: ' +datetime.utcfromtimestamp(int(submission.created_utc)).strftime('%Y-%m-%d %H:%M:%S') + ', URL:' +submission.url)
+    return headlines
+
+def analyze_sentiment(article):
+    """
+    Analyzes the sentiment of a given news article.
+
+    Args:
+    - news_article (dict): Dictionary containing 'summary', 'headline', and 'created_at' keys.
+
+    Returns:
+    - dict: A dictionary containing sentiment analysis results.
+    """
+
+    #Analyze sentiment using default model
+    #classifier = pipeline('sentiment-analysis')
+
+    #Analyze sentiment using specific model
+    classifier = pipeline(model='tabularisai/robust-sentiment-analysis') #mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis')
+    sentiment_result = classifier(str(article))
+
+    analysis_result = {
+        'News_Article': article,
+        'Sentiment': sentiment_result
+    }
+
+    return analysis_result
+
+
+def generate_summary_of_sentiment(sentiment_analysis_results): #, dominant_sentiment):
+    
+    
+    news_article_sentiment = str(sentiment_analysis_results)
+    print("News article sentiment : " + news_article_sentiment)
+    
+
+    os.environ["OPENAI_API_KEY"] = os.environ["OPENAI_API_KEY"]
+    model = ChatOpenAI(
+        model="gpt-4o",
+        temperature=0,
+        max_tokens=None,
+        timeout=None,
+        max_retries=2,
+        # api_key="...",  # if you prefer to pass api key in directly instaed of using env vars
+        # base_url="...",
+        # organization="...",
+        # other params...
+    )
+
+    messages=[
+            {"role": "system", "content": "You are a helpful assistant that looks at all news articles, their sentiment, along with domainant sentiment and generates a summary rationalizing dominant sentiment. At the end of the summary, add URL links with dates for all the articles in the markdown format for streamlit. Example of adding the URLs: The Check out the links: [link](%s) % url, 2024-03-01 "},
+            {"role": "user", "content": f"News articles and their sentiments: {news_article_sentiment}"} #, and dominant sentiment is: {dominant_sentiment}"}
+    ]
+    response = model.invoke(messages)
+    
+
+    summary = response.content
+    print ("+++++++++++++++++++++++++++++++++++++++++++++++")
+    print(summary)
+    print ("+++++++++++++++++++++++++++++++++++++++++++++++")
+    return summary
+
+
+def plot_sentiment_graph(sentiment_analysis_results):
+    """
+    Plots a sentiment analysis graph 
+
+    Args:
+    - sentiment_analysis_result): (dict): Dictionary containing 'Review Title : Summary', 'Rating', and 'Sentiment' keys.
+
+    Returns:
+    - dict: A dictionary containing sentiment analysis results.
+    """
+    df = pd.DataFrame(sentiment_analysis_results)
+    print(df)
+
+    #Group by Rating, sentiment value count
+    grouped = df['Sentiment'].value_counts()
+
+    sentiment_counts = df['Sentiment'].value_counts()
+
+    # Plotting pie chart
+    # fig = plt.figure(figsize=(5, 3))
+    # plt.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%', startangle=140)
+    # plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
+    
+    #Open below when u running this program locally and c
+    #plt.show()
+
+    return sentiment_counts
+
+
+def get_dominant_sentiment (sentiment_analysis_results):
+    """
+    Returns overall sentiment, negative or positive or neutral depending on the count of negative sentiment vs positive sentiment 
+
+    Args:
+    - sentiment_analysis_result): (dict): Dictionary containing 'summary', 'headline', and 'created_at' keys.
+
+    Returns:
+    - dict: A dictionary containing sentiment analysis results.
