Update app.py

app.py

@@ -1,146 +1,297 @@
 import streamlit as st
 import pandas as pd
+import numpy as np
 from GoogleNews import GoogleNews
 from transformers import pipeline
 import plotly.express as px
+import plotly.graph_objects as go
 import dateparser
-from datetime import datetime
+from datetime import datetime, timedelta
 
-# --- CONFIG ---
-st.set_page_config(page_title="VBI Terminal", page_icon="🛡️", layout="wide")
+# --- SYSTEM CONFIG ---
+st.set_page_config(
+    page_title="VBI Terminal: Strategic AI", 
+    page_icon="🛡️", 
+    layout="wide",
+    initial_sidebar_state="collapsed"
+)
 
-# Упрощенные стили, чтобы ничего не пропадало
+# --- PROFESSIONAL UI (Cyberpunk/Terminal Style) ---
 st.markdown("""
     <style>
-    .report-box {
-        background-color: #1e293b;
-        border: 2px solid #38bdf8;
-        padding: 20px;
-        border-radius: 10px;
-        color: white;
-        margin: 10px 0;
+    /* Dark Deep Background */
+    .stApp { background-color: #0f172a; color: #e2e8f0; }
+    
+    /* Metrics Cards */
+    [data-testid="stMetric"] {
+        background-color: #1e293b !important;
+        border: 1px solid #334155 !important;
+        border-left: 5px solid #0ea5e9 !important;
+        border-radius: 8px;
+        padding: 15px !important;
+        box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.5);
+    }
+    [data-testid="stMetricLabel"] p { color: #94a3b8 !important; font-size: 0.9rem !important; }
+    [data-testid="stMetricValue"] div { color: #f8fafc !important; font-size: 2rem !important; }
+    
+    /* Advisor Box */
+    .advisor-box {
+        background-color: #334155; 
+        border: 1px solid #10b981; 
+        border-radius: 10px; 
+        padding: 20px; 
+        margin-top: 20px;
+        color: #f0fdf4;
     }
+    .warning-box {
+        background-color: #450a0a; 
+        border: 1px solid #ef4444; 
+        border-radius: 10px; 
+        padding: 20px; 
+        margin-top: 20px;
+        color: #fef2f2;
+    }
+    
+    /* Tables */
+    .stDataFrame { border: 1px solid #334155; border-radius: 5px; }
     </style>
     """, unsafe_allow_html=True)
 
+# --- AI ENGINE ---
 @st.cache_resource
-def load_ai():
-    # Загрузка модели
+def load_neural_engine():
+    """Load Zero-Shot Classification Model"""
     return pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
 
-analyzer = load_ai()
+analyzer = load_neural_engine()
+
+# --- INTELLIGENCE ENGINE ---
+def calculate_source_weight(source_name):
+    """Assigns weight based on source credibility (Heuristic)"""
+    source_name = str(source_name).lower()
+    if any(x in source_name for x in ['reuters', 'bloomberg', 'forbes', 'tengrinews', 'zakon', 'kapital']):
+        return 2.0  # High Impact Tier 1
+    elif any(x in source_name for x in ['blog', 'reddit', 'twitter', 'post']):
+        return 0.8  # Low Impact / High Noise
+    return 1.0  # Standard
 
-def fetch_data(query, region, depth, mode):
-    gn = GoogleNews(lang='en', region=region)
+def fetch_intelligence(query, region, depth, mode):
+    """Fetches real-time data using OSINT techniques."""
+    lang_set = 'en'
+    gn = GoogleNews(lang=lang_set, region=region)
     gn.clear()
     
-    search_query = query
+    # Advanced Query Logic
+    final_query = query
     if mode == "Social Buzz (Risk)":
-        search_query = f'{query} scandal OR complaint OR leak'
+        final_query = f'{query} AND ("scandal" OR "fail" OR "crash" OR "complaint" OR "leak" OR "reddit" OR "opinion")'
+    elif mode == "Financial Intel":
+        final_query = f'{query} AND ("stock" OR "profit" OR "loss" OR "quarter" OR "ipo" OR "revenue")'
     
-    gn.search(search_query)
+    gn.search(final_query)
     results = gn.result()
     
-    # Если нужно больше данных, берем вторую страницу
-    if len(results) < 5:
+    if len(results) < depth:
         gn.getpage(2)
-        results = gn.result()
+        results += gn.result()
     
