Add application

.gitignore

@@ -0,0 +1,5 @@
+venv
+__pycache__
+.env
+tmp*.pdf
+.DS_Store

Dockerfile

@@ -0,0 +1,59 @@
+# Build stage
+FROM python:3.11-slim-buster AS Build
+
+# Set environment variables for Python and Poetry
+ENV PYTHONUNBUFFERED=1 \
+    PIP_NO_CACHE_DIR=1 \
+    POETRY_NO_INTERACTION=1 \
+    POETRY_VIRTUALENVS_CREATE=false \
+    POETRY_VERSION=1.7.1
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the dependencies file to the working directory
+COPY ./pyproject.toml /app/
+
+# Update, install dependencies, and prepare the Python environment
+RUN apt-get update && \
+    apt-get install -y gcc g++ unixodbc-dev && \
+    pip install "poetry==$POETRY_VERSION" && \
+    poetry export --without-hashes --format requirements.txt --output requirements.txt && \
+    python3 -m pip wheel --no-cache-dir --no-deps -w /app/wheels -r requirements.txt
+
+# Runtime stage
+FROM python:3.11-slim-buster AS Run
+
+# Set environment variables for Python and Poetry
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# Create a non-root user
+RUN useradd -m -u 1000 user
+
+# Switch to the non-root user
+USER user
+
+# Copy wheel files from the build stage
+COPY --from=build /app/wheels $HOME/app/wheels
+
+# Set the working directory to where the wheels are
+WORKDIR $HOME/app/wheels
+
+# Install the wheel files
+RUN pip3 --no-cache-dir install *.whl
+
+# Copy the application files to the working directory (change to your app name)
+COPY --chown=user ./finance-app-tutorial $HOME/app
+
+# Set the working directory to the application files
+WORKDIR $HOME/app
+
+# Specify the command to run the application
+ENTRYPOINT [ "writer", "run" ]
+
+# Expose the port the app runs on
+EXPOSE 8080
+
+# Set the default command to run the app
+CMD [ ".",  "--port", "8080", "--host", "0.0.0.0" ]

README.md

@@ -5,6 +5,7 @@ colorFrom: green
 colorTo: red
 sdk: docker
 pinned: false
+app_port: 8080
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

finance-app-tutorial/README.md

@@ -0,0 +1,3 @@
+This app was created using Writer Framework.
+
+To learn more about it, visit https://dev.writer.com/framework

finance-app-tutorial/charts.py

@@ -0,0 +1,117 @@
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+
+def handle_click(state, context: dict, ui):
+    # Resetting the classes for active button
+    if state["active_button"]:
+        active_button = ui.find(state["active_button"])
+        active_button.content["cssClasses"] = ""
+
+    event_target = context["target"]
+    button = ui.find(event_target)
+
+    # Storing the clicked button ID in state
+    state["active_button"] = event_target
+
+    button_text = button.content["text"]
+    _handle_time_period(state, button_text)
+    button.content["cssClasses"] = "button-click"
+    
+    button_max = ui.find("e13teponreio9yyz")
+    button_max.content["cssClasses"] = ""
+    
+    ui.component_tree.updated = True
+
+def _handle_time_period(state, period):
+    state["main_df_subset"] = state["main_df"]
+    if period == "5D":
+        state["main_df_subset"] = state["main_df_subset"][:5]
+    elif period == "1M":
+        state["main_df_subset"] = state["main_df_subset"][:30]
+    elif period == "3M":
+        state["main_df_subset"] = state["main_df_subset"][:90]
+    elif period == "1Y":
+        state["main_df_subset"] = state["main_df_subset"][:360]
+    elif period == "5Y":
+        state["main_df_subset"] = state["main_df_subset"][:1800]
+    elif period == "Max":
+        # No need to slice, already has the full data
+        pass
+    update_scatter_chart(state)
+
+def update_scatter_chart(state):
+    fig = px.line(state["main_df_subset"], x="Date", y="Open", height=400)
+    
+    df1 = state["main_df_subset"]
+    df2 = state["another_df"]
+    df2 = df2.head(len(df1))
+
+    # Add a new column to each dataframe to identify the source
+    df1["Source"] = "Main_DF"
+    df2["Source"] = "Another_DF"
+
+    # Concatenate the dataframes
+    combined_df = pd.concat([df1, df2])
+    state["main_df_subset"] = combined_df
+
+    # Plot the lines
+    fig = make_subplots(specs=[[{"secondary_y": True}]])
+
+    # Add traces for the primary y-axis (Main_DF)
+    fig.add_trace(
+        go.Scatter(x=df1["Date"], y=df1["Open"], name=state["symbol"], mode='lines'),
+        secondary_y=False,
+    )
+
+    # Add traces for the secondary y-axis (Another_DF)
+    fig.add_trace(
+        go.Scatter(x=df2["Date"], y=df2["Open"], name="S&P 500", mode='lines'),
+        secondary_y=True,
+    )
+
+    # Set axis titles
+    fig.update_yaxes(title_text=f"{state["symbol"]} Stock Price", secondary_y=False)
+    fig.update_yaxes(title_text="S&P 500", secondary_y=True)
+
+    # Update layout
+    fig.update_layout(height=550, title_text=f"{state["symbol"]} Stock vs the S&P 500", title_x = 0.5, title_y = 0.9, legend=dict(
+            orientation='h',
+            yanchor='top',
+            y=-0.2,  # Adjust this value as needed
+            xanchor='center',
+            x=0.5
+        ))
+
+    state["scatter_chart"] = fig
+    
+def update_bar_graph(state):
+    fig = px.line(state["main_df_subset"], x="Date", y="Open", height=400)
+
+    df = state["income_statement_df"]
+    selected_metrics = ["Total Revenue", "Net Income", "Operating Income"]
+    df_filtered = df.loc[selected_metrics]
+
+    # Transpose the DataFrame for easier plotting
+    df_transposed = df_filtered.transpose().reset_index()
+    df_transposed = df_transposed.melt(
+        id_vars=["index"], var_name="Metric", value_name="Value"
+    )
+
+    # Create the bar graph using Plotly Express
+    fig = px.bar(
+        df_transposed,
+        x="index",
+        y="Value",
+        color="Metric",
+        barmode="group",
+        labels={"index": "", "Value": ""},
+        title="Summary of Quarterly Income Statement",
+    )
+
+    fig.update_layout(
+        legend=dict(orientation="h", yanchor="top", y=-0.2, xanchor="center", x=0.5)
+    )
+
+    state["bar_graph"] = fig

