Create app.py

app.py

@@ -0,0 +1,95 @@
+# app.py
+
+import streamlit as st
+import pandas as pd
+import requests
+from simple_salesforce import Salesforce
+
+# -----------------------------
+# CONFIG — Fill with your creds
+# -----------------------------
+SF_USERNAME = "[email protected]"
+SF_PASSWORD = "Vedavathi@04"
+SF_SECURITY_TOKEN = "jqe4His8AcuFJucZz5NBHfGU"
+SF_DOMAIN = "login"  # or "test" if you're using a sandbox
+
+HF_API_URL = "https://api-inference.huggingface.co/models/your-model"
+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)
+
+# -----------------------------
+# 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 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
+# -----------------------------
+def predict_with_huggingface(df):
+    headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
+    predictions = []
+
+    for _, row in df.iterrows():
+        payload = {
+            "inputs": {
+                "solar": row.get("Solar_Generation__c"),
+                "wind": row.get("Wind_Generation__c"),
+                "tilt": row.get("Tilt__c"),
+                "vibration": row.get("Vibration__c"),
+                "camera": row.get("Camera_Status__c")
+            }
+        }
+        response = requests.post(HF_API_URL, headers=headers, json=payload)
+        if response.status_code == 200:
+            result = response.json()
+            label = result[0]['label'] if isinstance(result, list) else result.get("label", "Unknown")
+        else:
+            label = "Error"
+        predictions.append(label)
+    
+    df["Predicted Alert Level"] = predictions
+    return df
+
+# -----------------------------
+# Streamlit App UI
+# -----------------------------
+def main():
+    st.set_page_config(layout="wide")
+    st.title("📡 Salesforce → Hugging Face Smart Pole Anomaly Detector")
+
+    try:
+        sf = connect_salesforce()
+        df = fetch_pole_data(sf)
+
+        if df.empty:
+            st.warning("No records found.")
+            return
+
+        st.subheader("📋 Raw Pole Data from Salesforce")
+        st.dataframe(df)
+
+        st.subheader("🤖 Running Hugging Face Predictions...")
+        df_with_preds = predict_with_huggingface(df)
+        st.success("Predictions complete.")
+
+        st.subheader("📊 Results with AI-Predicted Alerts")
+        st.dataframe(df_with_preds)
+
+    except Exception as e:
+        st.error(f"Error: {e}")
+
+if __name__ == "__main__":
+    main()