Update app.py

app.py

@@ -1,97 +1,35 @@
 # app.py
 
-import os
 import streamlit as st
-import pandas as pd
-import requests
-from simple_salesforce import Salesforce
+from salesforce_integration import fetch_poles
+import plotly.express as px
+from modules.visuals import display_dashboard, display_charts
 
-# -----------------------------
-# CONFIG — Use env vars or hardcode for testing
-# -----------------------------
-SF_USERNAME = os.getenv("SF_USERNAME", "[email protected]")
-SF_PASSWORD = os.getenv("SF_PASSWORD", "Vedavathi@04")
-SF_SECURITY_TOKEN = os.getenv("SF_SECURITY_TOKEN", "jqe4His8AcuFJucZz5NBHfGU")
-SF_DOMAIN = os.getenv("SF_DOMAIN", "login")  # use "test" for sandbox
 
-HF_API_URL = os.getenv("HF_API_URL", "https://api-inference.huggingface.co/models/your-model")
-HF_API_TOKEN = os.getenv("HF_API_TOKEN", "hf_your_token")
 
-# -----------------------------
-# Connect to Salesforce
-# -----------------------------
-def connect_salesforce():
-    return Salesforce(username=SF_USERNAME, password=SF_PASSWORD, security_token=SF_SECURITY_TOKEN, domain=SF_DOMAIN)
+st.title("📡 VIEP Smart Poles Dashboard (Salesforce Data)")
 
-# -----------------------------
-# Fetch Smart Pole Data
-# -----------------------------
-def fetch_pole_data(sf):
-    query = """
-    SELECT Name, Solar_Generation__c, Wind_Generation__c, Tilt__c, Vibration__c, Camera_Status__c 
-    FROM Vedavathi_Smart_Pole__c
-    LIMIT 50
-    """
-    records = sf.query_all(query)['records']
-    df = pd.DataFrame(records).drop(columns=['attributes'])
-    return df
+# Fetch Data
+df = fetch_poles()
 
-# -----------------------------
-# Predict with Hugging Face
-# -----------------------------
-def predict_with_huggingface(df):
-    headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
-    predictions = []
+# Display Table
+st.subheader("📋 Pole Table")
+st.dataframe(df)
 
-    for _, row in df.iterrows():
-        payload = {
-            "inputs": {
-                "solar": row.get("Solar_Generation__c", 0),
-                "wind": row.get("Wind_Generation__c", 0),
-                "tilt": row.get("Tilt__c", 0),
-                "vibration": row.get("Vibration__c", 0),
-                "camera": row.get("Camera_Status__c", "Online")
-            }
-        }
-        try:
-            response = requests.post(HF_API_URL, headers=headers, json=payload, timeout=10)
-            response.raise_for_status()
-            result = response.json()
-            label = result[0]['label'] if isinstance(result, list) else result.get("label", "Unknown")
-        except Exception as e:
-            label = f"Error: {e}"
-        predictions.append(label)
+# Charts
+st.subheader("⚙ Energy Generation (Solar vs Wind)")
+st.plotly_chart(px.bar(df, x="Name", y=["Solar_Generation__c", "Wind_Generation__c"], barmode="group")) 
+st.subheader("🎥 Camera Status Overview")
 
-    df["Predicted Alert Level"] = predictions
-    return df
+camera_counts = df["Camera_Status__c"].value_counts().reset_index()
+camera_counts.columns = ["Status", "Count"]
 
-# -----------------------------
-# Streamlit App
-# -----------------------------
-def main():
-    st.set_page_config(layout="wide")
-    st.title("📡 Vedavathi Smart Pole Anomaly Detection")
-
-    try:
-        sf = connect_salesforce()
-        df = fetch_pole_data(sf)
-
-        if df.empty:
-            st.warning("No records found in Salesforce.")
-            return
-
-        st.subheader("📋 Raw Pole Data")
-        st.dataframe(df)
-
-        st.subheader("🤖 AI Predictions via Hugging Face")
-        df_pred = predict_with_huggingface(df)
-        st.success("✅ Predictions completed")
-
-        st.subheader("📊 Final Table with Alerts")
-        st.dataframe(df_pred)
-
-    except Exception as e:
-        st.error(f"❌ Error: {e}")
-
-if __name__ == "__main__":
-    main()
+st.plotly_chart(px.bar(
+    camera_counts,
+    x="Count",
+    y="Status",
+    orientation="h",
+    color="Status",
+    title="Camera Status Distribution",
+    text="Count"
+))