-    return results[:depth]
-
-def get_advice(df, score):
-    # Логика советов
-    if score < 50:
-        return ["🚨 **CRITICAL:** High reputational risk. Immediate PR intervention required.", 
-                "- Release an official statement addressing top concerns.",
-                "- Audit internal processes for technical or legal gaps."]
-    elif score < 75:
-        return ["⚠️ **STABILIZATION:** Reputation is volatile.",
-                "- Increase positive social presence.",
-                "- Address minor complaints before they escalate."]
+    clean_data = []
+    seen_titles = set()
+    
+    for item in results[:depth]:
+        title = item.get('title', '')
+        if title in seen_titles: continue 
+        seen_titles.add(title)
+        
+        raw_date = item.get('date', '')
+        parsed_date = dateparser.parse(raw_date)
+        if not parsed_date:
+            parsed_date = datetime.now()
+            
+        source_label = item.get('media', 'Unknown Node')
+        weight = calculate_source_weight(source_label)
+        
+        clean_data.append({
+            "Timestamp": parsed_date,
+            "Source": source_label,
+            "Headline": title,
+            "Link": item.get('link', '#'),
+            "Weight": weight
+        })
+    return clean_data
+
+# --- STRATEGIC ADVISOR MODULE ---
+def generate_ai_advice(rep_index, top_risk, volatility, sentiment_ratio):
+    """Generates text-based strategy based on metrics."""
+    advice = []
+    status = "STABLE"
+    css_class = "advisor-box"
+    
+    # 1. Reputation Assessment
+    if rep_index < 40:
+        status = "CRITICAL"
+        css_class = "warning-box"
+        advice.append(f"🚨 **CRISIS MODE ACTIVATED:** Reputation Index ({round(rep_index)}%) is critically low. Immediate PR intervention required.")
+    elif rep_index < 60:
+        status = "VOLATILE"
+        advice.append(f"⚠️ **Caution:** Brand sentiment is mixed ({round(rep_index)}%). Monitor closely.")
     else:
-        return ["✅ **HEALTHY:** Brand reputation is strong.",
-                "- Maintain current transparency levels.",
-                "- Use this period for aggressive market expansion."]
+        advice.append(f"✅ **Healthy:** Strong market perception ({round(rep_index)}%). Focus on maintaining momentum.")
 
-# --- UI ---
-st.title("🛡️ VBI: Neural Strategy Terminal")
+    # 2. Risk Specific Strategy
+    if top_risk == "Legal/Compliance":
+        advice.append("⚖️ **Legal Vector:** High volume of legal discussions detected. Prepare official statements regarding compliance/lawsuits immediately.")
+    elif top_risk == "Technical Failure":
+        advice.append("🔧 **Ops Vector:** Technical complaints are trending. Issue a transparency report on uptime/fixes to reassure customers.")
+    elif top_risk == "Financial Risk":
+        advice.append("📉 **Market Vector:** Financial anxiety detected. IR (Investor Relations) should release clarifying data.")
+    elif top_risk == "PR Crisis":
+        advice.append("📣 **PR Vector:** Viral negativity detected. Do not ignore. Use 'Recall & Reframe' strategy.")
 
+    # 3. Volatility Check
+    if volatility > 0.3:
+        advice.append("🌊 **High Volatility:** Opinions are shifting rapidly. Avoid controversial statements for 48h.")
+
+    return status, "\n\n".join(advice), css_class
+
+# --- SIDEBAR ---
 with st.sidebar:
-    st.header("Settings")
-    region = st.selectbox("Region", ["KZ", "US", "GB"], index=0)
-    mode = st.radio("Mode", ["Corporate News", "Social Buzz (Risk)"])
-    depth = st.slider("Depth", 10, 40, 20)
+    st.header("🛰️ VBI: COMMAND")
+    target_region = st.selectbox("Geo-Node", ["KZ", "US", "GB", "RU"], index=0)
+    source_mode = st.radio("Signal Mode:", ["Corporate News", "Social Buzz (Risk)", "Financial Intel"])
+    scan_depth = st.slider("Depth", 10, 60, 30)
+    st.divider()
+    st.markdown("Created by **VBI Intelligence**")
 
-query = st.text_input("Enter Company/Target Name:")
+# --- MAIN LAYOUT ---
+st.title("🛡️ VBI: Strategic Brand Intelligence")
+st.markdown("`v2.0` | Neural OSINT & Automated Strategy Generation")
 
