Update app.py

app.py

@@ -1,88 +1,36 @@
 # app.py
 
 import streamlit as st
-import pandas as pd
-import requests
-from simple_salesforce import Salesforce
+from modules.salesforce_connector import connect_salesforce, fetch_pole_data
+from modules.huggingface_inference import predict_alerts
+from modules.data_view import show_raw_data, show_predictions, show_status
 
-# ----------------------
-# CONFIG: Salesforce + Hugging Face
-# ----------------------
+# ---------------------- CONFIG ----------------------
 SF_USERNAME = "[email protected]"
 SF_PASSWORD = "Vedavathi@04"
 SF_SECURITY_TOKEN = "jqe4His8AcuFJucZz5NBHfGU"
-SF_DOMAIN = "login"  # or "test" for sandbox
-
-HF_API_URL = "https://api-inference.huggingface.co/models/your-username/your-model"
+HF_API_URL = "https://api-inference.huggingface.co/models/your-model"
 HF_API_TOKEN = "hf_your_token"
 
-# ----------------------
-# Connect to Salesforce
-# ----------------------
-@st.cache_resource
-def connect_salesforce():
-    sf = Salesforce(username=SF_USERNAME, password=SF_PASSWORD, security_token=SF_SECURITY_TOKEN, domain=SF_DOMAIN)
-    return sf
-
-# ----------------------
-# Get Pole Data from Salesforce
-# ----------------------
-def fetch_pole_data(sf):
-    query = """
-    SELECT Name, Solar_Gen__c, Wind_Gen__c, Tilt__c, Vibration__c, Camera_Status__c 
-    FROM Smart_Pole__c
-    LIMIT 50
-    """
-    records = sf.query_all(query)['records']
-    df = pd.DataFrame(records).drop(columns=['attributes'])
-    return df
-
-# ----------------------
-# Send Data to Hugging Face Model
-# ----------------------
-def get_hf_predictions(df):
-    headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
-    preds = []
-
-    for _, row in df.iterrows():
-        input_data = {
-            "solar": row["Solar_Gen__c"],
-            "wind": row["Wind_Gen__c"],
-            "tilt": row["Tilt__c"],
-            "vibration": row["Vibration__c"],
-            "camera": row["Camera_Status__c"]
-        }
-        response = requests.post(HF_API_URL, headers=headers, json={"inputs": input_data})
-        if response.status_code == 200:
-            result = response.json()
-            preds.append(result[0]['label'] if isinstance(result, list) else result.get("label", "Unknown"))
-        else:
-            preds.append("Error")
-
-    df["Alert_Prediction"] = preds
-    return df
-
-# ----------------------
-# Streamlit App
-# ----------------------
+# ---------------------- MAIN ----------------------
 def main():
-    st.title("🔌 Salesforce ↔ Hugging Face Smart Pole Integration")
+    st.title("📡 Hugging Face + Salesforce Smart Pole Monitor")
 
-    sf = connect_salesforce()
+    sf = connect_salesforce(SF_USERNAME, SF_PASSWORD, SF_SECURITY_TOKEN)
     df = fetch_pole_data(sf)
 
     if not df.empty:
-        st.subheader("📥 Raw Pole Data from Salesforce")
-        st.dataframe(df)
-
-        st.subheader("🤖 Running Hugging Face AI Predictions...")
-        df = get_hf_predictions(df)
-        
-        st.success("✅ Predictions Complete")
-        st.subheader("📊 Results with Predictions")
-        st.dataframe(df)
+        show_raw_data(df)
+        df = predict_alerts(df, HF_API_URL, HF_API_TOKEN)
+        show_status()
+        show_predictions(df)
     else:
         st.warning("No data fetched from Salesforce.")
 
+if __name__ == "__main__":
+    main()
+
+
+