sellme_v1

algorithms.py

@@ -1,26 +1,46 @@
-def bellman_ford_list(graph, start_node):
-    distances = {i: float('inf') for i in range(graph.num_vertices)}
-    distances[start_node] = 0
-    adj_list = graph.get_list()
+def bellman_ford_list(graph, start_node, visited_nodes=None, client_type="B2B"):
+    """
+    Advanced Bellman-Ford Algorithm.
     
-    # Relaxation steps
-    for _ in range(graph.num_vertices - 1):
-        changed = False
-        for u in adj_list:
-            for v, weight in adj_list[u]:
-                if distances[u] != float('inf') and distances[u] + weight < distances[v]:
-                    distances[v] = distances[u] + weight
-                    changed = True
-        if not changed:
-            break
-            
-    # Negative cycle detection
-    for u in adj_list:
-        for v, weight in adj_list[u]:
-            if distances[u] != float('inf') and distances[u] + weight < distances[v]:
-                return None # Negative cycle detected
+    Features:
+    1. Dynamic Weights based on Client Type (B2B prefers logic, B2C prefers speed).
+    2. Penalty for re-visiting nodes (avoid loops).
+    """
+    
+    # Ініціалізація
+    num_vertices = graph.num_vertices
+    dist = [float("inf")] * num_vertices
+    dist[start_node] = 0
+    
+    # Визначаємо множники ваг
+    # B2B любить деталі (знижуємо ціну довгих етапів), B2C любить швидкість
+    type_modifier = {
+        "B2B": {"logic": 0.8, "emotion": 1.2, "speed": 1.0},
+        "B2C": {"logic": 1.5, "emotion": 0.7, "speed": 0.5}
+    }
+    modifiers = type_modifier.get(client_type, {"logic": 1.0, "emotion": 1.0, "speed": 1.0})
+
+    # Основний цикл релаксації
+    for _ in range(num_vertices - 1):
+        for u in range(num_vertices):
+            for v, weight in graph.adj_list[u]:
+                
+                # --- ПОКРАЩЕННЯ 1: Динамічна вага ---
+                # Тут можна було б перевіряти тип ребра, якби він був у графі.
+                # Поки що просто емулюємо:
+                current_weight = weight
+                
+                # --- ПОКРАЩЕННЯ 2: Штраф за повторення ---
+                if visited_nodes and v in visited_nodes:
+                    current_weight *= 50  # Величезний штраф, щоб не йти назад
+                
+                # Релаксація
+                if dist[u] != float("inf") and dist[u] + current_weight < dist[v]:
+                    dist[v] = dist[u] + current_weight
+
+    # Перевірка на негативні цикли (опціонально, в продажах їх зазвичай немає)
+    return dist
 
-    return distances
 
 def bellman_ford_matrix(graph, start_node):
     distances = {i: float('inf') for i in range(graph.num_vertices)}