finance-app-tutorial/daily_APPLE.csv

@@ -0,0 +1,101 @@
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finance-app-tutorial/daily_IBM.csv

@@ -0,0 +1,101 @@
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+2024-02-27,184.1600,185.1300,182.6200,184.8700,3641378
+2024-02-26,185.6000,186.1250,184.0600,184.1300,4620815
+2024-02-23,184.9000,186.4550,184.5700,185.7200,3433800
+2024-02-22,182.4500,184.5500,181.9300,184.2100,5078398
+2024-02-21,182.5600,183.0300,178.7500,179.7000,4728473
+2024-02-20,187.6400,188.7700,183.0600,183.4400,4247181
+2024-02-16,186.6300,188.9500,185.9452,187.6400,4842840
+2024-02-15,183.6200,186.9800,183.6200,186.8700,4714301
+2024-02-14,185.0000,185.0000,182.2600,183.5700,3173391
+2024-02-13,184.2800,184.7700,182.3600,183.7000,4290453
+2024-02-12,185.9000,186.4800,184.0300,186.1600,4724021
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+2024-02-06,183.5500,184.6800,183.0400,183.4100,3338196
+2024-02-05,185.5100,185.7800,183.2550,183.4200,4379602
+2024-02-02,187.1000,187.3900,185.6150,185.7900,4055411
+2024-02-01,183.6300,187.5100,182.7100,186.9000,4669444
+2024-01-31,187.0500,187.6500,183.1400,183.6600,8876055
+2024-01-30,187.7100,188.6500,186.7700,187.8700,4575058
+2024-01-29,187.4600,189.4600,186.0500,187.1400,6107908
+2024-01-26,191.3100,192.3896,186.1600,187.4200,9895941
+2024-01-25,184.9600,196.9000,184.8300,190.4300,29596239