+    """
+    df = pd.DataFrame(sentiment_analysis_results)
+
+    # Group by the 'sentiment' column and count the occurrences of each sentiment value
+    print(df)
+    print(df['Sentiment'])
+    sentiment_counts = df['Sentiment'].value_counts().reset_index()
+    sentiment_counts.columns = ['sentiment', 'count']
+    print(sentiment_counts)
+
+    # Find the sentiment with the highest count
+    dominant_sentiment = sentiment_counts.loc[sentiment_counts['count'].idxmax()]
+
+    return dominant_sentiment['sentiment']
+
+#starting point of the program
+if __name__ == '__main__':
+    
+    #fetch news
+    news_articles = fetch_news('AAPL')
+
+    analysis_results = []
+    
+    #Perform sentiment analysis for each product review
+    for article in news_articles:
+        sentiment_analysis_result = analyze_sentiment(article['News_Article'])
+
+        # Display sentiment analysis results
+        print(f'News Article: {sentiment_analysis_result["News_Article"]} : Sentiment: {sentiment_analysis_result["Sentiment"]}', '\n')
+
+        result = {
+                    'News_Article': sentiment_analysis_result["News_Article"],
+                    'Sentiment': sentiment_analysis_result["Sentiment"][0]['label']
+                }
+        
+        analysis_results.append(result)
+
+    
+    #Graph dominant sentiment based on sentiment analysis data of reviews
+    dominant_sentiment = get_dominant_sentiment(analysis_results)
+    print(dominant_sentiment)
+    
+    #Plot graph
+    plot_sentiment_graph(analysis_results)
+

utils.py

@@ -0,0 +1,177 @@
+import matplotlib.pyplot as plt
+import plotly.graph_objects as go
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+import yfinance as yf
+from plotly.subplots import make_subplots
+
+def get_stock_price(stockticker: str) -> str:
+        ticker = yf.Ticker(stockticker)
+        todays_data = ticker.history(period='1d')
+        return str(round(todays_data['Close'][0], 2))
+
+def plot_candlestick_stock_price(historical_data):
+    """Useful for plotting candlestick plot for stock prices.
+    Use historical stock price data from yahoo finance for the week and plot them."""
+    df=historical_data[['Close','Open','High','Low']]
+    df.index=pd.to_datetime(df.index)
+    df.index.names=['Date']
+    df=df.reset_index()
+
+    fig = go.Figure(data=[go.Candlestick(x=df['Date'],
+                open=df['Open'],
+                high=df['High'],
+                low=df['Low'],
+                close=df['Close'])])
+    fig.show()
+
+def historical_stock_prices(stockticker, days_ago):
+    """Upload accurate data to accurate dates from yahoo finance."""
+    ticker = yf.Ticker(stockticker)
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days_ago)
+    start_date = start_date.strftime('%Y-%m-%d')
+    end_date = end_date.strftime('%Y-%m-%d')
+    historical_data = ticker.history(start=start_date, end=end_date)
+    return historical_data
+
+def plot_macd2(df):
+    try:
+        # Debugging: Print the dataframe columns and a few rows
+        print("DataFrame columns:", df.columns)
+        print("DataFrame head:\n", df.head())
+
+        # Convert DataFrame index and columns to numpy arrays
+        index = df.index.to_numpy()
+        close_prices = df['Close'].to_numpy()
+        macd = df['MACD'].to_numpy()
+        signal_line = df['Signal_Line'].to_numpy()
+        macd_histogram = df['MACD_Histogram'].to_numpy()
+
+        fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(10, 8), gridspec_kw={'height_ratios': [3, 1]})
+
+        # Subplot 1: Candlestick chart
+        ax1.plot(index, close_prices, label='Close', color='black')
+        ax1.set_title("Candlestick Chart")
+        ax1.set_ylabel("Price")
+        ax1.legend()
+
+        # Subplot 2: MACD
+        ax2.plot(index, macd, label='MACD', color='blue')