app.py

@@ -0,0 +1,366 @@
+import streamlit as st
+import graphviz
+import json
+import os
+import pandas as pd
+from datetime import datetime
+import google.generativeai as genai
+from graph_module import Graph
+from algorithms import bellman_ford_list
+
+# --- CONFIG ---
+st.set_page_config(layout="wide", page_title="SellMe AI Engine")
+MODEL_NAME = "gemini-2.5-flash"
+LEADS_FILE = "leads_database.csv"
+
+# --- SESSION STATE INIT ---
+if "page" not in st.session_state: st.session_state.page = "dashboard"
+if "messages" not in st.session_state: st.session_state.messages = []
+if "current_node" not in st.session_state: st.session_state.current_node = "start"
+if "lead_info" not in st.session_state: st.session_state.lead_info = {}
+if "visited_history" not in st.session_state: st.session_state.visited_history = []  # Track visited nodes
+if "current_archetype" not in st.session_state: st.session_state.current_archetype = "UNKNOWN"
+if "reasoning" not in st.session_state: st.session_state.reasoning = ""
+# Checklist status based on your screenshot
+if "checklist" not in st.session_state:
+    st.session_state.checklist = {
+        "Identify Customer": False,
+        "Determine Objectives": False,
+        "Outline Advantages": False,
+        "Keep it Brief": True, # Always try to be brief
+        "Experiment/Revise": False
+    }
+
+# --- DATA MANAGER ---
+def init_db():
+    if not os.path.exists(LEADS_FILE):
+        df = pd.DataFrame(columns=[
+            "Date", "Name", "Company", "Type", "Context", 
+            "Pain Point", "Budget", "Outcome", "Summary"
+        ])
+        df.to_csv(LEADS_FILE, index=False)
+
+def save_lead_to_db(lead_info, chat_history, outcome):
+    init_db()
+    # Ask AI to extract structured data from chat
+    model = genai.GenerativeModel(MODEL_NAME)
+    chat_text = "\n".join([f"{m['role']}: {m['content']}" for m in chat_history])
+    
+    prompt = f"""
+    Analyze this sales conversation:
+    {chat_text}
+    
+    Extract these fields in JSON format:
+    - pain_point: What is the client's main problem?
+    - budget: Did they mention money/price sensitivity?
+    - summary: 1 sentence summary of the call.
+    """
+    try:
+        response = model.generate_content(prompt)
+        # Simple parsing (in production use structured output)
+        ai_data = response.text
+    except:
+        ai_data = "AI Extraction Failed"
+
+    new_row = {
+        "Date": datetime.now().strftime("%Y-%m-%d %H:%M"),
+        "Name": lead_info.get("name"),
+        "Company": lead_info.get("company"),
+        "Type": lead_info.get("type"),
+        "Context": lead_info.get("context"),
+        "Pain Point": "AI Analysis Pending", # Placeholder for simplicity
+        "Budget": "Unknown",
+        "Outcome": outcome,
+        "Summary": f"Call with {len(chat_history)} messages. {outcome}"
+    }
+    
+    df = pd.read_csv(LEADS_FILE)
+    df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)
+    df.to_csv(LEADS_FILE, index=False)
+
+# --- AI & GRAPH LOGIC ---
+def configure_genai(api_key):
+    try:
+        genai.configure(api_key=api_key)
+        return True
+    except: return False
+
+def load_graph_data():
+    script_file = "sales_script_learned.json" if os.path.exists("sales_script_learned.json") else "sales_script.json"
+    with open(script_file, "r", encoding="utf-8") as f: data = json.load(f)
+    nodes = data["nodes"]
+    edges = data["edges"]
+    node_to_id = {name: i for i, name in enumerate(nodes.keys())}
+    id_to_node = {i: name for i, name in enumerate(nodes.keys())}
+    graph = Graph(len(nodes), directed=True)
+    for edge in edges:
+        if edge["from"] in node_to_id and edge["to"] in node_to_id:
+            graph.add_edge(node_to_id[edge["from"]], node_to_id[edge["to"]], edge["weight"])
+    return graph, node_to_id, id_to_node, nodes, edges
+
+def get_predicted_path(graph, start_id, target_id, id_to_node, node_to_id):
+    # Get visited node IDs and client type for smart pathfinding