finance-app-tutorial/main.py

@@ -0,0 +1,183 @@
+import writer as wf
+import writer.ai
+import pandas as pd
+from prompts import stock_prompts, income_prompts, earnings_prompt
+from stock_data import download_data, download_sp500, stock_news, _one_day_data, income_statement, earnings_calls
+from charts import update_scatter_chart
+from dotenv import load_dotenv
+import os
+
+load_dotenv() 
+
+writer.ai.init(os.getenv("WRITER_API_KEY"))
+
+# Update all data
+def updates(state):
+    state["message"] = "% Refreshing stock data..."
+    earnings_calls(state)
+    download_sp500(state)
+    stock_news(state)
+    download_data(state)
+    income_statement(state)
+    update_scatter_chart(state)
+    _one_day_data(state)
+    _refresh_window(state)
+
+# Refresh the window
+def _refresh_window(state):
+    state["show_income_metrics"]["visible"] = False
+    state["show_bar_graph"]["visible"] = False
+    state["show_analysis_text"]["visible"] = False
+    state["show_analysis_text"]["language"] = False
+    state["message"] = "Writer AI insights will be generated here"
+
+# Summarize earnings call using Palmyra-Fin model
+def summarize_earnings(state):
+    _refresh_window(state)  
+    state["message"] = f"% {state["symbol"]} earnings call will be summarized here"
+    
+    earnings_transcript = state["earnings_transcript"]
+    prompt = earnings_prompt.format(earnings_transcript=earnings_transcript)
+    submission = writer.ai.complete(prompt, config={"model": "palmyra-fin-32k", "temperature": 0.7, "max_tokens": 8192})
+    state["message"] = f"+ {state["symbol"]} earnings call summary"
+    state["analysis"] = submission.strip()
+    state["show_analysis_text"]["visible"] = True
+    
+def prompt_parameters_words(state,payload):
+    state["prompt_parameters_words"] = payload
+    _refresh_window(state)
+    
+def prompt_parameters_lang(state,payload):
+    state["prompt_parameters_lang"] = payload
+    generate_stock_analysis(state)
+    
+def generate_stock_analysis(state):
+    _refresh_window(state)  
+    if(state["prompt_parameters_lang"] == ""):
+        state["prompt_parameters_lang"] == "English"
+
+    state["message"] = f"% {state["symbol"]} trends will be analyzed here in {state['prompt_parameters_lang']}"
+    stock_name = state["symbol"]
+    stock_data = state["main_df"][:365]
+        
+    rounded_value = round(state["prompt_parameters_words"], 0)
+    language = state["prompt_parameters_lang"]
+    # Convert the rounded value to an integer
+    integer_value = int(rounded_value)
+
+    prompt = stock_prompts.format(language=language, stock_name=stock_name,words=integer_value,stock_data=stock_data)
+    submission = writer.ai.complete(prompt, config={"model": "palmyra-fin-32k", "temperature": 0.7, "max_tokens": 8192})
+    state["analysis"] = submission.strip()
+    state["message"] = f"+ {state["symbol"]} trends analyzed"
+    state["show_analysis_text"]["visible"] = True
+    state["show_analysis_text"]["language"] = True
+        
+    return submission
+
+
+def generate_income_analysis(state):
+    _refresh_window(state)
+    state["message"] = f"% {state["symbol"]} income statement will be visualized here"
+    stock_name = state["symbol"]
+    stock_data = state["main_df"][:365]
+    income_statement_data = state["income_statement_df"][:365]
+    prompt = income_prompts.format(
+        income_statement_data=income_statement_data,
+        stock_data=stock_data,
+        stock_name=stock_name,
+    )
+    submission = writer.ai.complete(prompt, config={"model": "palmyra-fin-32k", "temperature": 0.7, "max_tokens": 8192})
+    state["analysis"] = submission.strip()
+    state["message"] = f"+ {state["symbol"]} income statement visualized"
+    state["show_income_metrics"]["visible"] = True
+    state["show_analysis_text"]["visible"] = True
+    state["show_bar_graph"]["visible"] = True
+    return submission
+
+def stock_tags(state, payload):
+    state["symbol"] = payload
+    updates(state)
+
+def _get_main_df(filename):
+    main_df = pd.read_csv(filename)
+    return main_df 
+
+initial_state = wf.init_state(
+    {
+        "last_24_hours_open": "168.76",
+        "last_24_hours_high": "169.72",
+        "last_24_hours_low": "167.50",
+        "message": None,
+        "main_df": _get_main_df("daily_IBM.csv"),
+        "main_df_subset": _get_main_df("daily_IBM.csv"),
+        "symbol": "AAPL",
+        "articles": {
+            "EU seeks views on Microsoft, OpenAI, Google and Samsung deals, EU's Vestager says": {
+                "source": "Reuters",
+                "published_at": "June 28, 2024 at 15:40",
+                "url": "https://finance.yahoo.com/news/eu-seeks-views-microsoft-openai-144044802.html",
+            },
+            "Forget the S&P 500 -- Buy This Magnificent ETF Instead": {
+                "source": "Motley Fool",
+                "published_at": "June 28, 2024 at 15:00",
+                "url": "https://finance.yahoo.com/m/662fcc10-7b6c-3bd1-8b7f-6cc25e8a1e61/forget-the-s%26p-500-buy.html",
+            },
+            "Microsoft Corporation (MSFT) is Attracting Investor Attention: Here is What You Should Know": {
+                "source": "Zacks",
+                "published_at": "June 28, 2024 at 14:00",
+                "url": "https://finance.yahoo.com/news/microsoft-corporation-msft-attracting-investor-130015018.html",
+            },
+        },
+        "show_analysis_text": {
+            "visible": False,
+            "language": False
+        },
+        "show_income_metrics": {
+            "visible": False,
+        },
+        "tab_message": "- **Performance** tab highlights stock trends using an interactive graph where time filters can be selected.<br><li>**Stock data** tab shows the stock data from Yahoo Finance. <br><li>**Income data** tab shows the income statement from Yahoo Finance. <br><li>**View 10-K** tab shows the selected stock 10-K in a PDF viewer shown via an _API integration_.",
+        "prompt_parameters_lang": "English",
+        "prompt_parameters_words": 100,
+        "message": "Writer AI insights will be generated here",
+        "show_bar_graph": {"visible": False},
+        "output_language": {
+            "English": "English",
+            "Arabic": "Arabic",
+            "Bengali": "Bengali",
+            "Bulgarian": "Bulgarian",
+            "Chinese simplified": "Chinese simplified",
+            "Chinese traditional": "Chinese traditional",
+            "Croatian": "Croatian",
+            "Czech": "Czech",
+            "Danish": "Danish",
+            "Dutch": "Dutch",
+            "Finnish": "Finnish",
+            "French": "French",
+            "German": "German",
+            "Greek": "Greek",
+            "Hebrew": "Hebrew",
+            "Hindi": "Hindi",
+            "Hungarian": "Hungarian",
+            "Indonesian": "Indonesian",
+            "Italian": "Italian",
+            "Japanese": "Japanese",
+            "Korean": "Korean",
+            "Lithuanian": "Lithuanian",
+            "Polish": "Polish",
+            "Portuguese": "Portuguese",
+            "Romanian": "Romanian",
+            "Russian": "Russian",
+            "Spanish": "Spanish",
+            "Swahili": "Swahili",
+            "Swedish": "Swedish",
+            "Thai": "Thai",
+            "Turkish": "Turkish",
+            "Ukrainian": "Ukrainian",
+            "Vietnamese": "Vietnamese",
+        },
+    }
+)
+
+updates(initial_state)
+
+initial_state.import_stylesheet("theme", "/static/custom.css?18")

