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app.py

@@ -1,3 +1,5 @@
+# import requests for interacting with backend
+import requests
 
 # import streamlit library for IO
 import streamlit as st
@@ -5,13 +7,43 @@ import streamlit as st
 # import pandas
 import pandas as pd
 
-# library to download fine from Hugging Face
-from huggingface_hub import hf_hub_download
-
-# library to load model
-import joblib
+# 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"
+                            )
 
+    st.markdown('</div>', unsafe_allow_html=True)
 
+    return usre_input
 
 # ---------------------------------------------------------
 # PAGE CONFIG
@@ -22,15 +54,14 @@ st.set_page_config(
 )
 
 
-# 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)
+st.markdown("""
+<style>
+.block-container {
+    padding-top: 0.75rem;
+    padding-bottom: 0.75rem;
+}
+</style>
+""", unsafe_allow_html=True)
 
 
 # ---------------------------------------------------------
@@ -47,55 +78,69 @@ st.write("Fill in the details below and click **Predict** to see if the Engine n
 # ====================================
 st.subheader ("Engine Parameters")
 
-rpm               = st.number_input(
-    "Engine RPM (50.0 to 2500.0)",
-    min_value=50,
-    max_value=2500,
-    value=735,
-    step=10
+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"
 )
 
-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"
+
+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"
 )
 
-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"
+
+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"
 )
 
-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"
+
+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"
 )
 
-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
+
+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"
 )
 
-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
+
+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"
 )
 
 
@@ -111,13 +156,11 @@ 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(lub_oil_temp),
+        'Coolant_temp'            : float(coolant_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
@@ -127,16 +170,34 @@ if st.button("Check fo Maintenance"):
         ## get result as json
         result = response.json ()
 
-        ## Get Sales Prediction Value
-        prediction_from_backend = result.get ("NeedsMaintenance")  # Extract only the value
-
-        # generate output string
-        if prediction_from_backend == 1:
-            resultstr = "Engine **likely** needs maintenance."
+        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:
-            resultstr = "Engine does not need any maintenance"
 
-        st.success(resultstr)
+            error_str = result.get ("message")
+
+            st.error(error_str)
+
+    elif response.status_code == 400 or response.status_code == 500:  # known errors
+        
+        ## get result as json
+        result = response.json ()
+
+        error_str = result.get ("message")
+        st.error (f"Error processing request- Status Code : {response.status_code}, error : {error_str}")
         
     else:
         st.error (f"Error processing request- Status Code : {response.status_code}")
@@ -144,5 +205,3 @@ if st.button("Check fo Maintenance"):
     # Show the etails of data frame prepared from user input
     st.subheader("📦 Input Data Summary")
     st.dataframe (input_df)
-
-