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import streamlit as st
import pandas as pd
from scripts.load_models import distilbert_model, bert_topic_model, recommendation_model
import gdown
# Streamlit app layout
st.title("Intelligent Customer Feedback Analyzer")
st.write("Analyze customer feedback for sentiment, topics, and get personalized recommendations.")
# User input for customer feedback file
uploaded_file = st.file_uploader("Upload a Feedback File (CSV, JSON, TXT)", type=["csv", "json", "txt"])
# Function to extract feedback text from different file formats
def extract_feedback(file):
if file.type == "text/csv":
df = pd.read_csv(file)
feedback_text = []
for column in df.columns:
feedback_text.extend(df[column].dropna().astype(str).tolist()) # Include all text in the CSV
return feedback_text
elif file.type == "application/json":
json_data = json.load(file)
feedback_text = []
if isinstance(json_data, list):
feedback_text = [item.get('feedback', '') for item in json_data if 'feedback' in item]
elif isinstance(json_data, dict):
feedback_text = list(json_data.values()) # Include all values if feedback key doesn't exist
return feedback_text
elif file.type == "text/plain":
return [file.getvalue().decode("utf-8")]
else:
return ["Unsupported file type"]
# Display error or feedback extraction status
if uploaded_file:
feedback_text_list = extract_feedback(uploaded_file)
if feedback_text_list:
for feedback_text in feedback_text_list:
if st.button(f'Analyze Feedback: "{feedback_text[:30]}..."'):
# Sentiment Analysis
sentiment = distilbert_model.predict([feedback_text])
sentiment_result = 'Positive' if sentiment == 1 else 'Negative'
st.write(f"Sentiment: {sentiment_result}")
# Topic Modeling
topics = bert_topic_model.predict([feedback_text])
st.write(f"Predicted Topic(s): {topics}")
# Recommendation System
recommendations = recommendation_model.predict([feedback_text])
st.write(f"Recommended Actions: {recommendations}")
else:
st.error("Unable to extract feedback from the file.")
else:
st.info("Please upload a feedback file to analyze.")
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