finance-app-tutorial/prompts.py

@@ -0,0 +1,134 @@
+# BASE PROMPTS (TAILORED TO EACH COMPANY)
+
+# base_prompts = {}
+
+stock_prompts = """
+
+Variables:
+
+{language}, {stock_name}, {words}, {stock_data}
+
+************************
+
+Prompt:
+You will be acting as a stock market analyst. When I provide the stock data in the specified format between the <stock_data> tags, your task is to carefully review the data and provide insights, trends, and financial analysis for the stock specified in the {stock_name} variable.
+
+<stock_data>
+{stock_data}
+</stock_data>
+
+Please analyze the stock data concisely in {words} words using paragraphs, considering the following steps:
+
+1. Identify any notable trends in the stock's price, volume, or other key metrics over the given time period. Discuss potential reasons behind these trends.
+
+2. Compare the stock's performance and financial metrics to industry averages and key competitors. Discuss how the stock stacks up against its peers.
+
+3. Based on your analysis, provide an overall assessment of the stock's current position and future prospects. Consider factors such as growth potential, risk level, and market sentiment.
+
+Before giving your final recommendation, please provide detailed reasoning and analysis to support your conclusions inside <reasoning> tags.
+
+Finally, offer a clear recommendation inside <recommendation> tags on whether to buy, hold, or sell {stock_name}, taking into account both the stock's current valuation and its long-term potential.
+
+Remember to base your analysis and recommendation solely on the provided stock data for
+{stock_name}. If there is insufficient information to draw a conclusion, state this limitation in your response.
+
+Output the analysis in {language}.
+
+"""
+
+income_prompts = """
+
+Variables:
+
+{income_statement_data}, {stock_name}
+
+************************
+
+Prompt:
+You are a financial analyst tasked with analyzing the income statement of a
+company. To assist in your analysis, you have been provided with the following data:
+
+<stock_name>
+{stock_name}
+</stock_name>
+
+<income_statement_data>
+{income_statement_data}
+</income_statement_data>
+
+Using the provided data, please conduct an executive summary analysis in 100 words, using paragraphs, of the company's financial health and
+future prospects. Your analysis should include:
+
+<scratchpad>
+- Analyze key financial metrics from the income statement, such as revenue growth, profit margins,
+and expenses
+- Assess the company's profitability and efficiency based on the income statement data
+- Identify any potential risks or opportunities for the company based on the financial data
+- Consider industry trends and market conditions that may impact the company's performance
+</scratchpad>
+
+After conducting your analysis, please provide your findings and conclusions in the following
+format:
+
+<analysis>
+Income Statement Analysis:
+- [Discuss the company's revenue growth and profitability based on the income statement data]
+- [Identify any significant changes or trends in expenses or profit margins]
+- [Assess the company's overall financial health and efficiency based on the income statement
+metrics]
+
+Risks and Opportunities:
+- [Identify any potential risks or challenges the company may face based on the financial data]
+- [Discuss any opportunities for growth or improvement based on the analysis]
+
+Conclusion:
+- [Provide a summary of your overall assessment of the company's financial performance and future
+prospects]
+- [Include any recommendations or insights for investors or stakeholders]
+</analysis>
+
+Please ensure that your analysis is clear, concise, and well-supported by the provided financial
+data. Use specific examples and figures from the stock data and income statement to support your
+conclusions.
+
+"""
+
+earnings_prompt = """
+
+Variables:
+
+{earnings_transcript}
+
+************************
+
+Prompt:
+You are an expert at analyzing quarterly earnings reports. Please carefully review the following earnings call transcript:
+
+<transcript>
+{earnings_transcript}
+</transcript>
+
+After reading through the transcript, take some time to think through the key insights and takeaways
+in a <scratchpad>. Consider the following aspects:
+- Financial performance: How did the company perform financially this quarter? Were revenue,
+profits, margins, etc. up or down compared to prior periods? Did they meet, exceed or fall short of
+expectations?
+- Future outlook: What is the company's outlook for upcoming quarters? Are they optimistic or
+cautious? What are their projections for key metrics?
+- Strategic initiatives: Did the company discuss any major strategic initiatives, partnerships, new
+products, expansion plans or other projects? What is the rationale and potential impact?
+- Significant changes: Were there any notable leadership changes, restructurings, pivots in strategy
+or other significant developments disclosed?
+</scratchpad>
+
+Once you've thought through the main points, please provide a summary of the key insights from the
+earnings call. The summary should concisely hit on the major takeaways around
+financial performance, outlook, strategy and changes while including specific details that support
+the main points. Aim for around 4-6 paragraphs.
+
+Remember, your goal is to extract and communicate the most important information from the earnings
+call in a clear, insightful executive summary. Focus on the high-level story and don't get too in
+the weeds with minor details. Put yourself in the shoes of an analyst or investor and highlight what
+you think they would care about most.
+
+"""

finance-app-tutorial/static/README.md

@@ -0,0 +1,8 @@
+# Serving static files
+
+You can use this folder to store files which will be served statically in the "/static" route.
+
+This is useful to store images and other files which will be served directly to the user of your application.
+
+For example, if you store an image named "myimage.jpg" in this folder, it'll be accessible as "static/myimage.jpg".
+You can use this relative route as the source in an Image component.

finance-app-tutorial/static/custom.css

@@ -0,0 +1,12 @@
+.link {
+    text-align: right !important;
+}
+
+.button-click {
+    background-color: #5551ff !important;
+    color: white !important;
+}
+
+.custom-iframe {
+    height: 700px !important;
+}

