Feat: Add Brave, Charts and Email Notifications

app.py

@@ -1,57 +1,62 @@
 import chainlit as cl
-from src.agent import app  # Import the robot you built
+from src.agent import app
 
 @cl.on_chat_start
 async def start():
-    """
-    Sends a welcome message when the user opens the website.
-    """
-    await cl.Message(content="👋 **Hello! I am PrimoGreedy.**\n\nI am your skeptical financial analyst.\nGive me a ticker (e.g., AAPL, AMC, PLTR) and I will run it through the Firewall.").send()
+    # Send a Welcome Message that lists your new powers
+    await cl.Message(content="👋 **PrimoGreedy v3.0**\n\n- **Brave Search** Active 🦁\n- **Resend Email** Active 📧\n- **Charts** Active 📈\n\n*Type a ticker (e.g., NVDA) to scout, or just say Hello.*").send()
 
 @cl.on_message
 async def main(message: cl.Message):
-    """
-    Runs every time the user types a message.
-    """
-    ticker = message.content.upper().strip()
+    user_input = message.content.strip()
     
-    # 1. Notify user we are starting
-    msg = cl.Message(content=f"🔍 Analyzing **{ticker}**...")
+    # Simple check: If it looks like a ticker, say "Scouting", otherwise "Thinking"
+    if len(user_input) <= 5 and " " not in user_input:
+        msg = cl.Message(content=f"🔍 Scouting **{user_input.upper()}**...")
+    else:
+        msg = cl.Message(content=f"🤔 Thinking...")
     await msg.send()
     
-    # 2. Run the Agent Logic
-    # We use 'invoke' to run the graph we built in agent.py
     try:
-        result = await app.ainvoke({"ticker": ticker})
+        # Run the full Agent (Brain + Eyes + Hands)
+        result = await app.ainvoke({"ticker": user_input})
         
-        # 3. Check the Result
-        final_report = result.get('final_report')
+        # Extract all the new data we added
         status = result.get('status')
-        financial_data = result.get('financial_data', {})
-        
-        # 4. Display the Output
+        report = result.get('final_report')
+        chart_bytes = result.get('chart_data')
+        email_msg = result.get('email_status')
+
+        # Prepare the Image (if we have one)
+        elements = []
+        if chart_bytes:
+            elements.append(cl.Image(content=chart_bytes, name="chart", display="inline"))
+
+        # Format the Text Response
         if status == "FAIL":
-            # If the Firewall rejected it
-            reason = financial_data.get('reason', 'Unknown Reason')
-            
+            # Rejection (Firewall)
             response = f"""
-            ❌ **REJECTED BY FIREWALL**
-            
-            **Ticker:** {ticker}
-            **Reason:** {reason}
+            ❌ **REJECTED**: {result.get('financial_data', {}).get('reason')}
             
-            *I did not waste time searching for news because this stock is too risky.*
+            *No email sent. No chart drawn.*
             """
-        else:
-            # If it passed and the LLM wrote a report
+        elif status == "PASS":
+            # Success (Analysis + Chart + Email)
             response = f"""
             ✅ **PASSED FIREWALL**
             
-            {final_report}
-            """
+            {report}
             
-        # Send the final answer to the chat UI
-        await cl.Message(content=response).send()
+            ---
+            **System Status:**
+            {email_msg if email_msg else "📧 Email not sent (Check keys)"}
+            """
+        else:
+            # Just Chatting
+            response = report
+
+        # Send everything to the UI
+        await cl.Message(content=response, elements=elements).send()
         
     except Exception as e:
         await cl.Message(content=f"⚠️ Error: {str(e)}").send()

requirements.txt

@@ -7,4 +7,6 @@ langgraph
 yfinance
 duckduckgo-search
 openai
-requests
+requests
+matplotlib
+resend

src/agent.py

@@ -1,105 +1,96 @@
 import os
-from typing import TypedDict, Optional
+from typing import TypedDict, Optional, Any
 from langgraph.graph import StateGraph, END
 from langchain_core.messages import HumanMessage, SystemMessage
 import requests
 from src.llm import get_llm
 from src.finance_tools import check_financial_health
+from src.viz import get_stock_chart
+from src.email_utils import send_email_report
 
