app.py

# =========================================================
# VendIQ – Intelligent Market Assistant (LangGraph + Gradio)
# Founder: Rohit Kumar | Data Scientist
# =========================================================

import os
import random
import sqlite3
from datetime import datetime
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw, ImageFont
import torch

from transformers import pipeline
from diffusers import StableDiffusionPipeline

# ---- LangGraph Imports ----
from langgraph.graph import StateGraph, END
from typing import TypedDict, Optional

# ---- LangChain community imports ----
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS

# ---- UI ----
import gradio as gr

# =========================================================
# 1. CONFIGURATION
# =========================================================
TEXT_MODEL = "google/flan-t5-large"
IMAGE_MODEL = "runwayml/stable-diffusion-v1-5"
EMBED_MODEL = "sentence-transformers/all-MiniLM-L6-v2"

PDF_PATH = "Business Strategy Document – Mukesh Ambani Style.pdf"
DB_PATH = "vendiq.db"

device = "cuda" if torch.cuda.is_available() else "cpu"
text_gen = pipeline("text2text-generation", model=TEXT_MODEL)

try:
    image_gen = StableDiffusionPipeline.from_pretrained(
        IMAGE_MODEL,
        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
        safety_checker=None
    ).to(device)
except Exception:
    image_gen = None

# =========================================================
# 2. DATABASE
# =========================================================
def init_db():
    conn = sqlite3.connect(DB_PATH)
    c = conn.cursor()
    c.execute('''CREATE TABLE IF NOT EXISTS deals (
                    id INTEGER PRIMARY KEY AUTOINCREMENT,
                    company TEXT,
                    product TEXT,
                    strategy TEXT,
                    profit REAL,
                    marketing TEXT,
                    created_at TEXT
                )''')
    conn.commit()
    conn.close()

init_db()

# =========================================================
# 3. CONTEXT MEMORY
# =========================================================
def load_strategy():
    emb = HuggingFaceEmbeddings(model_name=EMBED_MODEL)
    if os.path.exists(PDF_PATH):
        loader = PyPDFLoader(PDF_PATH)
        pages = loader.load()
        vectordb = FAISS.from_documents(pages, emb)
        return vectordb
    return FAISS.from_texts(
        ["Reliance-style strategy emphasizing scale, technology, and market dominance"],
        emb
    )

vectordb = load_strategy()

def get_context(query):
    try:
        results = vectordb.similarity_search(query, k=2)
        return "\n".join([r.page_content for r in results])
    except Exception:
        return "Reliance-inspired strategy: scale, vertical integration, and customer-first innovation."

# =========================================================
# 4. LANGGRAPH STATE
# =========================================================
class VendIQState(TypedDict):
    scenario: str
    product: str
    context: Optional[str]
    strategy: Optional[str]
    marketing: Optional[str]
    profit: Optional[float]
    chart_path: Optional[str]
    ad_path: Optional[str]

# =========================================================
# 5. NODES
# =========================================================
def context_node(state: VendIQState):
    context = get_context(state["scenario"])
    state["context"] = context if context else f"General B2B environment for {state['scenario']}"
    return state


def strategy_node(state: VendIQState):
    tone = random.choice([
        "aggressive expansion mindset",
        "strategic partnership and alliance-driven growth",
        "innovation-led differentiation and disruption",
        "cost leadership with scalability focus",
        "customer loyalty and retention-based positioning"
    ])

    focus = random.choice([
        "digital transformation and process automation",
        "supply chain optimization and vendor integration",
        "B2B channel growth and ecosystem partnerships",
        "new market entry and regional dominance",
        "AI-driven customer analytics and personalization"
    ])

    risk_profile = random.choice([
        "high-reward disruptive expansion",
        "balanced strategic scaling",
        "low-risk sustainable long-term growth"
    ])

    prompt = f"""
You are **VendIQ**, an AI Strategy Architect modeled on Mukesh Ambani’s philosophy:
*scale fast, integrate vertically, and own the market ecosystem.*

---

### 🧩 Context
{state["context"]}

### 🌍 Scenario
{state["scenario"]}

### 🏷️ Product
{state["product"]}

---

### 🎯 Objective
Formulate a **5-Step Negotiation & Market-Entry Strategy** for **{state["product"]}**, crafted in Ambani-style — focused on ecosystem control, long-term scale, and competitive dominance.