+    visited_ids = [node_to_id[n] for n in st.session_state.get('visited_history', []) if n in node_to_id]
+    client_type = st.session_state.lead_info.get('type', 'B2B')
+    
+    # Use enhanced Bellman-Ford with penalties
+    dist = bellman_ford_list(graph, start_id, visited_nodes=visited_ids, client_type=client_type)
+    if dist[target_id] == float('inf'): return []
+    path = [target_id]
+    curr = target_id
+    while curr != start_id:
+        found = False
+        for u in range(graph.num_vertices):
+            for v, w in graph.adj_list[u]:
+                if v == curr and dist[v] == dist[u] + w:
+                    path.append(u); curr = u; found = True; break
+            if found: break
+        if not found: break
+    return [id_to_node[i] for i in reversed(path)]
+
+def analyze_full_context(model, user_input, current_node, chat_history):
+    """
+    Аналізує Інтент, Емоції та ПСИХОТИП клієнта.
+    """
+    history_text = "\n".join([f"{m['role']}: {m['content']}" for m in chat_history[-4:]]) # Беремо останні 4 фрази для контексту
+    
+    prompt = f"""
+    ROLE: Behavioral Psychologist & Sales Expert.
+    
+    CONTEXT:
+    Current Step: "{current_node}"
+    Recent Chat History:
+    {history_text}
+    User just said: "{user_input}"
+    
+    TASK 1: Detect User Archetype (Pattern). Choose ONE:
+    - DRIVER (Direct, impatient, results-oriented)
+    - ANALYST (Detail-oriented, asks 'how', skeptical)
+    - EXPRESSIVE (Emotional, enthusiastic, visionary)
+    - CONSERVATIVE (Risk-averse, slow, likes stability)
+    
+    TASK 2: Analyze Intent (MOVE, STAY, EXIT).
+    
+    OUTPUT JSON format:
+    {{
+        "archetype": "DRIVER" | "ANALYST" | "EXPRESSIVE" | "CONSERVATIVE",
+        "intent": "MOVE" | "STAY" | "EXIT",
+        "reasoning": "Why you chose this archetype (1 short sentence)"
+    }}
+    """
+    try:
+        response = model.generate_content(prompt)
+        clean_text = response.text.replace("```json", "").replace("```", "").strip()
+        return json.loads(clean_text)
+    except:
+        return {"archetype": "UNKNOWN", "intent": "STAY", "reasoning": "Error"}
+
+def generate_response(model, context, user_input, intent, lead_info, archetype):
+    # Визначаємо стиль спілкування залежно від патерну
+    style_instruction = ""
+    
+    if archetype == "DRIVER":
+        style_instruction = "STYLE: Ultra-short, confident. Focus on ROI and speed. No fluff. Be direct."
+    elif archetype == "ANALYST":
+        style_instruction = "STYLE: Logical, detailed. Use facts, numbers, and technical terms. Prove your point."
+    elif archetype == "EXPRESSIVE":
+        style_instruction = "STYLE: Energetic, inspiring. Use metaphors, exclamation marks. Focus on the 'Future Success'."
+    elif archetype == "CONSERVATIVE":
+        style_instruction = "STYLE: Calm, supportive, safe. Emphasize low risk, support, and ease of use. Don't push."
+    else:
+        style_instruction = "STYLE: Professional and polite."
+
+    if intent == "STAY":
+        prompt = f"""
+        ROLE: Chameleon Sales Rep.
+        ARCHETYPE DETECTED: {archetype} -> {style_instruction}
+        
+        SITUATION: Step "{context}". Client Objected: "{user_input}".
+        TASK: Handle objection strictly matching the detected STYLE.
+        CONSTRAINT: Speak naturally in Ukrainian. Output ONLY the response.
+        """
+    else:
+        prompt = f"""
+        ROLE: Chameleon Sales Rep.
+        ARCHETYPE DETECTED: {archetype} -> {style_instruction}
+        
+        GOAL: Transition to "{context}". User said: "{user_input}".
+        TASK: Bridge to the next step using the detected STYLE.
+        CONSTRAINT: Speak naturally in Ukrainian. Output ONLY the response.
+        """
+        
+    try:
+        return model.generate_content(prompt).text.strip()
+    except: return "..."
+
+# --- UI COMPONENTS ---
+def draw_graph(graph_data, current_node, predicted_path):
+    nodes = graph_data[3]
+    edges = graph_data[4]
+    dot = graphviz.Digraph()
+    dot.attr(rankdir='LR', splines='ortho')
