feat: web application multipage support
This commit is contained in:
@@ -0,0 +1,374 @@
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import plotly.express as px
|
||||
import streamlit as st
|
||||
|
||||
from core.utils.data_loader import OPTIMIZED_DTYPES
|
||||
from research.experiment.experiment_runner import ExperimentRunner
|
||||
from research.experiment.experiment_tracker import ExperimentTracker
|
||||
|
||||
|
||||
class Predictions:
|
||||
def __init__(
|
||||
self, config, experiment_tracker: ExperimentTracker, experiment_runner: ExperimentRunner
|
||||
):
|
||||
self.config = config
|
||||
self.experiment_tracker = experiment_tracker
|
||||
self.experiment_runner = experiment_runner
|
||||
|
||||
def index(self):
|
||||
st.title("Predictions")
|
||||
|
||||
# Load available models
|
||||
experiments = self.experiment_tracker.list_experiments()
|
||||
completed_experiments = [
|
||||
e for e in experiments if e.status.value == "completed" and e.model_path
|
||||
]
|
||||
|
||||
if not completed_experiments:
|
||||
st.warning("No trained models available. Please run some experiments first.")
|
||||
return
|
||||
|
||||
# Model selection
|
||||
model_options = {
|
||||
f"{exp.config.name} (Acc: {exp.test_metrics.get('accuracy', 0):.3f})": exp
|
||||
for exp in completed_experiments
|
||||
if exp.test_metrics
|
||||
}
|
||||
|
||||
selected_model_name = st.selectbox("Select Model", list(model_options.keys()))
|
||||
|
||||
if not selected_model_name:
|
||||
return
|
||||
|
||||
selected_experiment = model_options[selected_model_name]
|
||||
|
||||
# Prediction modes
|
||||
prediction_mode = st.radio(
|
||||
"Prediction Mode", ["Single Name", "Batch Upload", "Dataset Prediction"]
|
||||
)
|
||||
|
||||
if prediction_mode == "Single Name":
|
||||
self.show_single_prediction(selected_experiment)
|
||||
elif prediction_mode == "Batch Upload":
|
||||
self.show_batch_prediction(selected_experiment)
|
||||
elif prediction_mode == "Dataset Prediction":
|
||||
self.show_dataset_prediction(selected_experiment)
|
||||
|
||||
def show_single_prediction(self, experiment):
|
||||
"""Show single name prediction interface"""
|
||||
st.subheader("Single Name Prediction")
|
||||
|
||||
name_input = st.text_input("Enter a name:", placeholder="e.g., Jean Baptiste Mukendi")
|
||||
|
||||
if name_input and st.button("Predict Gender"):
|
||||
try:
|
||||
# Load the model
|
||||
model = self.experiment_runner.load_experiment_model(experiment.experiment_id)
|
||||
|
||||
if model is None:
|
||||
st.error("Failed to load model")
|
||||
return
|
||||
|
||||
# Create a DataFrame with the input
|
||||
input_df = self._prepare_single_input(name_input)
|
||||
|
||||
# Make prediction
|
||||
prediction = model.predict(input_df)[0]
|
||||
|
||||
# Get prediction probability if available
|
||||
confidence = self._get_prediction_confidence(model, input_df)
|
||||
|
||||
# Display results
|
||||
self._display_single_prediction_results(
|
||||
prediction, confidence, experiment, name_input
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
st.error(f"Error making prediction: {e}")
|
||||
|
||||
def _prepare_single_input(self, name_input: str) -> pd.DataFrame:
|
||||
"""Prepare single name input for prediction"""
|
||||
return pd.DataFrame(
|
||||
{
|
||||
"name": [name_input],
|
||||
"words": [len(name_input.split())],
|
||||
"length": [len(name_input.replace(" ", ""))],
|
||||
"province": ["unknown"], # Default values
|
||||
"identified_name": [None],
|
||||
"identified_surname": [None],
|
||||
"probable_native": [None],
|
||||
"probable_surname": [None],
|
||||
}
|
||||
)
|
||||
|
||||
def _get_prediction_confidence(self, model, input_df: pd.DataFrame) -> Optional[float]:
|
||||
"""Get prediction confidence if available"""
|
||||
try:
|
||||
probabilities = model.predict_proba(input_df)[0]
|
||||
return max(probabilities)
|
||||
except:
|
||||
return None
|
||||
|
||||
def _display_single_prediction_results(
|
||||
self, prediction: str, confidence: Optional[float], experiment, name_input: str
|
||||
):
|
||||
"""Display single prediction results"""
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
gender_label = "Female" if prediction == "f" else "Male"
|
||||
st.success(f"**Predicted Gender:** {gender_label}")
|
||||
|
||||
with col2:
|
||||
if confidence:
|
||||
