Update app.py

app.py

@@ -1,61 +1,56 @@
-# 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")
+# Page setup
+st.set_page_config(page_title="FactoryRAG - Upload 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
+# File uploader
+uploaded_file = st.sidebar.file_uploader("📂 Upload your sensor CSV log file", type=["csv"])
 
-# Load and embed logs
-df = load_logs()
-st.write("📊 Sensor Data Snapshot:", df.head())
+if uploaded_file:
+    df = pd.read_csv(uploaded_file)
+    st.success("✅ File uploaded and loaded!")
+    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)
+    # Convert to 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
 
-# Ask a question
-query = st.text_input("🔍 Ask something about the sensor logs:")
+    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)
 
-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])
+    # User query
+    query = st.text_input("🔍 Ask something about the sensor logs:")
 
-    prompt = f"Answer based on the following logs:\n{context}\n\nQuestion: {query}"
-    response = GEN_MODEL(prompt, max_length=256)[0]['generated_text']
+    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])
 
-    st.subheader("🤖 FactoryGPT Answer")
-    st.write(response)
+        prompt = f"Answer based on the following logs:\n{context}\n\nQuestion: {query}"
+        response = GEN_MODEL(prompt, max_length=256)[0]['generated_text']
 
-    st.markdown("### 🧑‍🏭 Human Feedback")
-    st.radio("Is this answer acceptable?", ["Approve", "Correct", "Escalate"], horizontal=True)
+        st.subheader("🤖 FactoryGPT Answer")
+        st.write(response)
 
-    with st.expander("📄 Retrieved Log Context"):
-        st.code(context)
+        st.markdown("### 🧑‍🏭 Human Feedback")
+        st.radio("Is this answer acceptable?", ["Approve", "Correct", "Escalate"], horizontal=True)
 
-elif len(df) == 0:
-    st.warning("⚠️ No data loaded. Please check your CSV file.")
+        with st.expander("📄 Retrieved Log Context"):
+            st.code(context)
+else:
+    st.info("👈 Please upload your sensor log file (CSV) to begin.")