Create app.py

app.py

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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.")