+    dot.attr('node', shape='box', style='rounded,filled', fontname='Arial', fontsize='10')
+    
+    for n in nodes:
+        fill = '#F0F2F6'; color = '#BDC3C7'; pen = '1'
+        if n == current_node: fill = '#FF4B4B'; color = 'black'; pen = '2'
+        elif n in predicted_path: fill = '#FFF8E1'; color = '#F1C40F'
+        dot.node(n, label=n, fillcolor=fill, color=color, penwidth=pen)
+        
+    for e in edges:
+        color = '#BDC3C7'
+        if e["from"] in predicted_path and e["to"] in predicted_path: color = '#F1C40F'
+        dot.edge(e["from"], e["to"], color=color)
+    return dot
+
+# --- MAIN APP ---
+st.sidebar.title("🛠️ SellMe Control")
+
+# --- API KEY SETUP (Cloud + Local) ---
+if "GOOGLE_API_KEY" in st.secrets:
+    api_key = st.secrets["GOOGLE_API_KEY"]
+else:
+    api_key = st.sidebar.text_input("Google API Key", type="password")
+
+if st.sidebar.button("📊 Dashboard"): st.session_state.page = "dashboard"; st.rerun()
+if st.sidebar.button("📞 New Call"): st.session_state.page = "setup"; st.rerun()
+
+if not api_key:
+    st.warning("🔑 Please enter API Key to start.")
+    st.stop()
+
+configure_genai(api_key)
+model = genai.GenerativeModel(MODEL_NAME)
+graph_data = load_graph_data()
+graph, node_to_id, id_to_node, nodes, edges = graph_data
+
+# --- PAGE: DASHBOARD ---
+if st.session_state.page == "dashboard":
+    st.title("📊 CRM Analytics")
+    init_db()
+    df = pd.read_csv(LEADS_FILE)
+    if not df.empty:
+        c1, c2, c3 = st.columns(3)
+        c1.metric("Total Calls", len(df))
+        c2.metric("B2B Leads", len(df[df['Type']=='B2B']))
+        c3.metric("Success", len(df[df['Outcome']=='Success']))
+        st.dataframe(df)
+    else: st.info("Database empty.")
+
+# --- PAGE: SETUP ---
+elif st.session_state.page == "setup":
+    st.title("👤 Lead Setup")
+    with st.form("lead_form"):
+        c1, c2 = st.columns(2)
+        name = c1.text_input("Name", "John Doe")
+        company = c2.text_input("Company", "Acme Corp")
+        l_type = c1.selectbox("Type", ["B2B", "B2C"])
+        context = c2.selectbox("Context", ["Cold Call", "Warm Lead", "Follow-up"])
+        
+        if st.form_submit_button("Start Call"):
+            st.session_state.lead_info = {"name": name, "company": company, "type": l_type, "context": context}
+            st.session_state.messages = []
+            st.session_state.current_node = "start"
+            st.session_state.visited_history = []  # Reset visited history
+            st.session_state.checklist = {k:False for k in st.session_state.checklist} # Reset
+            st.session_state.page = "chat"
+            st.rerun()
+
+# --- PAGE: CHAT ---
+elif st.session_state.page == "chat":
+    st.markdown(f"### Call with {st.session_state.lead_info['name']}")
+    
+    col_chat, col_tools = st.columns([1.5, 1])
+    
+    with col_tools:
+        st.markdown("#### 🎯 Call Objectives")
+        # Logic to auto-update checklist based on node
+        if "qualification" in st.session_state.current_node: st.session_state.checklist["Identify Customer"] = True
+        if "pain" in st.session_state.current_node or "shame" in st.session_state.current_node: st.session_state.checklist["Determine Objectives"] = True
+        if "pitch" in st.session_state.current_node: st.session_state.checklist["Outline Advantages"] = True
+        
+        for goal, done in st.session_state.checklist.items():
+            icon = "✅" if done else "⬜"
+            st.write(f"{icon} {goal}")
+        
+        # Display Client Profile (Real-time)
+        st.markdown("#### 🧠 Client Profile (Real-time)")
+        
+        # Get current archetype from session
+        current_archetype = st.session_state.get("current_archetype", "Analyzing...")
+        
+        # Visual cards for archetypes
+        cols = st.columns(4)
+        
+        # Styles for highlighting
+        def get_opacity(target): return "1.0" if current_archetype == target else "0.3"
+        
+        cols[0].markdown(f"<div style='opacity:{get_opacity('DRIVER')}; font-size:20px; text-align:center'>🔴<br>Boss</div>", unsafe_allow_html=True)
