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| import os | |
| import sys | |
| import tempfile | |
| import time | |
| import itertools | |
| import streamlit as st | |
| import pandas as pd | |
| from io import StringIO | |
| from threading import Thread | |
| # Add 'src' to Python path so we can import main.py | |
| sys.path.append(os.path.join(os.path.dirname(__file__), 'src')) | |
| from main import run_pipeline | |
| st.set_page_config(page_title="📰 AI News Analyzer", layout="wide") | |
| st.title("🧠 AI-Powered Investing News Analyzer") | |
| # === API Key Input === | |
| st.subheader("🔐 API Keys") | |
| openai_api_key = st.text_input("OpenAI API Key", type="password").strip() | |
| tavily_api_key = st.text_input("Tavily API Key", type="password").strip() | |
| # === Topic Input === | |
| st.subheader("📈 Topics of Interest") | |
| topics_data = [] | |
| with st.form("topics_form"): | |
| topic_count = st.number_input("How many topics?", min_value=1, max_value=10, value=1, step=1) | |
| for i in range(topic_count): | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| topic = st.text_input(f"Topic {i+1}", key=f"topic_{i}") | |
| with col2: | |
| days = st.number_input(f"Timespan (days)", min_value=1, max_value=30, value=7, key=f"days_{i}") | |
| topics_data.append({"topic": topic, "timespan_days": days}) | |
| submitted = st.form_submit_button("Run Analysis") | |
| # === Submission logic === | |
| if submitted: | |
| if not openai_api_key or not tavily_api_key or not all([td['topic'] for td in topics_data]): | |
| st.warning("Please fill in all fields.") | |
| else: | |
| os.environ["OPENAI_API_KEY"] = openai_api_key | |
| os.environ["TAVILY_API_KEY"] = tavily_api_key | |
| df = pd.DataFrame(topics_data) | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as tmp_csv: | |
| df.to_csv(tmp_csv.name, index=False) | |
| csv_path = tmp_csv.name | |
| progress_box = st.empty() | |
| rotating = True | |
| def rotating_messages(): | |
| messages = itertools.cycle([ | |
| "🔍 Searching financial news sources...", | |
| "🧠 Running language model analysis...", | |
| "📊 Generating investment reports..." | |
| ]) | |
| while rotating: | |
| progress_box.markdown(f"⏳ {next(messages)}") | |
| time.sleep(1.5) | |
| rotator_thread = Thread(target=rotating_messages) | |
| rotator_thread.start() | |
| try: | |
| output_path = run_pipeline(csv_path, tavily_api_key) | |
| rotating = False | |
| rotator_thread.join() | |
| progress_box.success("✅ Analysis complete!") | |
| if output_path and isinstance(output_path, list): | |
| for path in output_path: | |
| if os.path.exists(path): | |
| with open(path, 'r', encoding='utf-8') as file: | |
| html_content = file.read() | |
| filename = os.path.basename(path) | |
| st.download_button( | |
| label=f"📥 Download {filename}", | |
| data=html_content, | |
| file_name=filename, | |
| mime="text/html" | |
| ) | |
| st.components.v1.html(html_content, height=600, scrolling=True) | |
| else: | |
| st.error("❌ No reports were generated.") | |
| except Exception as e: | |
| rotating = False | |
| rotator_thread.join() | |
| progress_box.error(f"❌ Error: {e}") | |
| ################################################################################################## | |
| ################################################################################################## | |
| # import os | |
| # import sys | |
| # import tempfile | |
| # import streamlit as st | |
| # import pandas as pd | |
| # from io import StringIO | |
| # # Add 'src' to Python path so we can import main.py | |
| # sys.path.append(os.path.join(os.path.dirname(__file__), 'src')) | |
| # from main import run_pipeline | |
| # st.set_page_config(page_title="📰 AI News Analyzer", layout="wide") | |
| # st.title("🧠 AI-Powered Investing News Analyzer") | |
| # # === API Key Input === | |
| # st.subheader("🔐 API Keys") | |
| # openai_api_key = st.text_input("OpenAI API Key", type="password").strip() | |
| # tavily_api_key = st.text_input("Tavily API Key", type="password").strip() | |
| # # === Topic Input === | |
| # st.subheader("📈 Topics of Interest") | |
| # topics_data = [] | |
| # with st.form("topics_form"): | |
| # topic_count = st.number_input("How many topics?", min_value=1, max_value=10, value=1, step=1) | |
| # for i in range(topic_count): | |
| # col1, col2 = st.columns(2) | |
| # with col1: | |
| # topic = st.text_input(f"Topic {i+1}", key=f"topic_{i}") | |
| # with col2: | |
| # days = st.number_input(f"Timespan (days)", min_value=1, max_value=30, value=7, key=f"days_{i}") | |
| # topics_data.append({"topic": topic, "timespan_days": days}) | |
| # submitted = st.form_submit_button("Run Analysis") | |
| # # === Submission logic === | |
| # if submitted: | |
| # if not openai_api_key or not tavily_api_key or not all([td['topic'] for td in topics_data]): | |
| # st.warning("Please fill in all fields.") | |
| # else: | |
| # os.environ["OPENAI_API_KEY"] = openai_api_key | |
| # os.environ["TAVILY_API_KEY"] = tavily_api_key | |
| # df = pd.DataFrame(topics_data) | |
| # with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as tmp_csv: | |
| # df.to_csv(tmp_csv.name, index=False) | |
| # csv_path = tmp_csv.name | |
| # progress_box = st.empty() | |
| # def show_progress(msg): | |
| # progress_box.markdown(f"⏳ {msg}") | |
| # try: | |
| # output_path = run_pipeline(csv_path, tavily_api_key, progress_callback=show_progress) | |
| # progress_box.success("✅ Analysis complete!") | |
| # if output_path and isinstance(output_path, list): | |
| # for path in output_path: | |
| # if os.path.exists(path): | |
| # with open(path, 'r', encoding='utf-8') as file: | |
| # html_content = file.read() | |
| # filename = os.path.basename(path) | |
| # st.download_button( | |
| # label=f"📥 Download {filename}", | |
| # data=html_content, | |
| # file_name=filename, | |
| # mime="text/html" | |
| # ) | |
| # st.components.v1.html(html_content, height=600, scrolling=True) | |
| # else: | |
| # st.error("❌ No reports were generated.") | |
| # except Exception as e: | |
| # progress_box.error(f"❌ Error: {e}") | |