🔹 **Approach Tone:** {tone}  
🔹 **Strategic Focus:** {focus}  
🔹 **Risk Appetite:** {risk_profile}

---

### 📘 Each Step Should Include:
1️⃣ **Objective** — Clear, measurable business goal.  
2️⃣ **Action Plan** — Concrete strategic move or partnership.  
3️⃣ **Rationale** — Market or competitive logic behind it.  
4️⃣ **Expected Outcome** — Measurable or strategic impact.  
5️⃣ **Ambani-Style Twist** — How vertical integration or scale amplifies the result.

---

Format clearly using numbered steps (1–5).  
End with a single-line **Positioning Summary** showing how {state["product"]} establishes category dominance.

Maintain a professional, analytical tone — like a market intelligence brief.
"""
    state["strategy"] = text_gen(prompt.strip(), max_length=700, temperature=0.85)[0]["generated_text"]
    return state

# =========================================================
# 🧩 MARKETING NODE — DISTINCT 7-DAY PLAN (Ambani-Style)
# =========================================================

def marketing_node(state):
    """
    Generates a 7-Day Ambani-style Marketing Plan based on the scenario and product.
    Ensures clear day-wise objectives, actions, and KPIs.
    """
    prompt = f"""
You are **VendIQ**, an AI-powered B2B Marketing Strategist inspired by Reliance’s agility, scale, and dominance mindset.
Your goal is to convert a strategic vision into a precise 7-day marketing execution plan.

### Scenario
{state["scenario"]}

### Product
{state["product"]}

### Strategic Philosophy (for inspiration)
{state["context"]}

---

### TASK
Ignore negotiation or high-level strategy repetition. 
Focus on practical execution — digital + field + B2B partnerships. 
Be concise, measurable, and bold. Use Reliance-style scale thinking.

Generate a **7-Day Marketing Plan** using this format:

#### 🗓️ Day 1: Market Pulse
- **Objective:** Identify initial customer response
- **Action Steps:** (2–3 crisp bullet points)
- **KPIs:** (measurable goals)

#### 🚀 Day 2: Visibility & Awareness
...

Continue till **Day 7**, ending with a summary that highlights momentum, scaling, and brand loyalty.

Ensure:
- Each day has a unique purpose.
- Avoid repeating negotiation/strategy language.
- Include Ambani-style confidence in tone.
- Output should read like a business execution manual.

---

### OUTPUT
Now write the full 7-day marketing plan below:
"""

    from transformers import pipeline
    text_gen = pipeline("text-generation", model="google/flan-t5-large")

    marketing_plan = text_gen(prompt.strip(), max_length=900, temperature=1.05)[0]["generated_text"]

    # Save to state
    state["marketing"] = marketing_plan
    return state



def profit_node(state: VendIQState):
    """
    Generates a realistic 7-day profit forecast visualization for the product.
    Includes business-like chart design and performance trend logic.
    """
    base_profit = random.randint(500, 2500)
    growth_trend = random.choice(["upward", "steady", "volatile", "decline-risk"])

    if growth_trend == "upward":
        profits = [base_profit + i * random.randint(40, 120) for i in range(7)]
    elif growth_trend == "steady":
        profits = [base_profit + random.randint(-50, 50) for _ in range(7)]
    elif growth_trend == "volatile":
        profits = [base_profit + random.randint(-200, 250) for _ in range(7)]
    else:
        profits = [base_profit - i * random.randint(20, 70) for i in range(7)]

    days = list(range(1, 8))

