hosted_analysis.py

import streamlit as st
import matplotlib.pyplot as plt
import pandas as pd

# import plotly.express as px
import pickle
from sklearn.metrics import confusion_matrix
from model import X_test, y_test
import seaborn as sns

df1 = pd.read_csv("data/train.csv")


clf = pickle.load(open("models/final_pipeline.pickle", "rb"))
# Streamlit code starts here
st.title("Fake News Detection")

data = st.text_area(label="Enter News")
if data:
    prediction = clf.predict([data])
    result_message = "Authentic" if prediction == 1 else "Fake"
    st.subheader(f"The News is {result_message}")


st.title("Dataset Analysis")

# Display the first 5 rows of the DataFrame
st.subheader("Displaying the first 5 rows of the DataFrame:")
st.dataframe(df1.head(5))

# Show summary statistics
st.subheader("Summary Statistics:")
st.write(df1.describe())

# Show specific columns
selected_columns = st.multiselect("Select columns to display:", df1.columns)
if selected_columns:
    st.dataframe(df1[selected_columns].head(10))

# Show a count of rows
st.subheader("Row Count:")
st.write(f"The DataFrame contains {df1.count()} rows.")

# Pie Chart
st.title("Pie Chart")

# Calculate counts
lf_count = df1[df1["label"] == 0].shape[0]
lt_count = df1[df1["label"] == 1].shape[0]

labels = ["Authentic", "Fake"]
sizes = [lt_count, lf_count]

fig, ax = plt.subplots()
ax.pie(sizes, labels=labels, autopct="%1.1f%%", startangle=90)
ax.axis("equal")  # Equal aspect ratio ensures the pie chart is circular.

# Display the pie chart using Streamlit
st.pyplot(fig)


st.title("Bar Chart")

fig, ax = plt.subplots()
ax.bar(labels, sizes)

# Add labels and title
ax.set_xlabel("Category")
ax.set_ylabel("Count")
ax.set_title("Authentic vs. Fake Label Counts")

# Display the bar graph using Streamlit
st.pyplot(fig)

# Confusion matrix
st.title("Confusion Matrix")
y_pred = clf.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(6, 4))
# plt.imshow(cm, interpolation='nearest', cmap=plt.get_cmap('Blues'))
sns.heatmap(
    cm, annot=True, fmt="d", cmap="Blues", cbar=False, linewidths=0.5, linecolor="black"
)
st.pyplot(plt)