st.metric("Confidence", f"{confidence:.2%}")
|
||||
|
||||
# Additional info
|
||||
st.info(f"Model used: {experiment.config.name}")
|
||||
st.info(f"Features used: {', '.join([f.value for f in experiment.config.features])}")
|
||||
|
||||
def show_batch_prediction(self, experiment):
|
||||
"""Show batch prediction interface"""
|
||||
st.subheader("Batch Prediction")
|
||||
|
||||
uploaded_file = st.file_uploader("Upload CSV file with names", type="csv")
|
||||
|
||||
if uploaded_file is not None:
|
||||
try:
|
||||
df = pd.read_csv(uploaded_file, dtype=OPTIMIZED_DTYPES)
|
||||
|
||||
st.write("**Uploaded Data Preview:**")
|
||||
st.dataframe(df.head(), use_container_width=True)
|
||||
|
||||
# Column selection
|
||||
df = self._prepare_batch_data(df)
|
||||
|
||||
if st.button("Run Batch Prediction"):
|
||||
self._run_batch_prediction(df, experiment)
|
||||
|
||||
except Exception as e:
|
||||
st.error(f"Error processing file: {e}")
|
||||
|
||||
def _prepare_batch_data(self, df: pd.DataFrame) -> pd.DataFrame:
|
||||
"""Prepare batch data for prediction"""
|
||||
# Column selection
|
||||
if "name" not in df.columns:
|
||||
name_column = st.selectbox("Select the name column:", df.columns)
|
||||
df = df.rename(columns={name_column: "name"})
|
||||
|
||||
# Add missing columns with defaults
|
||||
required_columns = [
|
||||
"words",
|
||||
"length",
|
||||
"province",
|
||||
"identified_name",
|
||||
"identified_surname",
|
||||
"probable_native",
|
||||
"probable_surname",
|
||||
]
|
||||
|
||||
for col in required_columns:
|
||||
if col not in df.columns:
|
||||
if col == "words":
|
||||
df[col] = df["name"].str.split().str.len()
|
||||
elif col == "length":
|
||||
df[col] = df["name"].str.replace(" ", "").str.len()
|
||||
else:
|
||||
df[col] = None
|
||||
|
||||
return df
|
||||
|
||||
def _run_batch_prediction(self, df: pd.DataFrame, experiment):
|
||||
"""Run batch prediction and display results"""
|
||||
with st.spinner("Making predictions..."):
|
||||
# Load model
|
||||
model = self.experiment_runner.load_experiment_model(experiment.experiment_id)
|
||||
|
||||
if model is None:
|
||||
st.error("Failed to load model")
|
||||
return
|
||||
|
||||
# Make predictions
|
||||
predictions = model.predict(df)
|
||||
df["predicted_gender"] = predictions
|
||||
df["gender_label"] = df["predicted_gender"].map({"f": "Female", "m": "Male"})
|
||||
|
||||
# Try to get probabilities
|
||||
try:
|
||||
probabilities = model.predict_proba(df)
|
||||
df["confidence"] = np.max(probabilities, axis=1)
|
||||
except:
|
||||
df["confidence"] = None
|
||||
|
||||
st.success("Predictions completed!")
|
||||
|
||||
# Show results
|
||||
self._display_batch_results(df)
|
||||
|
||||
def _display_batch_results(self, df: pd.DataFrame):
|
||||
"""Display batch prediction results"""
|
||||
result_columns = ["name", "gender_label", "predicted_gender"]
|
||||
if "confidence" in df.columns:
|
||||
result_columns.append("confidence")
|
||||
|
||||
st.dataframe(df[result_columns], use_container_width=True)
|
||||
|
||||
# Download results
|
||||
csv = df.to_csv(index=False)
|
||||
st.download_button(
|
||||
label="Download Predictions",
|
||||
data=csv,
|
||||
file_name=f"predictions_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
|
||||
mime="text/csv",
|
||||
)
|
||||
|
||||
# Summary statistics
|
||||
self._display_batch_summary(df)
|
||||
|
||||
def _display_batch_summary(self, df: pd.DataFrame):
|
||||
"""Display batch prediction summary"""
|
||||
st.subheader("Prediction Summary")
|
||||
gender_counts = df["gender_label"].value_counts()
|
||||
|
||||
col1, col2, col3 = st.columns(3)
|
||||
with col1:
|
||||
st.metric("Total Predictions", len(df))
|
||||
with col2:
|
||||
st.metric("Female", gender_counts.get("Female", 0))
|
||||
with col3:
|
||||
st.metric("Male", gender_counts.get("Male", 0))
|
||||
|
||||
# Gender distribution chart
|
||||
fig = px.pie(
|
||||
values=gender_counts.values,
|
||||
names=gender_counts.index,
|
||||
title="Predicted Gender Distribution",
|
||||
)
|
||||
st.plotly_chart(fig, use_container_width=True)
|
||||
|
||||
def show_dataset_prediction(self, experiment):
|
||||
"""Show dataset prediction interface"""
|
||||
st.subheader("Dataset Prediction")
|
||||
st.write("Apply the model to existing datasets")
|
||||
|
||||
# Dataset selection
|
||||
dataset_options = {