+        ax2.plot(index, signal_line, label='Signal Line', color='red')
+
+        histogram_colors = np.where(macd_histogram >= 0, 'green', 'red')
+        ax2.bar(index, macd_histogram, color=histogram_colors, alpha=0.6)
+
+        ax2.set_title("MACD")
+        ax2.set_ylabel("MACD Value")
+        ax2.legend()
+
+        plt.xlabel("Date")
+        plt.tight_layout()
+
+        return fig
+    except Exception as e:
+        print(f"Error in plot_macd: {e}")
+        return None
+
+def plot_macd(df):
+
+    # Create Figure
+    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, row_heights=[0.2, 0.1],
+                        vertical_spacing=0.15,  # Adjust vertical spacing between subplots
+                        subplot_titles=("Candlestick Chart", "MACD"))  # Add subplot titles
+
+
+    # Subplot 1: Plot candlestick chart
+    fig.add_trace(go.Candlestick(
+        x=df.index,
+        open=df['Open'],
+        high=df['High'],
+        low=df['Low'],
+        close=df['Close'],
+        increasing_line_color='#00cc96',  # Green for increasing
+        decreasing_line_color='#ff3e3e',  # Red for decreasing
+        showlegend=False
+    ), row=1, col=1)  # Specify row and column indices
+
+
+    # Subplot 2: Plot MACD
+    fig.add_trace(
+        go.Scatter(
+            x=df.index,
+            y=df['MACD'],
+            mode='lines',
+            name='MACD',
+            line=dict(color='blue')
+        ),
+        row=2, col=1
+    )
+
+    fig.add_trace(
+        go.Scatter(
+            x=df.index,
+            y=df['Signal_Line'],
+            mode='lines',
+            name='Signal Line',
+            line=dict(color='red')
+        ),
+        row=2, col=1
+    )
+
+    # Plot MACD Histogram with different colors for positive and negative values
+    histogram_colors = ['green' if val >= 0 else 'red' for val in df['MACD_Histogram']]
+
+    fig.add_trace(
+        go.Bar(
+            x=df.index,
+            y=df['MACD_Histogram'],
+            name='MACD Histogram',
+            marker_color=histogram_colors
+        ),
+        row=2, col=1
+    )
+
+    # Update layout with zoom and pan tools enabled
+    layout = go.Layout(
+        title='MSFT Candlestick Chart and MACD Subplots',
+        title_font=dict(size=12),  # Adjust title font size
+        plot_bgcolor='#f2f2f2',  # Light gray background
+        height=600,
+        width=1200,
+        xaxis_rangeslider=dict(visible=True, thickness=0.03),
+    )
+
+    # Update the layout of the entire figure
+    fig.update_layout(layout)
+    fig.update_yaxes(fixedrange=False, row=1, col=1)
+    fig.update_yaxes(fixedrange=True, row=2, col=1)
+    fig.update_xaxes(type='category', row=1, col=1)
+    fig.update_xaxes(type='category', nticks=10, row=2, col=1)
+    
+    fig.show()
+    #return fig
+
+def calculate_MACD(df, fast_period=12, slow_period=26, signal_period=9):
+    """
+    Calculates the MACD (Moving Average Convergence Divergence) and related indicators.
+
+    Parameters:
+        df (DataFrame): A pandas DataFrame containing at least a 'Close' column with closing prices.
+        fast_period (int): The period for the fast EMA (default is 12).
+        slow_period (int): The period for the slow EMA (default is 26).
+        signal_period (int): The period for the signal line EMA (default is 9).
+
+    Returns:
+        DataFrame: A pandas DataFrame with the original data and added columns for MACD, Signal Line, and MACD Histogram.
+    """
+
+    df['EMA_fast'] = df['Close'].ewm(span=fast_period, adjust=False).mean()
+    df['EMA_slow'] = df['Close'].ewm(span=slow_period, adjust=False).mean()
+    df['MACD'] = df['EMA_fast'] - df['EMA_slow']
+
+    df['Signal_Line'] = df['MACD'].ewm(span=signal_period, adjust=False).mean()
+    df['MACD_Histogram'] = df['MACD'] - df['Signal_Line']
+
+    return df