Add matplotlib to requirements

app.py

@@ -1,139 +1,11 @@
-import os
-import sys
-import tempfile
-import time
-import itertools
-import streamlit as st
-import pandas as pd
-from threading import Thread
-from io import StringIO
-
-# Add 'src' to Python path
-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
-
-        # Placeholders
-        spinner_box = st.empty()
-        log_box = st.empty()
-
-        # Rotating spinner text
-        rotating = True
-        logs = []
-
-        def log(msg):
-            logs.append(msg)
-            log_box.code("\n".join(logs))
-
-        def rotating_messages():
-            messages = itertools.cycle([
-                "🔍 Searching financial news...",
-                "🧠 Running language models...",
-                "📊 Generating investment reports...",
-                "📝 Compiling markdown...",
-                "🔎 Looking for value signals..."
-            ])
-            while rotating:
-                spinner_box.markdown(f"⏳ {next(messages)}")
-                time.sleep(1.4)
-
-        rotator_thread = Thread(target=rotating_messages)
-        rotator_thread.start()
-
-        try:
-            # Log: API checks
-            log("🔐 Checking API keys...")
-            import openai
-            openai_client = openai.OpenAI(api_key=openai_api_key)
-            openai_client.models.list()  # triggers check
-            log("✅ OpenAI API key is valid.")
-
-            import requests
-            tavily_check = requests.post(
-                "https://api.tavily.com/search",
-                headers={"Authorization": f"Bearer {tavily_api_key}"},
-                json={"query": "test", "days": 1, "max_results": 1}
-            )
-            if tavily_check.status_code == 200:
-                log("✅ Tavily API key is valid.")
-            else:
-                raise ValueError(f"Tavily key failed: {tavily_check.status_code} - {tavily_check.text}")
-
-            # Run main pipeline
-            log("🚀 Running analysis pipeline...")
-            output_path = run_pipeline(csv_path, tavily_api_key, progress_callback=log)
-
-            rotating = False
-            rotator_thread.join()
-            spinner_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()
-            spinner_box.error("❌ Failed.")
-            log_box.error(f"❌ Error: {e}")
-
-##################################################################################################
-##################################################################################################
 # import os
 # import sys
 # import tempfile
+# import time
+# import itertools
 # import streamlit as st
 # import pandas as pd
+# from threading import Thread
 # from io import StringIO
 
 # # Add 'src' to Python path so we can import main.py
@@ -178,14 +50,56 @@ if submitted:
 #             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}")
+#         # === UI Elements ===
+#         spinner_box = st.empty()   # For rotating messages
+#         log_box = st.empty()       # For logs
+#         logs = []
+#         rotating = True
+
+#         def log(msg):
+#             logs.append(msg)
+#             log_box.code("\n".join(logs))
+
+#         # === Rotating UI Messages ===
+#         def rotating_messages():
+#             messages = itertools.cycle([
+#                 "🔍 Searching financial news...",
+#                 "🧠 Running language models...",
+#                 "📊 Analyzing investor sentiment...",
+#                 "📝 Summarizing key takeaways...",
+#                 "💹 Building markdown reports..."
+#             ])
+#             while rotating:
+#                 spinner_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, progress_callback=show_progress)
-#             progress_box.success("✅ Analysis complete!")
+#             # Check API Keys
+#             import openai
+#             openai.OpenAI(api_key=openai_api_key).models.list()
+#             log("✅ OpenAI API key is valid.")
+
+#             import requests
+#             tavily_test = requests.post(
+#                 "https://api.tavily.com/search",
+#                 headers={"Authorization": f"Bearer {tavily_api_key}"},
+#                 json={"query": "test", "days": 1, "max_results": 1}
+#             )
+#             if tavily_test.status_code == 200:
+#                 log("✅ Tavily API key is valid.")
+#             else:
+#                 raise ValueError(f"Tavily error: {tavily_test.status_code} - {tavily_test.text}")
+
+#             # Run the full pipeline
+#             log("🚀 Running analysis pipeline...")
+#             output_path = run_pipeline(csv_path, tavily_api_key, progress_callback=log)
+
+#             rotating = False
+#             rotator_thread.join()
+#             spinner_box.success("✅ Analysis complete!")
 
