Update app.py

app.py

@@ -1,3 +1,139 @@
+import streamlit as st
+import requests
+import pandas as pd
+
+# Global API key and default parameters
+API_KEY = os.getenv("FMP_API_KEY")
+DEFAULT_COMPANY = "syros"
+NUM_PAGES = 10  # Backend variable: number of pages to fetch
+
+# Initialize session state run flags and inputs if not already set
+if "run_mna_feed" not in st.session_state:
+    st.session_state.run_mna_feed = False
+if "run_mna_search" not in st.session_state:
+    st.session_state.run_mna_search = False
+if "search_company" not in st.session_state:
+    st.session_state.search_company = DEFAULT_COMPANY
+if "feed_date" not in st.session_state:
+    st.session_state.feed_date = pd.to_datetime("2025-01-01").date()
+
+##############################################
+# CACHED FUNCTIONS
+##############################################
+@st.cache_data(show_spinner=False)
+def fetch_mna_feed(num_pages=NUM_PAGES) -> pd.DataFrame:
+    """
+    Fetch the general M&A RSS feed data from multiple pages.
+    """
+    frames = []
+    for page in range(num_pages):
+        url = f"https://financialmodelingprep.com/api/v4/mergers-acquisitions-rss-feed?page={page}&apikey={API_KEY}"
+        response = requests.get(url)
+        response.raise_for_status()
+        data = response.json()
+        if not data:
+            break
+        frames.append(pd.DataFrame(data))
+    if frames:
+        df = pd.concat(frames, ignore_index=True)
+        # Convert transactionDate to datetime if present
+        if "transactionDate" in df.columns:
+            df["transactionDate"] = pd.to_datetime(df["transactionDate"], errors="coerce")
+        return df
+    return pd.DataFrame()
+
+@st.cache_data(show_spinner=False)
+def fetch_mna_search(company_name: str) -> pd.DataFrame:
+    """
+    Fetch M&A news filtered by company name using the search endpoint.
+    """
+    url = f"https://financialmodelingprep.com/api/v4/mergers-acquisitions/search?name={company_name}&apikey={API_KEY}"
+    response = requests.get(url)
+    response.raise_for_status()
+    data = response.json()
+    if not data:
+        return pd.DataFrame()
+    return pd.DataFrame(data)
+
+##############################################
+# MAIN APP
+##############################################
+def main():
+    st.set_page_config(page_title="M&A Feed Dashboard", layout="wide")
+    st.title("M&A Feed Dashboard")
+    st.write(
+        "This dashboard provides a real-time stream of Mergers & Acquisitions news and announcements. "
+        "Use the side menu below to choose between viewing the full M&A RSS feed or searching for M&A news by company name."
+    )
+    
+    # Sidebar (inside an expander) for navigation and options
+    with st.sidebar.expander("Navigation and Options", expanded=True):
+        page = st.radio(
+            "Select Page",
+            ("M&A Feed", "M&A Search"),
+            help="Choose 'M&A Feed' to view the complete RSS feed or 'M&A Search' to search for news by company name."
+        )
+        if page == "M&A Feed":
+            feed_date = st.date_input(
+                "From Date",
+                value=pd.to_datetime("2025-01-01").date(),
+                help="Select a starting date. Only M&A transactions on or after this date will be shown."
+            )
+            st.session_state.feed_date = feed_date
+            if st.button("Run M&A Feed"):
+                st.session_state.run_mna_feed = True
+        else:
+            company = st.text_input(
+                "Company Name",
+                value=DEFAULT_COMPANY,
+                help="Enter the company name to search for M&A news (default is 'syros')."
+            )
+            st.session_state.search_company = company
+            if st.button("Run M&A Search"):
+                st.session_state.run_mna_search = True
+
+    # Display page content based on the selected page
+    if page == "M&A Feed":
+        st.header("M&A RSS Feed")
+        st.write(
+            "Below is the latest M&A RSS feed data filtered by transaction date. "
+            "Only M&A transactions on or after the selected date are shown. "
+            "The table displays details such as the acquiring company, target company, transaction date, and URL."
+        )
+        if st.session_state.run_mna_feed:
+            df_feed = fetch_mna_feed()
+            if df_feed.empty:
+                st.error("No data returned from the M&A feed.")
+            else:
+                # Filter the data based on the selected date if transactionDate exists
+                if "transactionDate" in df_feed.columns:
+                    filtered_df = df_feed[df_feed["transactionDate"].dt.date >= st.session_state.feed_date]
+                else:
+                    filtered_df = df_feed
+                if filtered_df.empty:
+                    st.error("No M&A transactions found on or after the selected date.")
+                else:
+                    st.dataframe(filtered_df, use_container_width=True)
+        else:
+            st.info("Click 'Run M&A Feed' in the sidebar to fetch data.")
+    else:
+        st.header("M&A Search")
+        st.write(
+            "Search for M&A news by company name. "
+            "The table below displays the matching announcements, including details such as the acquiring company, "
+            "target company, transaction date, and announcement URL."
+        )
+        if st.session_state.run_mna_search:
+            df_search = fetch_mna_search(st.session_state.search_company)
+            if df_search.empty:
+                st.error("No M&A news found for the specified company name.")
+            else:
+                st.dataframe(df_search, use_container_width=True)
+        else:
+            st.info("Enter a company name in the sidebar and click 'Run M&A Search' to fetch data.")
+
+if __name__ == "__main__":
+    main()
 
 
 hide_streamlit_style = """