+        cols[1].markdown(f"<div style='opacity:{get_opacity('ANALYST')}; font-size:20px; text-align:center'>🔵<br>Analyst</div>", unsafe_allow_html=True)
+        cols[2].markdown(f"<div style='opacity:{get_opacity('EXPRESSIVE')}; font-size:20px; text-align:center'>🟡<br>Fan</div>", unsafe_allow_html=True)
+        cols[3].markdown(f"<div style='opacity:{get_opacity('CONSERVATIVE')}; font-size:20px; text-align:center'>🟢<br>Safe</div>", unsafe_allow_html=True)
+        
+        if st.session_state.reasoning:
+            st.caption(f"🤖 AI Insight: {st.session_state.reasoning}")
+            
+        st.markdown("#### 📊 AI Strategy")
+        curr_id = node_to_id[st.session_state.current_node]
+        target_id = node_to_id["close_deal"]  # Fixed: using close_deal from sales_script.json
+        path = get_predicted_path(graph, curr_id, target_id, id_to_node, node_to_id)
+        st.graphviz_chart(draw_graph(graph_data, st.session_state.current_node, path))
+
+    with col_chat:
+        for msg in st.session_state.messages:
+            with st.chat_message(msg["role"]): st.write(msg["content"])
+            
+        if not st.session_state.messages:
+            greeting = nodes["start"]
+            # Adapt greeting based on B2B/B2C
+            if st.session_state.lead_info.get('type') == 'B2B':
+                greeting = f"Доброго дня, це {st.session_state.lead_info.get('company', 'компанія')}? Мене звати..."
+            st.session_state.messages.append({"role": "assistant", "content": greeting})
+            st.rerun()
+
+        if user_input := st.chat_input("Reply..."):
+            st.session_state.messages.append({"role": "user", "content": user_input})
+            
+            # Logic - Analyze with full context including archetype detection
+            current_text = nodes[st.session_state.current_node]
+            analysis = analyze_full_context(model, user_input, st.session_state.current_node, st.session_state.messages)
+            intent = analysis.get("intent", "STAY")
+            archetype = analysis.get("archetype", "UNKNOWN")
+            reasoning = analysis.get("reasoning", "")
+            
+            # Store archetype and reasoning for display
+            st.session_state.current_archetype = archetype
+            st.session_state.reasoning = reasoning
+            
+            if "EXIT" in intent:
+                outcome = "Success" if "close" in st.session_state.current_node else "Fail"
+                save_lead_to_db(st.session_state.lead_info, st.session_state.messages, outcome)
+                st.success("Call Saved!")
+                st.session_state.page = "dashboard"; st.rerun()
+            
+            elif "STAY" in intent:
+                resp = generate_response(model, current_text, user_input, "STAY", st.session_state.lead_info, archetype)
+            
+            else: # MOVE
+                # Track current node in visited history before moving
+                if st.session_state.current_node not in st.session_state.visited_history:
+                    st.session_state.visited_history.append(st.session_state.current_node)
+                
+                curr_id = node_to_id[st.session_state.current_node]
+                best_next = None; min_w = float('inf')
+                for n, w in graph.adj_list[curr_id]:
+                    if w < min_w: min_w = w; best_next = n
+                
+                if best_next is not None:
+                    st.session_state.current_node = id_to_node[best_next]
+                    new_text = nodes[st.session_state.current_node]
+                    resp = generate_response(model, new_text, user_input, "MOVE", st.session_state.lead_info, archetype)
+                else:
+                    resp = "Call finished."
+                    save_lead_to_db(st.session_state.lead_info, st.session_state.messages, "End of Script")
+
+            st.session_state.messages.append({"role": "assistant", "content": resp})
+            st.rerun()

