app2.py

import streamlit as st
from PIL import Image
import matplotlib.pyplot as plt
import networkx as nx
import json
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
torch.cuda.empty_cache()
import os
import numpy as np

from pipeline.detector import detect_symbols_and_lines
from pipeline.graph_builder import build_graph
from pipeline.gnn_model import run_gnn
from pipeline.agent import generate_agent_actions

st.set_page_config(layout="wide")
st.title("?? Agentic Predictive Maintenance (P&ID Graph + GNN)")

# ===== Initialize Session State =====
for key, default in {
    "G": None,
    "feature_map": {},
    "scores": {},
    "fig": None,
    "actions": [],
    "deepseek_responses": [],
}.items():
    if key not in st.session_state:
        st.session_state[key] = default

# ===== Redisplay Previous Outputs =====
if st.session_state["fig"]:
    st.subheader("?? Previous Graph Visualization")
    st.pyplot(st.session_state["fig"])

if st.session_state["actions"]:
    st.subheader("??? Previous Agent Actions")
    for action in st.session_state["actions"]:
        st.write(action)

if st.session_state["deepseek_responses"]:
    st.subheader("?? Previous DeepSeek Responses")
    for r in st.session_state["deepseek_responses"]:
        st.markdown(f"**You:** {r['query']}")
        st.markdown(f"**DeepSeek:** {r['answer']}")

# ===== Upload and Analyze Image =====
uploaded_file = st.file_uploader("Upload a P&ID Image", type=["png", "jpg", "jpeg"])
if uploaded_file:
    image = Image.open(uploaded_file)
    st.image(image, caption="P&ID Diagram", use_column_width=True)

    if st.button("?? Run Detection and Analysis"):
        detections, annotations, class_names = detect_symbols_and_lines(image)
        graph = build_graph(image, detections, annotations, class_names)

        st.info("Running anomaly detection on the graph...")

        fig, feature_map, red_nodes, central_node, scores, G = run_gnn()

        st.session_state.G = G
        st.session_state.feature_map = feature_map
        st.session_state.scores = scores
        st.session_state.fig = fig

        st.pyplot(fig)

        actions = generate_agent_actions(fig, feature_map, red_nodes, central_node, scores)
        st.session_state.actions = actions

        for action in actions:
            st.write(action)

# ===== DeepSeek Local Model Setup =====
@st.cache_resource
def load_deepseek_model():
    model_name = "deepseek-ai/deepseek-coder-1.3b-instruct"  # lightweight option
    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype=torch.float16,
        device_map="cuda",
        trust_remote_code=True
    )
    return model, tokenizer

# ===== DeepSeek Q&A =====
st.subheader("?? Ask Questions About the Graph (DeepSeek Local)")
user_query = st.chat_input("Ask a question about the graph...")

if user_query:
    G = st.session_state.get("G")
    feature_map = st.session_state.get("feature_map", {})
    scores = st.session_state.get("scores", {})

    if G and feature_map and scores:
        graph_data = {
            "nodes": [
                {
                    "id": str(i),
                    "label": feature_map.get(i, f"Node {i}"),
                    "score": float(scores.get(i, 0.0))
                }
                for i in G.nodes()
            ],
            "edges": [
                {"source": str(u), "target": str(v)}
                for u, v in G.edges()
            ]
        }

        prompt = (
            "You are an expert graph analyst. Analyze this P&ID graph and answer the question.\n\n"
            "### Graph Data:\n"
            f"{json.dumps(graph_data, indent=2)}\n\n"
            "### Question:\n"
            f"{user_query}\n\n"
            "### Answer:\n"
        )

        try:
            with st.spinner("Thinking (via DeepSeek Local)..."):
                model, tokenizer = load_deepseek_model()

                inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
                outputs = model.generate(
                    **inputs,
                    max_new_tokens=128,
                    temperature=0.7,
                    do_sample=True
                )

                answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
                answer = answer[len(prompt):].strip()

                st.session_state.deepseek_responses.append({
                    "query": user_query,
                    "answer": answer
                })

                st.markdown(f"**DeepSeek:** {answer}")

        except Exception as e:
            st.error(f"DeepSeek error: {e}")
            st.error("Ensure enough GPU memory (8GB+ recommended).")
    else:
        st.warning("?? Please analyze a diagram first to generate a graph.")