-if st.button("RUN ANALYSIS"):
-    if not query:
-        st.error("Please enter a name.")
-    else:
-        with st.spinner("Processing neural nodes..."):
-            try:
-                raw = fetch_data(query, region, depth, mode)
+col_search, col_btn = st.columns([4, 1])
+with col_search:
+    target_query = st.text_input("TARGET ENTITY:", placeholder="e.g. FlyArystan, Kaspi, KazMunayGas")
+with col_btn:
+    st.write("")
+    st.write("")
+    start_btn = st.button("🚀 INITIATE SCAN", use_container_width=True)
+
+if start_btn and target_query:
+    with st.spinner(f"🛰️ Intercepting signals for '{target_query}'..."):
+        # 1. Fetch
+        raw_data = fetch_intelligence(target_query, target_region, scan_depth, source_mode)
+        
+        if not raw_data:
+            st.error("No signals detected. System Standby.")
+        else:
+            # 2. Process
+            processed_data = []
+            risk_vectors = ["Legal/Compliance", "Financial Risk", "Technical Failure", "Market Expansion", "PR Crisis", "Customer Service"]
+            sentiment_cats = ["Positive", "Negative", "Neutral"]
+            
+            prog = st.progress(0)
+            for i, item in enumerate(raw_data):
+                # Neural Analysis
+                risk_out = analyzer(item['Headline'], candidate_labels=risk_vectors)
+                sent_out = analyzer(item['Headline'], candidate_labels=sentiment_cats)
                 