-# --- 1. STATE DEFINITION ---
+# --- 1. STATE ---
 class AgentState(TypedDict):
     ticker: str
     status: str
     financial_data: Optional[dict]
     final_report: Optional[str]
+    chart_data: Optional[Any]
+    email_status: Optional[str]
 
 llm = get_llm()
 
-# --- 2. BRAVE SEARCH TOOL ---
+# --- 2. TOOLS ---
 def brave_market_search(query: str):
-    """Fetches real-time market sentiment using Brave Search API."""
+    """Uses Brave API to find real-time news."""
     api_key = os.getenv("BRAVE_API_KEY")
-    if not api_key:
-        return "Brave API Key missing. Skipping web search."
+    if not api_key: return "⚠️ Brave Key Missing."
     
     url = "https://api.search.brave.com/res/v1/web/search"
-    headers = {
-        "Accept": "application/json",
-        "X-Subscription-Token": api_key
-    }
-    params = {"q": f"{query} stock market news sentiment", "count": 5}
+    headers = {"X-Subscription-Token": api_key}
+    params = {"q": f"{query} stock news sentiment", "count": 3}
     
     try:
-        response = requests.get(url, headers=headers, params=params)
-        results = response.json().get("web", {}).get("results", [])
-        snippets = [f"- {r['title']}: {r['description']}" for r in results]
-        return "\n".join(snippets) if snippets else "No recent news found."
+        data = requests.get(url, headers=headers, params=params).json()
+        results = data.get("web", {}).get("results", [])
+        return "\n".join([f"- {r['title']}" for r in results])
     except Exception as e:
-        return f"Search error: {str(e)}"
+        return f"Search Error: {str(e)}"
 
 # --- 3. NODES ---
-
 def check_health(state: AgentState):
-    """The Logic Firewall Node."""
     ticker = state['ticker'].upper()
     result = check_financial_health(ticker)
     return {"financial_data": result, "status": result['status']}
 
 async def analyze_stock(state: AgentState):
-    """The AI Research Node (Now with Brave Search)."""
-    if state['status'] == "FAIL":
-        return state
+    if state['status'] == "FAIL": return state
 
     ticker = state['ticker'].upper()
-    # Use Brave for real-time data
-    market_news = brave_market_search(ticker)
     
-    prompt = f"""
-    You are a professional stock analyst.
-    Analyze {ticker} based on these financials and real-time news.
+    # 1. Run Tools
+    news = brave_market_search(ticker)
+    chart = get_stock_chart(ticker)
     
-    Financial Health: {state['financial_data']['reason']}
-    Recent Market Sentiment (via Brave): 
-    {market_news}
+    # 2. AI Analysis
+    prompt = f"""
+    Analyze {ticker}.
+    Financials: {state['financial_data']['reason']}
+    Real-Time News (Brave): {news}
     
-    Provide a concise 'Recommendation' (BUY/HOLD/SELL) and a brief justification.
+    Give a recommendation (BUY/HOLD/SELL) and detailed reasoning.
     """
+    response = await llm.ainvoke([
+        SystemMessage(content="You are PrimoGreedy. Sarcastic, skeptical, data-driven."), 
+        HumanMessage(content=prompt)
+    ])
+    
+    # 3. Send Email (Only if it passed the firewall)
+    email_result = send_email_report(ticker, response.content)
     
-    response = await llm.ainvoke([SystemMessage(content="You are a skeptical analyst."), HumanMessage(content=prompt)])
-    return {"final_report": response.content}
+    return {
+        "final_report": response.content, 
+        "chart_data": chart,
+        "email_status": email_result
+    }
 
 async def chat_mode(state: AgentState):
-    """The Conversational Node."""
-    prompt = [
-        SystemMessage(content="You are PrimoGreedy, a witty and skeptical financial assistant. Keep it brief."),
-        HumanMessage(content=state['ticker'])
-    ]
-    response = await llm.ainvoke(prompt)
+    response = await llm.ainvoke([HumanMessage(content=state['ticker'])])
     return {"final_report": response.content, "status": "CHAT"}
 
