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README.md

@@ -1,19 +1,2 @@
----
-title: Agentic Predictive Maintenance
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: Streamlit template space
----
-
-# Welcome to Streamlit!
-
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
-
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+# Agentic_predective_maintenance
+# Agentic_predective_maintenance

app.py

@@ -0,0 +1,171 @@
+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 openai
+import os
+import numpy as np
+
+# Ensure models and datasets are available
+from download_models import download_all
+
+# Run only if critical file is missing
+if not os.path.exists("P&ID-Symbols-3/train/_annotations.coco.json"):
+    with st.spinner("Downloading required files (models & datasets)..."):
+        download_all()
+
+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 variables
+if "G" not in st.session_state:
+    st.session_state.G = None
+if "feature_map" not in st.session_state:
+    st.session_state.feature_map = {}
+if "scores" not in st.session_state:
+    st.session_state.scores = {}
+
+#uploaded_file = st.file_uploader("Upload a P&ID Image", type=["png", "jpg", "jpeg"])
+#if uploaded_file:
+# === User can choose from downloaded dataset OR upload their own ===
+st.subheader("Upload or Select a P&ID Drawing")
+
+local_dataset_dir = "P&ID-Symbols-3/P&ID-Symbols-3/test"
+image_files = []
+if os.path.exists(local_dataset_dir):
+    image_files = [f for f in os.listdir(local_dataset_dir) if f.lower().endswith((".png", ".jpg", ".jpeg"))]
+else:
+    st.warning(f"Dataset folder not found: {local_dataset_dir}. Please run download_models.py to download it.")
+
+selected_image = st.selectbox("Select a sample from P&ID-Symbols-3:", ["-- Select an example --"] + image_files)
+uploaded_file = st.file_uploader("...Or upload your own P&ID image", type=["png", "jpg", "jpeg"])
+
+image = None
+image_source = ""
+
+if selected_image and selected_image != "-- Select an example --":
+    image_path = os.path.join(local_dataset_dir, selected_image)
+    image = Image.open(image_path)
+    image_source = f"Sample from dataset: {selected_image}"
+elif uploaded_file:
+    image = Image.open(uploaded_file)
+    image_source = f"Uploaded: {uploaded_file.name}"
+
+if image:
+    st.image(image, caption=image_source, use_column_width=True)
+
+    #image = Image.open(uploaded_file)
+    #st.image(image, caption="P&ID Diagram", use_column_width=True)
+
+    if st.button(" Run Detection and Analysis"):
+        # Uncomment these when detection and graph building pipelines are ready
+        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 (simulated for now)...")
+
+        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.pyplot(fig)
+
+        actions = generate_agent_actions(fig, feature_map, red_nodes, central_node, scores)
+        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 version
+    # model_name = "deepseek-ai/deepseek-llm-7b"  # Larger but more capable
+    
+    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+    '''model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.float16,
+        device_map="auto",
+        trust_remote_code=True'''
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.float16,
+        device_map="cpu",
+        #load_in_4bit=True,  # 4-bit quantization
+        trust_remote_code=True
+    )
+    return model, tokenizer
+
+# === Q&A Interface ===
+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 is not None and feature_map and len(scores) > 0:
+        graph_data = {
+            "nodes": [
+                {
+                    "id": str(i),
+                    "label": feature_map[i] if i < len(feature_map) else f"Node {i}",
+                    "score": float(scores[i]) if i < len(scores) else 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)..."):
+                # Load model (cached after first run)
+                model, tokenizer = load_deepseek_model()
+                
+                # Generate response
+                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)
+                # Remove the prompt from the answer
+                answer = answer[len(prompt):].strip()
+                
+                st.markdown(f"**DeepSeek:** {answer}")
+                
+        except Exception as e:
+            st.error(f"DeepSeek error: {e}")
+            st.error("Make sure you have enough GPU memory (8GB+ recommended for 7B model)")
+    else:
+        st.warning("Graph or scores are not ready yet.")

requirements.txt

@@ -1,3 +1,16 @@
-altair
-pandas
-streamlit
+streamlit
+networkx
+matplotlib
+Pillow
+supervision
+opencv-python-headless
+pydantic
+peft
+pycocotools
+numpy
+gdown
+scikit-learn
+torch-geometric
+torch>=2.0.0
+torchvision>=0.15.0
+transformers>=4.28.0