Upload 4 files

app.py

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+import streamlit as st
+import pandas as pd
+import requests
+import joblib
+import json
+import os
+import numpy as np
+from sklearn.decomposition import PCA
+
+# Load local model components
+pca_path = "models/pca.pkl"
+local_model_path = "models/model.pkl"
+
+# Load models if they exist
+
+pca = joblib.load(pca_path) if os.path.exists(pca_path) else None
+local_model = joblib.load(local_model_path) if os.path.exists(local_model_path) else None
+
+# MLflow Server Endpoint
+MLFLOW_SERVER_URL = "http://127.0.0.1:8000/invocations"
+
+# Streamlit UI
+st.title("Imago AI Vomitoxin Prediction System")
+st.write("Upload a CSV file to predict vomitoxin levels in corn samples.")
+
+# Upload CSV File
+uploaded_file = st.file_uploader("Upload CSV File", type=["csv"])
+
+# Prediction Function
+def predict(data):
+    """Predict using MLflow server if available, else use local model."""
+    
+    # Try MLflow first
+    try:
+        st.write("Checking MLflow Server...")
+        
+        # Convert data to JSON format for MLflow
+        json_data = json.dumps({"inputs": df.to_numpy().tolist()})  #  Convert to NumPy first
+
+        
+        # Send request to MLflow
+        response = requests.post(MLFLOW_SERVER_URL, json={"inputs": json_data})
+        
+        # If successful, return MLflow prediction
+        if response.status_code == 200:
+            predictions = response.json()
+            st.success("Prediction done using MLflow Server!")
+            return np.array(predictions)
+        else:
+            st.warning("MLflow Server not responding. Falling back to local model.")
+    
+    except requests.exceptions.RequestException:
+        st.warning("MLflow Server is not running. Using local model.")
+
+    # If MLflow fails, use local model
+    if local_model and pca:
+        # Apply Scaling & PCA
+        # scaled_data = scaler.transform(data)
+        pca_data = pca.transform(data)
+        
+        # Predict with local model
+        predictions = local_model.predict(pca_data)
+        st.success("Prediction done using Local Model!")
+        return predictions
+    else:
+        st.error("No model available for prediction.")
+        return None
+
+# Process File & Predict
+if uploaded_file:
+    try:
+        # Read CSV
+        df = pd.read_csv(uploaded_file)
+        
+        # Ensure required columns exist
+        if "hsi_id" not in df.columns:
+            st.error("CSV file must contain `hsi_id` column.")
+        else:
+            # Extract Features (Drop hsi_id)
+            X = df.drop(columns=["hsi_id"])
+
+            # Make Predictions
+            predictions = predict(X)
+
+            if predictions is not None:
+                # Create output DataFrame
+                df["vomitoxin_ppb"] = predictions
+                
+                # Mark Harmful Cases (vomitoxin > 1000)
+                df["Harmful"] = df["vomitoxin_ppb"].apply(lambda x: "⚠️ Yes" if x > 1000 else "✅ No")
+                
+                # Display DataFrame
+                st.subheader("Prediction Results")
+                st.write(df[["hsi_id", "vomitoxin_ppb", "Harmful"]])
+
+                # Highlight Harmful Cases
+                st.subheader("Harmful Cases")
+                harmful_df = df[df["vomitoxin_ppb"] > 1000]
+                st.write(harmful_df[["hsi_id", "vomitoxin_ppb"]])
+
+    except Exception as e:
+        st.error(f"Error processing file: {e}")

models/model.pkl

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+oid sha256:1364d694a2903dbe637c0496b055c29eef75866bf2695f6e14654f955027ebd7
+size 183743

models/pca.pkl

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+oid sha256:d1431b403e519ce7e0c8004d39e170470e12be008c2ee840d8c52d16b55fdb4a
+size 17038

models/tsne.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d610993cd9dfe855a877a1ca6a3e219844e415a1e4af3c0afb8927cad11cd040
+size 9679