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# app.py - FactoryRAG: Q&A over Sensor Logs (Streamlit + Hugging Face)
import streamlit as st
import pandas as pd
import numpy as np
from sentence_transformers import SentenceTransformer
from transformers import pipeline
# Set page config
st.set_page_config(page_title="FactoryRAG - Sensor Logs", layout="wide")
st.title("🏭 FactoryRAG: Human-Centric AI for Sensor Log Analysis")
# Load models
EMBED_MODEL = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
GEN_MODEL = pipeline('text2text-generation', model='google/flan-t5-base')
# Load CSV sensor log file
@st.cache_data
def load_logs():
df = pd.read_csv("01-04T184148_000_mode1.csv")
return df
# Convert log rows to natural language chunks
def convert_to_chunks(df):
chunks = []
for idx, row in df.iterrows():
sentence = f"Log entry {idx}: " + ", ".join([f"{col}: {row[col]:.2f}" for col in df.columns])
chunks.append(sentence)
return chunks
# Load and embed logs
df = load_logs()
st.write("📊 Sensor Data Snapshot:", df.head())
if 'chunks' not in st.session_state:
st.session_state.chunks = convert_to_chunks(df)
st.session_state.embeddings = EMBED_MODEL.encode(st.session_state.chunks)
# Ask a question
query = st.text_input("🔍 Ask something about the sensor logs:")
if query:
query_vec = EMBED_MODEL.encode([query])[0]
scores = np.dot(st.session_state.embeddings, query_vec)
top_idxs = np.argsort(scores)[-3:][::-1]
context = "\n".join([st.session_state.chunks[i] for i in top_idxs])
prompt = f"Answer based on the following logs:\n{context}\n\nQuestion: {query}"
response = GEN_MODEL(prompt, max_length=256)[0]['generated_text']
st.subheader("🤖 FactoryGPT Answer")
st.write(response)
st.markdown("### 🧑🏭 Human Feedback")
st.radio("Is this answer acceptable?", ["Approve", "Correct", "Escalate"], horizontal=True)
with st.expander("📄 Retrieved Log Context"):
st.code(context)
elif len(df) == 0:
st.warning("⚠️ No data loaded. Please check your CSV file.")
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