Spaces:
Sleeping
Sleeping
File size: 2,249 Bytes
300ed35 89aee27 813bfdd 892e60c 89aee27 300ed35 89aee27 813bfdd 300ed35 89aee27 813bfdd 300ed35 89aee27 813bfdd e71ff68 300ed35 813bfdd 892e60c 813bfdd 89aee27 813bfdd ccfdf25 813bfdd 89aee27 300ed35 89aee27 300ed35 89aee27 813bfdd 300ed35 813bfdd 89aee27 813bfdd 89aee27 813bfdd 89aee27 300ed35 89aee27 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
import streamlit as st
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.text import tokenizer_from_json
from tensorflow.keras.preprocessing.sequence import pad_sequences
import json
import numpy as np
import lime
from lime.lime_text import LimeTextExplainer
import matplotlib.pyplot as plt
import base64
from io import BytesIO
# --- Load tokenizer ---
@st.cache_resource
def load_tokenizer():
with open("tokenizer.json", "r") as f:
data = json.load(f)
return tokenizer_from_json(json.dumps(data))
# --- Load model ---
@st.cache_resource
def load_sentiment_model():
return load_model("review_amazon_sentiment5.h5")
# --- Predict function ---
def predict_proba(texts):
sequences = tokenizer.texts_to_sequences(texts)
padded = pad_sequences(sequences, maxlen=max_tokens)
preds = model.predict(padded)
return np.hstack([preds, 1-preds]) # For LIME binary classifier
# --- Visualize explanation ---
def plot_explanation(exp):
fig = exp.as_pyplot_figure()
buf = BytesIO()
fig.savefig(buf, format="png", bbox_inches="tight")
st.image(buf.getvalue(), use_container_width=True)
# --- Initialize ---
tokenizer = load_tokenizer()
model = load_sentiment_model()
max_tokens = 166
explainer = LimeTextExplainer(class_names=["Negative", "Positive"])
# --- Streamlit UI ---
st.title("Amazon Review Sentiment Analyzer")
user_input = st.text_area("Enter an Amazon product review:")
if st.button("Analyze"):
if user_input.strip():
# Predict
sequence = tokenizer.texts_to_sequences([user_input])
padded = pad_sequences(sequence, maxlen=max_tokens)
pred_prob = model.predict(padded)[0][0]
sentiment = "🟢 Positive" if pred_prob < 0.5 else "🔴 Negative"
# Show Result
st.markdown(f"**Sentiment:** {sentiment}")
st.markdown(f"**Confidence:** {pred_prob:.2f}")
# Explain with LIME
with st.spinner("Explaining prediction..."):
explanation = explainer.explain_instance(user_input, predict_proba, num_features=10)
st.markdown("### 🔍 Why this prediction?")
plot_explanation(explanation)
else:
st.warning("Please enter some text to analyze.")
|