|
||||
"Featured Dataset": self.config.data.output_files["featured"],
|
||||
"Evaluation Dataset": self.config.data.output_files["evaluation"],
|
||||
}
|
||||
|
||||
selected_dataset = st.selectbox("Select Dataset", list(dataset_options.keys()))
|
||||
file_path = self.config.paths.get_data_path(dataset_options[selected_dataset])
|
||||
|
||||
if not file_path.exists():
|
||||
st.warning(f"Dataset not found: {file_path}")
|
||||
return
|
||||
|
||||
# Load and show dataset info
|
||||
df = self._load_dataset(str(file_path))
|
||||
if df.empty:
|
||||
return
|
||||
|
||||
st.write(f"Dataset contains {len(df):,} records")
|
||||
|
||||
# Prediction options
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
sample_size = st.number_input(
|
||||
"Sample size (0 = all data)", 0, len(df), min(1000, len(df))
|
||||
)
|
||||
|
||||
with col2:
|
||||
compare_with_actual = False
|
||||
if "sex" in df.columns:
|
||||
compare_with_actual = st.checkbox("Compare with actual labels", value=True)
|
||||
|
||||
if st.button("Run Dataset Prediction"):
|
||||
self._run_dataset_prediction(df, experiment, sample_size, compare_with_actual)
|
||||
|
||||
def _load_dataset(self, file_path: str) -> pd.DataFrame:
|
||||
"""Load dataset with error handling"""
|
||||
try:
|
||||
return pd.read_csv(file_path, dtype=OPTIMIZED_DTYPES)
|
||||
except Exception as e:
|
||||
st.error(f"Error loading dataset: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def _run_dataset_prediction(
|
||||
self, df: pd.DataFrame, experiment, sample_size: int, compare_with_actual: bool
|
||||
):
|
||||
"""Run dataset prediction and display results"""
|
||||
with st.spinner("Running predictions..."):
|
||||
# Sample data if requested
|
||||
if sample_size > 0:
|
||||
df_sample = df.sample(n=sample_size, random_state=42)
|
||||
else:
|
||||
df_sample = df
|
||||
|
||||
# Load model and make predictions
|
||||
model = self.experiment_runner.load_experiment_model(experiment.experiment_id)
|
||||
|
||||
if model is None:
|
||||
st.error("Failed to load model")
|
||||
return
|
||||
|
||||
predictions = model.predict(df_sample)
|
||||
df_sample["predicted_gender"] = predictions
|
||||
|
||||
# Show results
|
||||
if compare_with_actual and "sex" in df_sample.columns:
|
||||
self._display_dataset_comparison(df_sample)
|
||||
else:
|
||||
self._display_dataset_predictions(df_sample)
|
||||
|
||||
def _display_dataset_comparison(self, df_sample: pd.DataFrame):
|
||||
"""Display dataset predictions with actual comparison"""
|
||||
# Calculate accuracy
|
||||
accuracy = (df_sample["sex"] == df_sample["predicted_gender"]).mean()
|
||||
st.metric("Accuracy on Selected Data", f"{accuracy:.4f}")
|
||||
|
||||
# Confusion matrix
|
||||
from sklearn.metrics import confusion_matrix
|
||||
|
||||
cm = confusion_matrix(df_sample["sex"], df_sample["predicted_gender"])
|
||||
|
||||
fig = px.imshow(cm, text_auto=True, aspect="auto", title="Confusion Matrix")
|
||||
st.plotly_chart(fig, use_container_width=True)
|
||||
|
||||
# Sample of correct and incorrect predictions
|
||||
correct_mask = df_sample["sex"] == df_sample["predicted_gender"]
|
||||
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
st.write("**Sample Correct Predictions**")
|
||||
correct_sample = df_sample[correct_mask][["name", "sex", "predicted_gender"]].head(10)
|
||||
st.dataframe(correct_sample, use_container_width=True)
|
||||
|
||||
with col2:
|
||||
st.write("**Sample Incorrect Predictions**")
|
||||
incorrect_sample = df_sample[~correct_mask][["name", "sex", "predicted_gender"]].head(
|
||||
10
|
||||
)
|
||||
st.dataframe(incorrect_sample, use_container_width=True)
|
||||
|
||||
def _display_dataset_predictions(self, df_sample: pd.DataFrame):
|
||||
"""Display dataset predictions without comparison"""
|
||||
# Just show predictions
|
||||
st.write("**Sample Predictions**")
|
||||
sample_results = df_sample[["name", "predicted_gender"]].head(20)
|
||||
st.dataframe(sample_results, use_container_width=True)
|
||||
|
||||
# Gender distribution
|
||||
gender_counts = df_sample["predicted_gender"].value_counts()
|
||||
fig = px.pie(
|
||||
values=gender_counts.values,
|
||||
names=gender_counts.index,
|
||||
title="Predicted Gender Distribution",
|
||||
)
|
||||
st.plotly_chart(fig, use_container_width=True)
|
||||
Reference in New Issue
Block a user