    plt.figure(figsize=(6, 3.5))
    plt.plot(days, profits, marker="o", linewidth=2)
    plt.title(f"📊 7-Day Profit Projection – {state['product']}", fontsize=11, fontweight="bold")
    plt.xlabel("Day", fontsize=9)
    plt.ylabel("Profit ($)", fontsize=9)
    plt.grid(True, linestyle="--", alpha=0.6)
    plt.tight_layout()

    chart_path = f"{state['product']}_profit.png"
    plt.savefig(chart_path, dpi=150)
    plt.close()

    state["profit"] = profits[-1]
    state["chart_path"] = chart_path
    return state


def ad_node(state: VendIQState):
    """
    Generates a B2B advertisement image for the product.
    Uses Stable Diffusion if available; otherwise, falls back to a styled banner.
    """
    theme = random.choice([
        "AI innovation and automation",
        "enterprise intelligence and scale",
        "sustainability and green business",
        "luxury fintech excellence",
        "smart logistics transformation",
        "healthcare precision technology"
    ])

    color_scheme = random.choice([
        "blue and silver",
        "black and gold",
        "emerald green and white",
        "navy and cyan",
        "crimson red and charcoal gray",
        "deep teal and white"
    ])

    visual_tone = random.choice([
        "futuristic realism",
        "executive corporate branding",
        "3D minimalism with strong contrast",
        "AI-powered digital theme",
        "luxury business aesthetics"
    ])

    if image_gen:
        prompt = (
            f"Design a premium B2B advertisement for a product named {state['product']}. "
            f"Theme: {theme}. "
            f"Color scheme: {color_scheme}. "
            f"Visual tone: {visual_tone}. "
            f"Include elements of market leadership, innovation, and trust. "
            f"Style: professional, cinematic, and Ambani-style corporate dominance. "
            f"No text or logos. 16:9 aspect ratio."
        )
        img = image_gen(prompt).images[0]
    else:
        # Fallback banner generation
        color_map = {
            "blue and silver": (40, 60, 140),
            "black and gold": (30, 25, 25),
            "emerald green and white": (0, 100, 60),
            "navy and cyan": (15, 40, 80),
            "crimson red and charcoal gray": (90, 20, 30),
            "deep teal and white": (0, 80, 85)
        }
        bg_color = color_map.get(color_scheme, (0, 80, 160))
        img = Image.new("RGB", (960, 540), color=bg_color)
        draw = ImageDraw.Draw(img)
        font = ImageFont.load_default()

        # Add subtle design
        draw.rectangle([0, 400, 960, 540], fill=(255, 255, 255, 40))
        draw.text((30, 430), f"{state['product']}", font=font, fill="white")
        draw.text((30, 460), "Powered by VendIQ – B2B Market Intelligence", font=font, fill="white")

    ad_path = f"{state['product']}_ad.png"
    img.save(ad_path)
    state["ad_path"] = ad_path
    return state
from reportlab.lib.pagesizes import A4
from reportlab.pdfgen import canvas
from reportlab.lib.units import inch
from reportlab.lib.utils import simpleSplit

def save_to_pdf(product, strategy_text, marketing_text):
    filename = f"{product.replace(' ', '_')}_VendIQ_Report.pdf"
    c = canvas.Canvas(filename, pagesize=A4)
    width, height = A4

    # ---- Title ----
    c.setFont("Helvetica-Bold", 18)
    c.drawCentredString(width / 2, height - 1*inch, "VendIQ – Strategic Market Report")

    c.setFont("Helvetica", 12)
    c.drawCentredString(width / 2, height - 1.3*inch, f"Product: {product}")
    c.drawCentredString(width / 2, height - 1.6*inch, datetime.now().strftime("Generated on %Y-%m-%d %H:%M"))
    c.line(1*inch, height - 1.7*inch, width - 1*inch, height - 1.7*inch)