-                if not raw:
-                    st.warning("No data found for this target. Try a different name.")
-                else:
-                    data = []
-                    risk_labels = ["Legal", "Financial", "Technical", "PR/Social"]
-                    sent_labels = ["Positive", "Negative", "Neutral"]
-
-                    for item in raw:
-                        text = item.get('title', '')
-                        if not text: continue
-                        
-                        # AI Inference
-                        r_res = analyzer(text, candidate_labels=risk_labels)
-                        s_res = analyzer(text, candidate_labels=sent_labels)
-                        
-                        # Time parsing
-                        dt = dateparser.parse(item.get('date', 'now')) or datetime.now()
-                        
-                        data.append({
-                            "Time": dt,
-                            "Source": item.get('media', 'Web'),
-                            "Headline": text,
-                            "Risk": r_res['labels'][0],
-                            "Sentiment": s_res['labels'][0]
-                        })
-                    
-                    df = pd.DataFrame(data)
-                    
-                    # Metrics
-                    pos = len(df[df['Sentiment'] == "Positive"])
-                    neg = len(df[df['Sentiment'] == "Negative"])
-                    idx = 50 + (((pos - neg) / len(df)) * 50)
-
-                    # --- DISPLAY ---
-                    c1, c2, c3 = st.columns(3)
-                    c1.metric("Reputation Index", f"{round(idx, 1)}%")
-                    c2.metric("Signals", len(df))
-                    c3.metric("Threats", neg)
-
-                    # AI Advice Box
-                    st.markdown("<div class='report-box'>", unsafe_allow_html=True)
-                    st.subheader("🤖 AI Strategic Advice")
-                    for line in get_advice(df, idx):
-                        st.write(line)
-                    st.markdown("</div>", unsafe_allow_html=True)
-
-                    # Charts
-                    col_a, col_b = st.columns(2)
-                    with col_a:
-                        fig1 = px.pie(df, names='Sentiment', title="Sentiment", color='Sentiment',
-                                     color_discrete_map={"Positive":"#10b981", "Negative":"#ef4444", "Neutral":"#64748b"})
-                        st.plotly_chart(fig1, use_container_width=True)
-                    with col_b:
-                        fig2 = px.bar(df['Risk'].value_counts().reset_index(), x='count', y='Risk', title="Risk Sectors")
-                        st.plotly_chart(fig2, use_container_width=True)
-
-                    # Table
-                    st.subheader("Intelligence Log")
-                    st.dataframe(df, use_container_width=True)
-
-            except Exception as e:
-                st.error(f"An error occurred: {e}")
+                # Logic: If Risk Score is low, it might be just General News
+                top_risk = risk_out['labels'][0]
+                if risk_out['scores'][0] < 0.4:
+                    top_risk = "General Noise"
+
+                # Weighted Score Calculation
+                sent_score = 1 if sent_out['labels'][0] == 'Positive' else -1 if sent_out['labels'][0] == 'Negative' else 0
+                weighted_impact = sent_score * item['Weight']
+
+                processed_data.append({
+                    "Time": item['Timestamp'],
+                    "Source": item['Source'],
+                    "Headline": item['Headline'],
+                    "Risk Category": top_risk,
+                    "Risk Conf": risk_out['scores'][0],
+                    "Sentiment": sent_out['labels'][0],
+                    "Impact Score": weighted_impact
+                })
+                prog.progress((i + 1) / len(raw_data))
+            prog.empty()
+
+            df = pd.DataFrame(processed_data).sort_values(by='Time')
+
+            # --- ANALYTICS ENGINE ---
+            total_impact = df['Impact Score'].sum()
+            total_weight = df['Impact Score'].abs().sum() if df['Impact Score'].abs().sum() != 0 else 1
+            
+            # Reputation Index (0-100)
+            rep_index = 50 + ((total_impact / len(df)) * 50)
+            rep_index = max(0, min(100, rep_index)) # Clamp 0-100
+
+            # Volatility (Std Dev of Impact)
+            volatility = df['Impact Score'].std()
+            
+            # Top Risk
+            top_risk_cat = df['Risk Category'].value_counts().idxmax()
+            
+            # Advice Generation
+            status, strategy_text, css_style = generate_ai_advice(rep_index, top_risk_cat, volatility, 0)
+
+            # --- DASHBOARD ---
+            
+            # 1. STRATEGIC ADVISOR BLOCK
+            st.markdown(f"""
+            <div class="{css_style}">
+                <h3>🧠 AI Strategic Advisor: {status}</h3>
+                <div style='white-space: pre-wrap; font-size: 1.1rem;'>{strategy_text}</div>
+            </div>
+            """, unsafe_allow_html=True)
+            
+            st.divider()
+
+            # 2. KEY METRICS
+            m1, m2, m3, m4 = st.columns(4)
+            m1.metric("Reputation Index", f"{round(rep_index, 1)}%", delta="Weighted Impact")
+            m2.metric("Signal Volume", len(df))
+            m3.metric("Primary Threat", top_risk_cat, delta_color="off")
+            m4.metric("Volatility", f"{round(volatility, 2)}", help="Higher means less stable opinions")
+
+            # 3. VISUALIZATIONS
+            st.subheader("📊 Visual Intelligence")
+            
+            tab1, tab2 = st.tabs(["📈 Trend Dynamics", "🛡️ Risk Matrix"])
+            
+            with tab1:
+                # Rolling Average Trend
+                df['MA'] = df['Impact Score'].rolling(window=3).mean()
+                fig_trend = go.Figure()
+                fig_trend.add_trace(go.Scatter(x=df['Time'], y=df['Impact Score'], mode='markers', name='Raw Signal', marker=dict(color='#94a3b8')))
+                fig_trend.add_trace(go.Scatter(x=df['Time'], y=df['MA'], mode='lines', name='Reputation Trend', line=dict(color='#38bdf8', width=3)))
+                fig_trend.update_layout(title="Reputation Stability Over Time", template="plotly_dark", paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)')
+                st.plotly_chart(fig_trend, use_container_width=True)
+
+            with tab2:
+                c1, c2 = st.columns(2)
+                with c1:
+                    fig_pie = px.pie(df, names='Sentiment', title="Sentiment Distribution", color='Sentiment',
+                                     color_discrete_map={"Positive": "#10b981", "Negative": "#ef4444", "Neutral": "#64748b"})
+                    fig_pie.update_layout(template="plotly_dark", paper_bgcolor='rgba(0,0,0,0)')
+                    st.plotly_chart(fig_pie, use_container_width=True)
+                with c2:
+                    risk_counts = df['Risk Category'].value_counts().reset_index()
+                    risk_counts.columns = ['Risk', 'Count']
+                    fig_bar = px.bar(risk_counts, x='Count', y='Risk', orientation='h', title="Risk Vectors",
+                                     color='Count', color_continuous_scale='Redor')
+                    fig_bar.update_layout(template="plotly_dark", paper_bgcolor='rgba(0,0,0,0)')
+                    st.plotly_chart(fig_bar, use_container_width=True)
+
+            # 4. INTELLIGENCE LOG
+            st.subheader("📡 Decrypted Signal Log")
+            
+            def highlight_risk(val):
+                color = '#ef4444' if val in ['Legal/Compliance', 'Technical Failure', 'PR Crisis'] else '#cbd5e1'
+                return f'color: {color}'
+
+            display_df = df[['Time', 'Source', 'Headline', 'Risk Category', 'Sentiment', 'Impact Score']].copy()
+            st.dataframe(
+                display_df.style.map(highlight_risk, subset=['Risk Category']),
+                use_container_width=True,
+                height=400
+            )