finance-app-tutorial/stock_data.py

@@ -0,0 +1,91 @@
+import pandas as pd
+from datetime import datetime
+import yfinance as yf
+from charts import update_bar_graph
+from dotenv import load_dotenv
+import json
+
+load_dotenv()
+
+# Download stock data and format into a DataFrame
+def download_data(state):
+    df = yf.download(state["symbol"], period="max", interval="1d")
+    df = df.reset_index()
+    df = df.sort_values(by="Date", ascending=False)
+    df = df.round({"Open": 2, "High": 2, "Low": 2, "Close": 2, "Adj Close": 2})
+    df["Date"] = pd.to_datetime(df["Date"])
+    state["main_df_subset"] = df
+    state["main_df"] = state["main_df_subset"]
+
+# Download S&P 500 data and format into a DataFrame
+def download_sp500(state):
+    df = yf.download(tickers="^GSPC", period="max", interval="1d")
+    df = df.reset_index()
+    df = df.sort_values(by="Date", ascending=False)
+    df = df.round({"Open": 2, "High": 2, "Low": 2, "Close": 2, "Adj Close": 2})
+    df["Date"] = pd.to_datetime(df["Date"])
+    state["another_df"] = df
+
+# Retrieve latest stock news   
+def stock_news(state):
+    msft = yf.Ticker(state["symbol"])
+    articles = {}
+    data = msft.news
+    latest_articles = sorted(
+        data, key=lambda x: x["providerPublishTime"], reverse=True
+    )[:4]
+
+    for item in latest_articles:
+        provider_publish_time = item.get("providerPublishTime", "")
+        if provider_publish_time:
+            # Convert the timestamp to a readable date
+            formatted_date = datetime.fromtimestamp(provider_publish_time)
+            readable_date = formatted_date.strftime("%B %d, %Y at %H:%M")
+        else:
+            readable_date = "Date not available"
+
+        title = item.get("title", "No Title")
+        articles[title] = {
+            "source": item.get("publisher", ""),
+            "published_at": readable_date,
+            "url": item.get("link", ""),
+        }
+    state["articles"] = articles
+
+# Retrieve income statement data
+def income_statement(state):
+    quarterly_income_stmt = yf.Ticker(state["symbol"]).quarterly_income_stmt
+    df = pd.DataFrame(quarterly_income_stmt)
+    df.columns = pd.to_datetime(df.columns).strftime("%Y-%m-%d")
+    state["income_statement_df"] = df
+    update_bar_graph(state)
+    show_fin_metrics(state)
+
+# Show financial metrics
+def show_fin_metrics(state):
+    stock = yf.Ticker(state["symbol"])
+    operating_margins = stock.info['operatingMargins']
+    gross_margin = stock.info['grossMargins']
+    ebitda_margin = stock.info['ebitdaMargins']
+    
+    state["operating_margin"] = f"{operating_margins * 100:.2f}%"
+    state["gross_margin"] = f"{gross_margin * 100:.2f}%"
+    state["ebitda_margin"] = f"{ebitda_margin * 100:.2f}%"
+  
+def _one_day_data(state):
+    state["last_24_hours_open"] = round(state["main_df"]["Open"].iloc[0], 2)
+    state["last_24_hours_high"] = round(state["main_df"]["High"].iloc[0], 2)
+    state["last_24_hours_low"] = round(state["main_df"]["Low"].iloc[0], 2)
+
+# Retrieve earnings call transcript from JSON file
+# You could replace this with a call to an API like Financial Modeling Prep
+def earnings_calls(state):
+    ticker = state["symbol"]
+    with open('earnings-data.json', 'r') as file:
+        earnings_transcript = json.load(file)
+    if earnings_transcript:
+        for item in earnings_transcript:
+            if item['symbol'] == ticker:
+                state["earnings_transcript"] = item["content"]
+    else:
+        print("No earnings transcript found.")