    # ---- Section 1: Negotiation & Market Entry ----
    text_obj = c.beginText(1*inch, height - 2.2*inch)
    text_obj.setFont("Helvetica-Bold", 14)
    text_obj.textLine("Section 1 – Negotiation & Market Entry Strategy")
    text_obj.setFont("Helvetica", 11)
    text_obj.textLine("")
    for line in simpleSplit(strategy_text, "Helvetica", 11, width - 2*inch):
        text_obj.textLine(line)
    c.drawText(text_obj)

    # ---- Section 2: 7-Day Marketing Plan ----
    c.showPage()
    text_obj = c.beginText(1*inch, height - 1*inch)
    text_obj.setFont("Helvetica-Bold", 14)
    text_obj.textLine("Section 2 – 7-Day B2B Marketing Plan")
    text_obj.setFont("Helvetica", 11)
    text_obj.textLine("")
    for line in simpleSplit(marketing_text, "Helvetica", 11, width - 2*inch):
        text_obj.textLine(line)
    c.drawText(text_obj)

    c.save()
    return filename


def save_node(state: VendIQState):
    conn = sqlite3.connect(DB_PATH)
    c = conn.cursor()
    c.execute(
        "INSERT INTO deals (company, product, strategy, profit, marketing, created_at) VALUES (?, ?, ?, ?, ?, ?)",
        ("GlobalTech", state["product"], state["strategy"], state["profit"], state["marketing"],
         datetime.now().strftime("%Y-%m-%d %H:%M"))
    )
    conn.commit()
    conn.close()
    pdf_path = save_to_pdf(state["product"], state["strategy"], state["marketing"])
    state["pdf_path"] = pdf_path
    return state

# =========================================================
# 6. LANGGRAPH PIPELINE
# =========================================================
graph = StateGraph(VendIQState)
graph.add_node("context", context_node)
graph.add_node("strategy", strategy_node)
graph.add_node("marketing", marketing_node)
graph.add_node("profit", profit_node)
graph.add_node("ad", ad_node)
graph.add_node("save", save_node)

graph.set_entry_point("context")
graph.add_edge("context", "strategy")
graph.add_edge("strategy", "marketing")
graph.add_edge("marketing", "profit")
graph.add_edge("profit", "ad")
graph.add_edge("ad", "save")
graph.add_edge("save", END)

vendiq_graph = graph.compile()

# =========================================================
# 7. GRADIO UI
# =========================================================
def vendiq_app(scenario, product):
    if not scenario or not product:
        return "⚠ Please enter both fields.", None, None, None, None, None

    result = vendiq_graph.invoke({"scenario": scenario, "product": product})
    return (
        result["strategy"],
        result["profit"],
        result["chart_path"],
        result["marketing"],
        result["ad_path"],
        result.get("pdf_path")  # ✅ include PDF download link
    )


with gr.Blocks(title="VendIQ – Intelligent Market Assistant") as app:
    gr.Markdown("<h1 style='text-align:center;'>VendIQ – Intelligent Market Assistant</h1>")
    gr.Markdown("<p style='text-align:center;'>B2B Strategy, Marketing, and Profit Insights with Ambani-style intelligence.</p>")

    with gr.Tab("🚀 Generate Strategy"):
        scenario = gr.Textbox(label="Business Scenario", placeholder="E.g., entering new logistics market")
        product = gr.Textbox(label="Product", placeholder="E.g., AI Supply Chain Platform")
        btn = gr.Button("Generate Strategy")

        strategy_out = gr.Textbox(label="Negotiation Strategy", lines=6)
        profit_out = gr.Number(label="Projected Profit ($)")
        chart_out = gr.Image(label="Profit Chart")
        marketing_out = gr.Textbox(label="7-Day Marketing Plan", lines=6)
        ad_out = gr.Image(label="Ad Image")
        pdf_out = gr.File(label="📄 Download Full Report (PDF)")  # ✅ Added output

        btn.click(
            fn=vendiq_app,
            inputs=[scenario, product],
            outputs=[strategy_out, profit_out, chart_out, marketing_out, ad_out, pdf_out]
        )

app.launch()