Upload folder using huggingface_hub

app.py

@@ -1,5 +1,3 @@
-# import requests for interacting with backend
-import requests
 
 # import streamlit library for IO
 import streamlit as st
@@ -7,43 +5,13 @@ import streamlit as st
 # import pandas
 import pandas as pd
 
-# define functiom which can provide formatted input with appropriate label and input text
-# this will help in p[roducing consistent representation
-def formatted_number_input(title, hint, **kwargs):
-    st.markdown(f"**{title}**")
-    st.caption(hint)
-    return st.number_input("", **kwargs)
-
-def formatted_number_input2(title, hint, minval, maxval, defvalue, steps, valformat="%.6f"):
-
-    st.markdown('<div style="margin-bottom:4px;">', unsafe_allow_html=True)
-    
-    col1, col2 = st.columns([3, 1], vertical_alignment="center")
-    
-    with col1:
-        st.markdown(
-            f"""
-            <div style="line-height:1.0">
-                <strong>{title}</strong><br>
-                <span style="font-size:1.20em; color:gray;">{hint}</span>
-            </div>
-            """,
-            unsafe_allow_html=True
-        )
-    
-    with col2:
-        usre_input = st.number_input("", 
-                            min_value=minval,
-                            max_value=maxval,
-                            value=defvalue,
-                            step=steps,
-                            format=valformat,
-                            label_visibility="collapsed"
-                            )
+# library to download fine from Hugging Face
+from huggingface_hub import hf_hub_download
+
+# library to load model
+import joblib
 
-    st.markdown('</div>', unsafe_allow_html=True)
 
-    return usre_input
 
 # ---------------------------------------------------------
 # PAGE CONFIG
@@ -54,14 +22,15 @@ st.set_page_config(
 )
 
 
-st.markdown("""
-<style>
-.block-container {
-    padding-top: 0.75rem;
-    padding-bottom: 0.75rem;
-}
-</style>
-""", unsafe_allow_html=True)
+# Download and load the model
+model_path = hf_hub_download(
+    repo_id="harishsohani/AIMLProjectTest",
+    #### Final name will be ####
+    # hf_repo_id = "harishsohani/AIMLPredictMaintenance"
+    repo_type="space",
+    filename="best_eng_fail_pred_model.joblib"
+    )
+model = joblib.load(model_path)
 
 
 # ---------------------------------------------------------
@@ -78,69 +47,55 @@ st.write("Fill in the details below and click **Predict** to see if the Engine n
 # ====================================
 st.subheader ("Engine Parameters")
 
-rpm = formatted_number_input2(
-    "Lubricating oil pressure in kilopascals (kPa)",
-    "50 to 2500",
-    minval=50.0,
-    maxval=2500.0,
-    defvalue=735.0,
-    steps=10.0,
-    valformat="%.2f"
+rpm               = st.number_input(
+    "Engine RPM (50.0 to 2500.0)",
+    min_value=50,
+    max_value=2500,
+    value=735,
+    step=10
 )
 
-
-oil_pressure = formatted_number_input2(
-    "Lubricating oil pressure in kilopascals (kPa)",
-    "0.001 to 10.0",
-    minval=0.001,
-    maxval=10.0,
-    defvalue=3.300000,
-    steps=0.001,
-    valformat="%.6f"
+oil_pressure      = st.number_input(
+    "Lubricating oil pressure in kilopascals (kPa) (0.001 to 10.0)",
+    min_value=0.001,
+    max_value=10.0,
+    value=3.300,
+    step=0.001,
+    format="%.3f"
 )
 
-
-fuel_pressure = formatted_number_input2(
-    "Fuel Pressure in kilopascals (kPa)",
-    "0.01 to 25.0",
-    minval=0.01,
-    maxval=25.0,
-    defvalue=6.500000,
-    steps=0.01,
-    valformat="%.6f"
+fuel_pressure     = st.number_input(
+    "Fuel Pressure in kilopascals (kPa) (0.01 to 25.0)",
+    min_value=0.01,
+    max_value=25.0,
+    value=6.50,
+    step=0.01,
+    format="%.2f"
 )
 