 #             if output_path and isinstance(output_path, list):
 #                 for path in output_path:
@@ -203,7 +117,165 @@ if submitted:
 #                             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}")
+#             rotating = False
+#             rotator_thread.join()
+#             spinner_box.error("❌ Failed.")
+#             log_box.error(f"❌ Error: {e}")
 
 
+##################################################################################################
+##################################################################################################
+import os
+import sys
+import tempfile
+import time
+import itertools
+import streamlit as st
+import pandas as pd
+from threading import Thread
+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")
+
+# === State control ===
+if "running" not in st.session_state:
+    st.session_state.running = False
+if "stop_requested" not in st.session_state:
+    st.session_state.stop_requested = False
+
+# === Tabs ===
+tabs = st.tabs(["📥 Input", "📤 Results", "🪵 Logs"])
+input_tab, result_tab, log_tab = tabs
+
+# === Input Tab ===
+with input_tab:
+    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()
+
+    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})
+
+        run_btn = st.form_submit_button(
+            "Run Analysis" if not st.session_state.running else "Running...", disabled=st.session_state.running
+        )
+
+# === Output placeholders ===
+with result_tab:
+    spinner_box = st.empty()
+    download_box = st.empty()
+
+with log_tab:
+    log_box = st.empty()
+    stop_btn = st.button("🛑 Stop Analysis", disabled=not st.session_state.running)
+
+# === Run if submitted ===
+if run_btn and not st.session_state.running:
+    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
+        st.session_state.running = True
+        st.session_state.stop_requested = False
+        logs = []
+
+        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
+
+        def log(msg, level="info"):
+            emoji = {"info": "ℹ️", "warning": "⚠️", "error": "❌"}.get(level, "")
+            logs.append(f"{emoji} {msg}")
+            colors = {"info": "blue", "warning": "orange", "error": "red"}
+            styled = [f'<span style="color:{colors.get(level, "black")};">{emoji} {msg}</span>' for msg in logs]
+            log_box.markdown("<br>".join(styled), unsafe_allow_html=True)
+
+        # Rotating status messages
+        def rotating_messages():
+            phrases = itertools.cycle([
+                "🔍 Searching financial news...",
+                "🧠 Running language models...",
+                "📊 Analyzing investor sentiment...",
+                "📝 Summarizing insights...",
+                "💡 Generating markdown reports..."
+            ])
+            while st.session_state.running and not st.session_state.stop_requested:
+                spinner_box.markdown(f"⏳ {next(phrases)}")
+                time.sleep(1.5)
+
+        rotator = Thread(target=rotating_messages)
+        rotator.start()
+
+        try:
+            import openai
+            openai.OpenAI(api_key=openai_api_key).models.list()
+            log("OpenAI API key validated.", "info")
+
+            import requests
+            test = requests.post(
+                "https://api.tavily.com/search",
+                headers={"Authorization": f"Bearer {tavily_api_key}"},
+                json={"query": "test", "days": 1, "max_results": 1}
+            )
+            if test.status_code == 200:
+                log("Tavily API key validated.", "info")
+            else:
+                raise ValueError(f"Tavily key rejected: {test.status_code} {test.text}")
+
+            # Run the pipeline
+            def progress_callback(msg):
+                if st.session_state.stop_requested:
+                    raise Exception("⛔ Analysis stopped by user.")
+                log(msg, "info")
+
+            log("Starting pipeline...", "info")
+            output_path = run_pipeline(csv_path, tavily_api_key, progress_callback=progress_callback)
+
+            st.session_state.running = False
+            rotator.join()
+            spinner_box.success("✅ Analysis complete!")
+
+            if output_path:
+                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)
+                            download_box.download_button(
+                                f"📥 Download {filename}", html_content, filename=filename, mime="text/html"
+                            )
+                            download_box.components.v1.html(html_content, height=600, scrolling=True)
+            else:
+                log("No reports were generated.", "warning")
+                spinner_box.error("❌ No reports were generated.")
+
+        except Exception as e:
+            st.session_state.running = False
+            rotator.join()
+            spinner_box.error(f"❌ Error: {e}")
+            log(str(e), "error")
+
+# === Handle manual stop ===
+if stop_btn and st.session_state.running:
+    st.session_state.stop_requested = True
+    log("🛑 Stop requested by user...", "warning")