leads.csv

@@ -0,0 +1 @@
+Date,Client Name,Company,Type,Context,Outcome,Final Step,Summary

leads_database.csv

@@ -0,0 +1 @@
+Date,Name,Company,Type,Context,Pain Point,Budget,Outcome,Summary

leads_manager.py

@@ -0,0 +1,51 @@
+import pandas as pd
+import os
+from datetime import datetime
+
+LEADS_FILE = "leads.csv"
+
+def init_db():
+    """Створює файл, якщо його немає"""
+    if not os.path.exists(LEADS_FILE):
+        df = pd.DataFrame(columns=[
+            "Date", "Client Name", "Company", "Type", "Context", 
+            "Outcome", "Final Step", "Summary"
+        ])
+        df.to_csv(LEADS_FILE, index=False)
+
+def save_lead(lead_data, outcome, final_step, chat_history):
+    """Зберігає результат розмови"""
+    init_db()
+    
+    # Робимо просте самарі (останні 2 повідомлення або статус)
+    summary = f"Ended at {final_step}. Total msgs: {len(chat_history)}"
+    
+    new_row = {
+        "Date": datetime.now().strftime("%Y-%m-%d %H:%M"),
+        "Client Name": lead_data.get("name", "Unknown"),
+        "Company": lead_data.get("company", "-"),
+        "Type": lead_data.get("type", "B2B"),
+        "Context": lead_data.get("context", "Cold Call"),
+        "Outcome": outcome,
+        "Final Step": final_step,
+        "Summary": summary
+    }
+    
+    df = pd.read_csv(LEADS_FILE)
+    df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)
+    df.to_csv(LEADS_FILE, index=False)
+    return True
+
+def get_analytics():
+    """Повертає статистику для дашборду"""
+    init_db()
+    df = pd.read_csv(LEADS_FILE)
+    if df.empty:
+        return None
+        
+    stats = {
+        "total": len(df),
+        "success_rate": round(len(df[df["Outcome"] == "Success"]) / len(df) * 100, 1),
+        "top_fail_reasons": df[df["Outcome"] == "Fail"]["Final Step"].value_counts().head(3)
+    }
+    return df, stats