finance-app-tutorial/ui.json

@@ -0,0 +1,749 @@
+{
+    "metadata": {
+        "writer_version": "0.7.0rc2"
+    },
+    "components": {
+        "root": {
+            "id": "root",
+            "type": "root",
+            "content": {
+                "appName": "Finance Dashboard"
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "handlers": {},
+            "visible": true
+        },
+        "c0f99a9e-5004-4e75-a6c6-36f17490b134": {
+            "id": "c0f99a9e-5004-4e75-a6c6-36f17490b134",
+            "type": "page",
+            "content": {
+                "pageMode": ""
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "root",
+            "handlers": {},
+            "visible": true
+        },
+        "bebc5fe9-63a7-46a7-b0fa-62303555cfaf": {
+            "id": "bebc5fe9-63a7-46a7-b0fa-62303555cfaf",
+            "type": "header",
+            "content": {
+                "text": "Finance Research Dashboard"
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "c0f99a9e-5004-4e75-a6c6-36f17490b134",
+            "handlers": {},
+            "visible": true
+        },
+        "ahwykl4cny005thy": {
+            "id": "ahwykl4cny005thy",
+            "type": "dataframe",
+            "content": {
+                "dataframe": "@{main_df_subset}",
+                "showIndex": "no",
+                "enableSearch": "no"
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "vyij4ox2ad95xyr9",
+            "handlers": {},
+            "visible": true
+        },
+        "opn16nq04zq9xha8": {
+            "id": "opn16nq04zq9xha8",
+            "type": "plotlygraph",
+            "content": {
+                "spec": "@{scatter_chart}"
+            },
+            "isCodeManaged": false,
+            "position": 2,
+            "parentId": "1db76a00z7imlyz1",
+            "handlers": {
+                "plotly-click": "charts.update_scatter_chart"
+            }
+        },
+        "e13teponreio9yyz": {
+            "id": "e13teponreio9yyz",
+            "type": "button",
+            "content": {
+                "text": "Max",
+                "buttonColor": "#BFCBFF",
+                "cssClasses": "",
+                "buttonTextColor": "#000000"
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "g5mgb80xt38atz4f",
+            "handlers": {
+                "wf-click": "charts.handle_click"
+            },
+            "visible": true
+        },
+        "g5mgb80xt38atz4f": {
+            "id": "g5mgb80xt38atz4f",
+            "type": "horizontalstack",
+            "content": {},
+            "isCodeManaged": false,
+            "position": 1,
+            "parentId": "1db76a00z7imlyz1",
+            "handlers": {},
+            "visible": true
+        },
+        "juip2rw7hywumv35": {
+            "id": "juip2rw7hywumv35",
+            "type": "button",
+            "content": {
+                "text": "5D",
+                "buttonColor": "#BFCBFF",
+                "cssClasses": "",
+                "buttonTextColor": "#000000"
+            },
+            "isCodeManaged": false,
+            "position": 1,
+            "parentId": "g5mgb80xt38atz4f",
+            "handlers": {
+                "wf-click": "charts.handle_click"
+            },
+            "visible": true
+        },
+        "afamuurw1lpjkkze": {
+            "id": "afamuurw1lpjkkze",
+            "type": "button",
+            "content": {
+                "text": "1M",
+                "buttonColor": "#BFCBFF",
+                "cssClasses": "",
+                "buttonTextColor": "#000000"
+            },
+            "isCodeManaged": false,
+            "position": 2,
+            "parentId": "g5mgb80xt38atz4f",
+            "handlers": {
+                "wf-click": "charts.handle_click"
+            },
+            "visible": true
+        },
+        "77u7x4pf0ximsf0j": {
+            "id": "77u7x4pf0ximsf0j",
+            "type": "button",
+            "content": {
+                "text": "3M",
+                "buttonColor": "#BFCBFF",
+                "cssClasses": "",
+                "buttonTextColor": "#000000"
+            },
+            "isCodeManaged": false,
+            "position": 3,
+            "parentId": "g5mgb80xt38atz4f",
+            "handlers": {
+                "wf-click": "charts.handle_click"
+            },
+            "visible": true
+        },
+        "oh2g51ixvuu8gdwa": {
+            "id": "oh2g51ixvuu8gdwa",
+            "type": "metric",
+            "content": {
+                "name": "Open",
+                "note": "L24",
+                "description": "",
+                "metricValue": "@{last_24_hours_open}"
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "rgv1dgdrvgiosh42",
+            "handlers": {},
+            "visible": true
+        },
+        "rgv1dgdrvgiosh42": {
+            "id": "rgv1dgdrvgiosh42",
+            "type": "horizontalstack",
+            "content": {},
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "1db76a00z7imlyz1",
+            "handlers": {},
+            "visible": true
+        },
+        "6646510g33an9r0g": {
+            "id": "6646510g33an9r0g",
+            "type": "metric",
+            "content": {
+                "note": "L24",
+                "name": "High",
+                "description": "",
+                "metricValue": "@{last_24_hours_high}"
+            },
+            "isCodeManaged": false,
+            "position": 2,
+            "parentId": "rgv1dgdrvgiosh42",
+            "handlers": {},
+            "visible": true
+        },
+        "df3mr8my8ivc8yrt": {
+            "id": "df3mr8my8ivc8yrt",
+            "type": "metric",
+            "content": {
+                "note": "L24",
+                "name": "Low",
+                "description": "",
+                "metricValue": "@{last_24_hours_low}"
+            },
+            "isCodeManaged": false,
+            "position": 4,
+            "parentId": "rgv1dgdrvgiosh42",
+            "handlers": {},
+            "visible": true
+        },
+        "9v92jyh672s5wjkg": {
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+            "type": "tab",
+            "content": {
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+            "position": 1,
+            "parentId": "yzujnjalyh2s2x16",
+            "handlers": {},
+            "visible": true
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+            "isCodeManaged": false,
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+            "handlers": {},
+            "visible": true
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+        "nzp2whzndpknrk55": {
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+            "visible": true
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+        "suu5ceib1bqj0si5": {
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+            "type": "columns",
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+            "handlers": {},
+            "visible": true
+        },
+        "vyij4ox2ad95xyr9": {
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+            "type": "column",
+            "content": {
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+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "suu5ceib1bqj0si5",
+            "handlers": {},
+            "visible": true
+        },
+        "chwivjdj78wyyi9x": {
+            "id": "chwivjdj78wyyi9x",
+            "type": "button",
+            "content": {
+                "text": "Analyze trends"
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "1yuilwwrhm40gxtx",