-# --- 4. THE ROUTER ---
+# --- 4. ROUTER ---
 def route_query(state: AgentState):
     query = state['ticker'].strip().upper()
-    # Simple logic: If 1-5 chars and no spaces, it's likely a ticker
     if 1 <= len(query) <= 5 and " " not in query:
         return "financial_health_check"
     return "chat_mode"
 
-# --- 5. BUILD THE GRAPH ---
+# --- 5. GRAPH ---
 workflow = StateGraph(AgentState)
-
 workflow.add_node("financial_health_check", check_health)
 workflow.add_node("analyst_research", analyze_stock)
 workflow.add_node("chat_mode", chat_mode)
 
-workflow.set_conditional_entry_point(
-    route_query,
-    {
-        "financial_health_check": "financial_health_check",
-        "chat_mode": "chat_mode"
-    }
-)
-
+workflow.set_conditional_entry_point(route_query, {"financial_health_check": "financial_health_check", "chat_mode": "chat_mode"})
 workflow.add_edge("financial_health_check", "analyst_research")
 workflow.add_edge("analyst_research", END)
 workflow.add_edge("chat_mode", END)

src/email_utils.py

@@ -0,0 +1,39 @@
+import os
+import resend
+
+def send_email_report(ticker: str, report: str):
+    """
+    Sends an email alert using Resend.
+    """
+    api_key = os.getenv("RESEND_API_KEY")
+    target_email = os.getenv("TARGET_EMAIL") # We will set this in Hugging Face Secrets
+
+    if not api_key or not target_email:
+        return "⚠️ Email skipped: Missing API Key or Target Email."
+
+    resend.api_key = api_key
+
+    # Format the email body (Convert newlines to HTML breaks)
+    html_body = f"""
+    <h1>PrimoGreedy Report: {ticker}</h1>
+    <p><strong>Status:</strong> PASSED FIREWALL</p>
+    <hr>
+    <div style="font-family: monospace; white-space: pre-wrap;">
+    {report}
+    </div>
+    <hr>
+    <p><em>Generated by your AI Agent.</em></p>
+    """
+
+    params = {
+        "from": "PrimoGreedy <onboarding@resend.dev>", # Default testing domain
+        "to": [target_email],
+        "subject": f"💰 Trade Alert: {ticker} Analysis",
+        "html": html_body
+    }
+
+    try:
+        email = resend.Emails.send(params)
+        return f"📧 Email sent! (ID: {email.get('id')})"
+    except Exception as e:
+        return f"⚠️ Email failed: {str(e)}"

src/viz.py

@@ -0,0 +1,34 @@
+import matplotlib.pyplot as plt
+import yfinance as yf
+import io
+
+def get_stock_chart(ticker: str):
+    """
+    Generates a 6-month price chart and returns the image as bytes.
+    """
+    try:
+        stock = yf.Ticker(ticker)
+        hist = stock.history(period="6mo")
+        
+        if hist.empty: return None
+
+        plt.figure(figsize=(10, 5))
+        plt.plot(hist.index, hist['Close'], label='Price', color='#00ff00', linewidth=2)
+        plt.title(f"{ticker} - 6 Month Trend", color='white')
+        plt.grid(True, alpha=0.3)
+        
+        # Dark Mode Style
+        ax = plt.gca()
+        ax.set_facecolor('#0e1117')
+        plt.gcf().set_facecolor('#0e1117')
+        ax.tick_params(colors='white')
+        
+        # Save to RAM
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png', bbox_inches='tight')
+        plt.close()
+        buf.seek(0)
+        return buf.getvalue()
+        
+    except:
+        return None

test_llm.py

@@ -4,7 +4,7 @@ print("\n--- 🧠 PRIMOGREEDY BRAIN TEST ---\n")
 
 try:
     llm = get_llm()
-    print("🤖 Asking Llama 3: 'What is the most dangerous risk for a bank?'...")
+    print("🤖 Asking Agent: 'What is the most dangerous risk for a bank?'...")
     
     response = llm.invoke("What is the single most dangerous risk for a bank? Answer in 1 sentence.")