Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,132 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import warnings
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# --- 1. SILENCE ALL WARNINGS & ERRORS ---
+warnings.filterwarnings("ignore", category=RuntimeWarning)
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+import os
+import sys
+import logging
+import contextlib
+from dotenv import load_dotenv
+import yfinance as yf
+from duckduckgo_search import DDGS
+from langchain_core.language_models.llms import LLM
+from huggingface_hub import InferenceClient
+from typing import Any, List, Optional
+from pydantic import Field
+
+# Silence yfinance logger
+logging.getLogger('yfinance').setLevel(logging.CRITICAL)
+
+@contextlib.contextmanager
+def suppress_output():
+    """Redirects stdout/stderr to devnull to hide yfinance errors."""
+    with open(os.devnull, "w") as devnull:
+        old_stdout, old_stderr = sys.stdout, sys.stderr
+        try:
+            sys.stdout, sys.stderr = devnull, devnull
+            yield
+        finally:
+            sys.stdout, sys.stderr = old_stdout, old_stderr
+
+# --- CONFIG ---
+st.set_page_config(page_title="Stock Scout", page_icon="📈", layout="centered")
+load_dotenv()
+hf_token = os.getenv("HF_TOKEN")
+
+if not hf_token:
+    st.error("Missing HF_TOKEN in .env file.")
+    st.stop()
+
+# --- MODEL (Unbreakable Cluster) ---
+class HFChatModel(LLM):
+    token: str = Field(...)
+    models: List[str] = [
+        "microsoft/Phi-3.5-mini-instruct", 
+        "Qwen/Qwen2.5-7B-Instruct",         
+        "HuggingFaceH4/zephyr-7b-beta"
+    ]
+    
+    def _call(self, prompt: str, stop: Optional[List[str]] = None, **kwargs: Any) -> str:
+        client = InferenceClient(token=self.token)
+        messages = [{"role": "user", "content": prompt}]
+        for model_id in self.models:
+            try:
+                response = client.chat_completion(
+                    model=model_id, messages=messages, max_tokens=600, temperature=0.5
+                )
+                return response.choices[0].message.content
+            except:
+                continue
+        return "System Busy."
+
+    @property
+    def _llm_type(self) -> str:
+        return "custom_hf_chat_cluster"
+
+llm = HFChatModel(token=hf_token)
+
+# --- FUNCTIONS ---
+def get_stock_data(ticker):
+    try:
+        with suppress_output():
+            stock = yf.Ticker(ticker)
+            hist = stock.history(period="5d")
+        if len(hist) < 2: return None, None
+        curr = hist['Close'].iloc[-1]
+        prev = hist['Close'].iloc[-2]
+        change = ((curr - prev) / prev) * 100
+        return curr, change
+    except:
+        return None, None
+
+def analyze_news(ticker):
+    try:
+        with suppress_output():
+            results = DDGS().news(f"{ticker} stock news", max_results=5)
+        if not results: return "No recent news found."
+        
+        news_context = []
+        sources_txt = "\n\n**Sources:**\n"
+        for i, res in enumerate(results, 1):
+            title = res.get('title', '?')
+            link = res.get('url', '#')
+            news_context.append(f"[Source {i}]: {title}")
+            sources_txt += f"{i}. [{title}]({link})\n"
+            
+        full_text = "\n".join(news_context)
+        prompt = f"""
+        Analyze news for {ticker}:
+        {full_text}
+        1. Why is it moving? (Cite [Source X])
+        2. Sentiment: BULLISH/BEARISH/NEUTRAL
+        """
+        return llm.invoke(prompt) + sources_txt
+    except:
+        return "Analysis failed."
+
+# --- UI (SIMPLE DROPDOWN ONLY) ---
+st.title("📈 Stock Scout")
+
+# 1. The Safe List
+POPULAR_TICKERS = [
+    "AAPL", "NVDA", "TSLA", "AMD", "AMZN", "MSFT", "GOOGL", "META",
+    "JPM", "BAC", "WMT", "DIS", "NFLX", "KO", "PEP", "BA", "INTC", "PYPL"
+]
+
+# 2. The Selector
+selected_ticker = st.selectbox("Select Stock:", options=POPULAR_TICKERS)
+
+# 3. Execution (Instant)
+if selected_ticker:
+    price, change = get_stock_data(selected_ticker)
+    
+    if price:
+        st.metric(selected_ticker, f"${price:.2f}", f"{change:.2f}%")
+        st.divider()
+        with st.spinner(f"Analyzing {selected_ticker}..."):
+            st.info(analyze_news(selected_ticker))
+    else:
+        st.error("Data unavailable.")