+            "handlers": {
+                "wf-click": "generate_stock_analysis"
+            },
+            "visible": true
+        },
+        "km4vvfnq6bwlakmp": {
+            "id": "km4vvfnq6bwlakmp",
+            "type": "text",
+            "content": {
+                "text": "@{analysis}",
+                "useMarkdown": "yes"
+            },
+            "isCodeManaged": false,
+            "position": 4,
+            "parentId": "i23vdas64ziuiazm",
+            "handlers": {},
+            "visible": "show_analysis_text.visible"
+        },
+        "h72djsz1oqncb5ls": {
+            "id": "h72djsz1oqncb5ls",
+            "type": "section",
+            "content": {
+                "title": "Latest news"
+            },
+            "isCodeManaged": false,
+            "position": 1,
+            "parentId": "0p1levffmcf4xlpl",
+            "handlers": {},
+            "visible": true
+        },
+        "zinu9f9cyur23m66": {
+            "id": "zinu9f9cyur23m66",
+            "type": "repeater",
+            "content": {
+                "repeaterObject": "@{articles}",
+                "keyVariable": "itemId",
+                "valueVariable": "item"
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "h72djsz1oqncb5ls",
+            "handlers": {},
+            "visible": true
+        },
+        "dbdmqg2z74k4frq4": {
+            "id": "dbdmqg2z74k4frq4",
+            "type": "columns",
+            "content": {
+                "cssClasses": ""
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+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "zinu9f9cyur23m66",
+            "handlers": {},
+            "visible": true
+        },
+        "p13mw4f6rr4z8d47": {
+            "id": "p13mw4f6rr4z8d47",
+            "type": "column",
+            "content": {
+                "title": "",
+                "width": "1",
+                "isSticky": "yes",
+                "isCollapsible": "",
+                "startCollapsed": "",
+                "cssClasses": ""
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "dbdmqg2z74k4frq4",
+            "handlers": {},
+            "visible": true
+        },
+        "tji08jpu1y52kzz1": {
+            "id": "tji08jpu1y52kzz1",
+            "type": "link",
+            "content": {
+                "url": "@{item.url}",
+                "text": "@{item.source}",
+                "primaryTextColor": "#1d53d3",
+                "cssClasses": "link"
+            },
+            "isCodeManaged": false,
+            "position": 1,
+            "parentId": "4avlmncllyffxniu",
+            "handlers": {},
+            "visible": true
+        },
+        "4avlmncllyffxniu": {
+            "id": "4avlmncllyffxniu",
+            "type": "section",
+            "content": {
+                "title": "@{itemId}",
+                "primaryTextColor": "#000000",
+                "containerBackgroundColor": "#f7f7f7",
+                "containerShadow": "5px 5px 5px 2px #f8f7f7"
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "p13mw4f6rr4z8d47",
+            "handlers": {},
+            "visible": true
+        },
+        "w3wt3976ggvcuwrg": {
+            "id": "w3wt3976ggvcuwrg",
+            "type": "text",
+            "content": {
+                "text": "**@{item.published_at}**",
+                "alignment": "right",
+                "useMarkdown": "yes"
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "4avlmncllyffxniu",
+            "handlers": {},
+            "visible": true
+        },
+        "1db76a00z7imlyz1": {
+            "id": "1db76a00z7imlyz1",
+            "type": "section",
+            "content": {
+                "title": "@{symbol} stock performance"
+            },
+            "isCodeManaged": false,
+            "position": 1,
+            "parentId": "18wepug77yr8m1iu",
+            "handlers": {},
+            "visible": true
+        },
+        "i23vdas64ziuiazm": {
+            "id": "i23vdas64ziuiazm",
+            "type": "section",
+            "content": {
+                "title": ""
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "0p1levffmcf4xlpl",
+            "handlers": {},
+            "visible": true
+        },
+        "8dbcbgiukyg74dkb": {
+            "id": "8dbcbgiukyg74dkb",
+            "type": "columns",
+            "content": {},
+            "isCodeManaged": false,
+            "position": 3,
+            "parentId": "c0f99a9e-5004-4e75-a6c6-36f17490b134",
+            "handlers": {},
+            "visible": true
+        },
+        "0p1levffmcf4xlpl": {
+            "id": "0p1levffmcf4xlpl",
+            "type": "column",
+            "content": {
+                "width": "0.75"
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "8dbcbgiukyg74dkb",
+            "handlers": {},
+            "visible": true
+        },
+        "334d69p14onyi9wq": {
+            "id": "334d69p14onyi9wq",
+            "type": "column",
+            "content": {
+                "width": "1"
+            },
+            "isCodeManaged": false,
+            "position": 1,
+            "parentId": "8dbcbgiukyg74dkb",
+            "handlers": {},
+            "visible": true
+        },
+        "yzujnjalyh2s2x16": {
+            "id": "yzujnjalyh2s2x16",
+            "type": "tabs",
+            "content": {},
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "334d69p14onyi9wq",
+            "handlers": {},
+            "visible": true
+        },
+        "18wepug77yr8m1iu": {
+            "id": "18wepug77yr8m1iu",
+            "type": "tab",
+            "content": {
+                "name": "Performance"
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "yzujnjalyh2s2x16",
+            "handlers": {}
+        },
+        "9eieaedqpr1ickbb": {
+            "id": "9eieaedqpr1ickbb",
+            "type": "section",
+            "content": {
+                "title": "Investment research options",
+                "cssClasses": "",
+                "containerBackgroundColor": "#e9d3fd"
+            },
+            "isCodeManaged": false,
+            "position": 2,
+            "parentId": "c0f99a9e-5004-4e75-a6c6-36f17490b134",
+            "handlers": {},
+            "visible": true
+        },
+        "1yuilwwrhm40gxtx": {
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+            "type": "horizontalstack",
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+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "9eieaedqpr1ickbb",
+            "handlers": {},
+            "visible": true
+        },
+        "1vyfwym5dfh07ihb": {
+            "id": "1vyfwym5dfh07ihb",
+            "type": "message",
+            "content": {
+                "message": "@{message}"
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "i23vdas64ziuiazm",
+            "handlers": {},
+            "visible": true
+        },
+        "fwmg7b84wstpxs7m": {
+            "id": "fwmg7b84wstpxs7m",
+            "type": "button",
+            "content": {
+                "text": "1Y",
+                "buttonColor": "#BFCBFF",
+                "cssClasses": "",
+                "buttonTextColor": "#000000"
+            },
+            "isCodeManaged": false,
+            "position": 4,
+            "parentId": "g5mgb80xt38atz4f",
+            "handlers": {
+                "wf-click": "charts.handle_click"
+            },
+            "visible": true
+        },
+        "jcs5oslp27w5v52j": {
+            "id": "jcs5oslp27w5v52j",
+            "type": "button",
+            "content": {
+                "text": "5Y",
+                "buttonColor": "#BFCBFF",