packages.txt

@@ -0,0 +1 @@
+graphviz

requirements.txt

@@ -0,0 +1,4 @@
+streamlit
+google-generativeai
+pandas
+graphviz

sales_script.json

@@ -1,14 +1,20 @@
 {
     "nodes": {
-        "start": "Привіт! Це AI-асистент SellMe. Маєте хвилинку?",
-        "qualification": "Скажіть, ви використовуєте CRM систему?",
-        "pitch_crm": "Круто! Наш AI інтегрується з вашою CRM і сам заповнює картки.",
-        "pitch_no_crm": "Зрозумів. Наш AI може працювати навіть без CRM, в Телеграмі.",
-        "price_question": "Скільки це коштує? - Це залежить від кількості менеджерів.",
-        "objection_expensive": "Дорого? А скільки ви втрачаєте на незакритих угодах?",
-        "discount_offer": "Можемо запропонувати тестовий період за 1$.",
-        "close_deal": "Домовились! Висилаю посилання на оплату.",
-        "exit_bad": "Добре, вибачте за турботу. Гарного дня."
+        "start": "Привіт! Це AI-асистент SellMe. У нас є рішення, що піднімає продажі на 30%. Маєте хвилинку дізнатись як?",
+        "objection_busy": "Розумію, часу обмаль. Але це займе всього 30 секунд, а зекономить вам години. Тільки одне питання: як ви зараз шукаєте клієнтів?",
+        "qualification": "Скажіть, ви зараз використовуєте якусь CRM чи ведете все в Excel/блокноті?",
+        "tech_shame": "Ох, Excel — це класика, але ж уявіть, скільки лідів там губиться! А якби система сама нагадувала про кожного клієнта?",
+        "tech_praise": "Круто, що у вас є CRM! Але чи заповнюють її менеджери руками? Наш AI робить це автоматично.",
+        "pitch_value": "Суть проста: SellMe слухає розмову і сам створює угоду в CRM. Ви економите 2 години в день. Звучить цікаво?",
+        "objection_trust": "Звучить надто добре, щоб бути правдою? Розумію. Ми теж так думали, поки не побачили кейс компанії 'SoftGroup', яка скоротила штат вдвічі, зберігши прибуток.",
+        "objection_competitor": "А, ви про [Конкурента]? Вони молодці. Але у них ви платите за кожного юзера, а у нас — безліміт. Навіщо переплачувати?",
+        "price_reveal": "Вартість всього 50$ на місяць за всю команду. Це дешевше, ніж кава для офісу.",
+        "objection_expensive": "50$ — це дорого? Давайте порахуємо. Якщо AI врятує хоча б одну угоду на місяць, він вже окупився в 10 разів. Хіба ні?",
+        "objection_think": "Розумію, треба подумати. Але поки ви думаєте, ваші конкуренти вже впроваджують AI. Може, просто спробуємо безкоштовний тиждень?",
+        "soft_push": "Дивіться, ви нічим не ризикуєте. Я можу відкрити доступ прямо зараз без картки. Спробуємо?",
+        "close_deal": "Чудово! Ви мудрий керівник. Диктуйте пошту, куди скинути доступ.",
+        "exit_bad": "Добре. Якщо захочете автоматизувати хаос — ми тут. Гарного дня.",
+        "exit_later": "Окей, я наберу вас через місяць, коли вам набридне заповнювати звіти руками. До зв'язку!"
     },
     "edges": [
         {
@@ -16,55 +22,130 @@
             "to": "qualification",
             "weight": 1
         },
+        {
+            "from": "start",
+            "to": "objection_busy",
+            "weight": 5
+        },
         {
             "from": "start",
             "to": "exit_bad",
             "weight": 100
         },
+        {
+            "from": "objection_busy",
+            "to": "qualification",
+            "weight": 2
+        },
+        {
+            "from": "objection_busy",
+            "to": "exit_later",
+            "weight": 10
+        },
+        {
+            "from": "qualification",
+            "to": "tech_shame",
+            "weight": 2
+        },
         {
             "from": "qualification",
-            "to": "pitch_crm",
+            "to": "tech_praise",
             "weight": 2
         },
         {
             "from": "qualification",
-            "to": "pitch_no_crm",
+            "to": "objection_trust",
+            "weight": 5
+        },
+        {
+            "from": "tech_shame",
+            "to": "pitch_value",
+            "weight": 1
+        },
+        {
+            "from": "tech_praise",
+            "to": "pitch_value",
+            "weight": 1
+        },
+        {
+            "from": "pitch_value",
+            "to": "price_reveal",
             "weight": 2
         },
         {
-            "from": "pitch_crm",
-            "to": "price_question",
+            "from": "pitch_value",
+            "to": "objection_competitor",
+            "weight": 4
+        },
+        {
+            "from": "pitch_value",
+            "to": "objection_trust",
+            "weight": 4
+        },
+        {
+            "from": "objection_trust",
+            "to": "pitch_value",
             "weight": 2
         },
         {
-            "from": "pitch_no_crm",
-            "to": "price_question",
+            "from": "objection_trust",
+            "to": "soft_push",
             "weight": 3
         },
         {
-            "from": "price_question",
-            "to": "objection_expensive",
-            "weight": 5
+            "from": "objection_competitor",
+            "to": "price_reveal",
+            "weight": 2
+        },
+        {
+            "from": "objection_competitor",
+            "to": "exit_bad",
+            "weight": 50
         },
         {
-            "from": "price_question",
+            "from": "price_reveal",
             "to": "close_deal",
             "weight": 10
         },
         {
-            "from": "objection_expensive",
-            "to": "exit_bad",
-            "weight": 50
+            "from": "price_reveal",
+            "to": "objection_expensive",
+            "weight": 3
+        },
+        {
+            "from": "price_reveal",
+            "to": "objection_think",
+            "weight": 4
         },
         {
             "from": "objection_expensive",
-            "to": "discount_offer",
+            "to": "soft_push",
             "weight": 1
         },
         {
-            "from": "discount_offer",
+            "from": "objection_expensive",
+            "to": "exit_bad",
+            "weight": 20
+        },
+        {
+            "from": "objection_think",
+            "to": "soft_push",
+            "weight": 2
+        },
+        {
+            "from": "objection_think",
+            "to": "exit_later",
+            "weight": 5
+        },
+        {
+            "from": "soft_push",
             "to": "close_deal",
             "weight": 1
+        },
+        {
+            "from": "soft_push",
+            "to": "exit_later",
+            "weight": 10
         }
     ]
 }