-
-coolant_pressure = formatted_number_input2(
-    "Coolant Pressure in kilopascals (kPa)",
-    "0.01 to 10.0",
-    minval=0.01,
-    maxval=10.0,
-    defvalue=2.250000,
-    steps=0.10,
-    valformat="%.6f"
+coolant_pressure  = st.number_input(
+    "Coolant Pressure in kilopascals (kPa) (0.01 to 10.0)",
+    min_value=0.01,
+    max_value=10.0,
+    value=2.25,
+    step=0.10,
+    format="%.2f"
 )
 
-
-lub_oil_temp = formatted_number_input2(
-    "Lubricating oil Temperature in degrees Celsius (°C)",
-    "50.0 to 100.0",
-    minval=50.0,
-    maxval=100.0,
-    defvalue=75.0,
-    steps=0.1,
-    valformat="%.6f"
+lub_oil_temp      = st.number_input(
+    "Lubricating oil Temperature in degrees Celsius (°C) (50.0 to 100.0)",
+    min_value=50.0,
+    max_value=100.0,
+    value=75.0,
+    step=0.1
 )
 
-
-coolant_temp = formatted_number_input2(
-    "Coolant Temperature in degrees Celsius (°C)",
-    "50.0 to 200.0",
-    minval=50.0,
-    maxval=200.0,
-    defvalue=75.000000,
-    steps=0.1,
-    valformat="%.6f"
+coolant_temp      = st.number_input(
+    "Coolant Temperature in degrees Celsius (°C)  (50.0 to 200.0)",
+    min_value=50.0,
+    max_value=200.0,
+    value=75.0,
+    step=1.0
 )
 
 
@@ -156,11 +111,13 @@ if st.button("Check fo Maintenance"):
         'Fuel_pressure'           : float(fuel_pressure),
         'Coolant_pressure'        : float(coolant_pressure),
         'lub_oil_temp'            : float(lub_oil_temp),
-        'Coolant_temp'            : float(coolant_temp),
+        'Coolant_temp'            : float(lub_oil_temp),
     }
 
     input_df = pd.DataFrame([input_data])
 
+    prediction = model.predict(input_df)[0]
+
     response = requests.post (
         "https://harishsohani-AIMLProjectTestBackEnd.hf.space/v1/EngPredMaintenance",
         json=input_data
@@ -170,34 +127,16 @@ if st.button("Check fo Maintenance"):
         ## get result as json
         result = response.json ()
 
-        resp_status = result.get ("status")
-
-        if resp_status == "success":
-            
-            ## Get Sales Prediction Value
-            prediction_from_backend = result.get ("prediction")  # Extract only the value
-    
-            # generate output string
-            if prediction_from_backend == 1:
-                resultstr = "Engine **likely** needs maintenance."
-            else:
-                resultstr = "Engine does not need any maintenance"
-    
-            st.success(resultstr)
-            
-        else:
-
-            error_str = result.get ("message")
+        ## Get Sales Prediction Value
+        prediction_from_backend = result.get ("NeedsMaintenance")  # Extract only the value
 
-            st.error(error_str)
-
-    elif response.status_code == 400 or response.status_code == 500:  # known errors
-        
-        ## get result as json
-        result = response.json ()
+        # generate output string
+        if prediction_from_backend == 1:
+            resultstr = "Engine **likely** needs maintenance."
+        else:
+            resultstr = "Engine does not need any maintenance"
 
-        error_str = result.get ("message")
-        st.error (f"Error processing request- Status Code : {response.status_code}, error : {error_str}")
+        st.success(resultstr)
         
     else:
         st.error (f"Error processing request- Status Code : {response.status_code}")