+                "cssClasses": "",
+                "buttonTextColor": "#000000"
+            },
+            "isCodeManaged": false,
+            "position": 5,
+            "parentId": "g5mgb80xt38atz4f",
+            "handlers": {
+                "wf-click": "charts.handle_click"
+            },
+            "visible": true
+        },
+        "s2jsn9u5xb23hase": {
+            "id": "s2jsn9u5xb23hase",
+            "type": "tags",
+            "content": {
+                "tags": "{\n  \"AAPL\": \"AAPL\",\n  \"IBM\": \"IBM\",\n  \"NVDA\": \"NVDA\",\n  \"MSFT\": \"MSFT\",\n  \"TSLA\": \"TSLA\"\n}"
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "kr54hbg2r5fkhsct",
+            "handlers": {
+                "wf-tag-click": "stock_tags"
+            },
+            "visible": true
+        },
+        "kr54hbg2r5fkhsct": {
+            "id": "kr54hbg2r5fkhsct",
+            "type": "section",
+            "content": {
+                "title": "Select a stock ticker"
+            },
+            "isCodeManaged": false,
+            "position": 1,
+            "parentId": "c0f99a9e-5004-4e75-a6c6-36f17490b134",
+            "handlers": {},
+            "visible": true
+        },
+        "8yxe1itvlcq4zhpi": {
+            "id": "8yxe1itvlcq4zhpi",
+            "type": "tab",
+            "content": {
+                "name": "Income data"
+            },
+            "isCodeManaged": false,
+            "position": 2,
+            "parentId": "yzujnjalyh2s2x16",
+            "handlers": {},
+            "visible": true
+        },
+        "tmiso5qe0e0jfl4i": {
+            "id": "tmiso5qe0e0jfl4i",
+            "type": "dataframe",
+            "content": {
+                "dataframe": "@{income_statement_df}"
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "8yxe1itvlcq4zhpi",
+            "handlers": {},
+            "visible": true
+        },
+        "x6p3vxia2xlpbbib": {
+            "id": "x6p3vxia2xlpbbib",
+            "type": "button",
+            "content": {
+                "text": "Visualize income statement"
+            },
+            "isCodeManaged": false,
+            "position": 1,
+            "parentId": "1yuilwwrhm40gxtx",
+            "handlers": {
+                "wf-click": "generate_income_analysis"
+            },
+            "visible": true
+        },
+        "oo7y10uo68z3ayf8": {
+            "id": "oo7y10uo68z3ayf8",
+            "type": "plotlygraph",
+            "content": {
+                "spec": "@{bar_graph}"
+            },
+            "isCodeManaged": false,
+            "position": 2,
+            "parentId": "i23vdas64ziuiazm",
+            "handlers": {
+                "plotly-click": "charts.update_bar_graph"
+            },
+            "visible": "show_bar_graph.visible"
+        },
+        "a8a854zgzdrcel9j": {
+            "id": "a8a854zgzdrcel9j",
+            "type": "dropdowninput",
+            "content": {
+                "label": "Output language",
+                "options": "@{output_language}"
+            },
+            "isCodeManaged": false,
+            "position": 3,
+            "parentId": "i23vdas64ziuiazm",
+            "handlers": {
+                "wf-option-change": "prompt_parameters_lang"
+            },
+            "visible": "show_analysis_text.language"
+        },
+        "1c3vvmgyhvkm9d6e": {
+            "id": "1c3vvmgyhvkm9d6e",
+            "type": "horizontalstack",
+            "content": {},
+            "isCodeManaged": false,
+            "position": 1,
+            "parentId": "i23vdas64ziuiazm",
+            "handlers": {},
+            "visible": "show_income_metrics.visible"
+        },
+        "e7kux7h89zec8cmh": {
+            "id": "e7kux7h89zec8cmh",
+            "type": "metric",
+            "content": {
+                "name": "Operating Margin",
+                "note": "",
+                "metricValue": "@{operating_margin}"
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "1c3vvmgyhvkm9d6e",
+            "handlers": {},
+            "visible": true
+        },
+        "0u3afw3gbxbj8h1x": {
+            "id": "0u3afw3gbxbj8h1x",
+            "type": "separator",
+            "content": {},
+            "isCodeManaged": false,
+            "position": 1,
+            "parentId": "1c3vvmgyhvkm9d6e",
+            "handlers": {},
+            "visible": true
+        },
+        "yxm3uezkrgpl11fc": {
+            "id": "yxm3uezkrgpl11fc",
+            "type": "metric",
+            "content": {
+                "name": "Gross Margin",
+                "note": "",
+                "metricValue": "@{gross_margin}"
+            },
+            "isCodeManaged": false,
+            "position": 2,
+            "parentId": "1c3vvmgyhvkm9d6e",
+            "handlers": {},
+            "visible": true
+        },
+        "nmdddxrvpgirasj6": {
+            "id": "nmdddxrvpgirasj6",
+            "type": "metric",
+            "content": {
+                "name": "EBIDTA Margin",
+                "note": "",
+                "metricValue": "@{ebitda_margin}"
+            },
+            "isCodeManaged": false,
+            "position": 4,
+            "parentId": "1c3vvmgyhvkm9d6e",
+            "handlers": {},
+            "visible": true
+        },
+        "xlzf1vlkmxokw9yj": {
+            "id": "xlzf1vlkmxokw9yj",
+            "type": "separator",
+            "content": {},
+            "isCodeManaged": false,
+            "position": 3,
+            "parentId": "1c3vvmgyhvkm9d6e",
+            "handlers": {},
+            "visible": true
+        },
+        "c52e2wkln9shvcoq": {
+            "id": "c52e2wkln9shvcoq",
+            "type": "section",
+            "content": {
+                "title": "",
+                "containerBackgroundColor": "#FFE999"
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "18wepug77yr8m1iu",
+            "handlers": {},
+            "visible": "demo_mode.visible"
+        },
+        "3knng5h0zv8i3xra": {
+            "id": "3knng5h0zv8i3xra",
+            "type": "text",
+            "content": {
+                "text": "@{tab_message}",
+                "useMarkdown": "yes"
+            },
+            "isCodeManaged": false,
+            "position": 0,
+            "parentId": "c52e2wkln9shvcoq",
+            "handlers": {},
+            "visible": ""
+        },
+        "rhomvmilcrm8f2r1": {
+            "id": "rhomvmilcrm8f2r1",
+            "type": "button",
+            "content": {
+                "text": "Summarize earnings"
+            },
+            "isCodeManaged": false,
+            "position": 2,
+            "parentId": "1yuilwwrhm40gxtx",
+            "handlers": {
+                "wf-click": "summarize_earnings"
+            },
+            "visible": true
+        },
+        "1o0ui3hahmjblams": {
+            "id": "1o0ui3hahmjblams",
+            "type": "text",
+            "parentId": "c0f99a9e-5004-4e75-a6c6-36f17490b134",
+            "content": {
+                "text": "Made with \u2764\ufe0f using [Writer Framework](https://github.com/writer/writer-framework/)",
+                "useMarkdown": "yes",
+                "alignment": "center"
+            },
+            "handlers": {},
+            "position": 4,
+            "visible": true
+        }
+    }
+}

pyproject.toml

@@ -0,0 +1,15 @@
+[tool.poetry]
+name = "writer-framework-default"
+version = "0.1.0"
+description = ""
+authors = ["Your Name <[email protected]>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.10.0"
+writer = {version = "^0.6.0"}
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"