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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}")
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