Added some files

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+test.csv filter=lfs diff=lfs merge=lfs -text
+train.csv filter=lfs diff=lfs merge=lfs -text

app2.py

@@ -0,0 +1,148 @@
+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.")

download_models.py

@@ -0,0 +1,34 @@
+# download_models.py
+import os
+import gdown
+import zipfile
+
+def download_all():
+    # Make necessary directories
+    os.makedirs("output", exist_ok=True)
+    os.makedirs("P&ID-Symbols-3/train", exist_ok=True)
+
+    # File ID mapping
+    file_map = {
+        "output/checkpoint0009.pth": "1WfsV8ZuDwlgvBsompA8jdpG_XqNGsWGT",
+        "output/checkpoint_best_total.pth": "1UpVLNeKDrocU4UgBe361IwvHnpFkKues",
+        "rf-detr-base.pth": "1L7mU1jyQNLxJcex3jTd5wccdtUrhgQ2c",
+        "P&ID-Symbols-3/train/_annotations.coco.json": "159ArLMxS1PZ4zD6CAf7bbJfEvLToFA-C",
+        "P&ID-Symbols-3.zip": "1HfjxUN7j92XCciKWB4_ZkC_W6UUKWtDd"
+    }
+
+    # Download each file
+    for path, file_id in file_map.items():
+        if not os.path.exists(path):
+            print(f"\nDownloading {path} ...")
+            gdown.download(id=file_id, output=path, quiet=False)
+
+    # Unzip image folder if not already extracted
+    zip_path = "P&ID-Symbols-3.zip"
+    if os.path.exists(zip_path):
+        print(f"\nExtracting {zip_path} ...")
+        with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+            zip_ref.extractall(".")
+        print("? Extraction complete.")
+
+    print("? All files downloaded and set up.")

test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c25a116e1561d9a3a810013ee35de6cfacf0468fb1372c1df25636b01588ff7
+size 15724537

train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cbca99010e6d52117a7470bafc6eddf764028c7e7c2dea8b95810278734930f0
+size 16726262