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import streamlit as st
import pandas as pd
import json
import joblib
from huggingface_hub import hf_hub_download
# Display the version of huggingface_hub
import huggingface_hub
st.write(f"Hugging Face Hub version: {huggingface_hub.__version__}")
# Function to download models from Hugging Face using huggingface_hub
def download_model_from_huggingface(repo_id, filename):
"""Downloads a model file from Hugging Face repository."""
try:
# Using huggingface_hub API to download model directly
model_file = hf_hub_download(repo_id, filename)
return model_file
except Exception as e:
st.error(f"Error downloading model {filename}: {e}")
return None
# Function to load models safely
def load_model(model_file):
"""Loads a model from a file."""
try:
# Here we're assuming the model is in a `.joblib` file format
return joblib.load(model_file)
except Exception as e:
st.error(f"Error loading model: {e}")
return None
# Set repository ID and model filenames
REPO_ID = "totoro74/Intelligent_Customer_Analyzer"
# Load models using the download function
try:
# Download and load the BERTopic model
bert_topic_model_file = download_model_from_huggingface(REPO_ID, "models/bertopic_model.joblib")
bert_topic_model = load_model(bert_topic_model_file)
# Download and load the Recommendation model
recommendation_model_file = download_model_from_huggingface(REPO_ID, "models/recommendation_model.joblib")
recommendation_model = load_model(recommendation_model_file)
except Exception as e:
st.error(f"⚠️ Error loading models: {e}")
# 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):
"""Extracts text data from CSV, JSON, or TXT files."""
try:
if file.type == "text/csv":
df = pd.read_csv(file)
feedback_text = df.iloc[:, 0].dropna().astype(str).tolist()
return feedback_text
elif file.type == "application/json":
json_data = json.load(file)
feedback_text = [item.get('feedback', '') for item in json_data if isinstance(item, dict)]
return feedback_text
elif file.type == "text/plain":
return file.getvalue().decode("utf-8").split("\n")
else:
return ["Unsupported file type"]
except Exception as e:
st.error(f"Error processing file: {e}")
return []
# Process uploaded file
if uploaded_file:
feedback_text_list = extract_feedback(uploaded_file)
if feedback_text_list and bert_topic_model and recommendation_model:
for feedback_text in feedback_text_list:
with st.expander(f'🔍 Analyze Feedback: "{feedback_text[:30]}..."'):
try:
# Topic Prediction
topics = bert_topic_model.predict([feedback_text])
st.write(f"**Predicted Topic(s):** {topics}")
# Recommendations
recommendations = recommendation_model.predict([feedback_text])
st.write(f"**Recommended Actions:** {recommendations}")
except Exception as e:
st.error(f"Error analyzing feedback: {e}")
else:
st.error("⚠️ Unable to analyze feedback. Please check if models are correctly loaded.")
else:
st.info("📌 Please upload a feedback file to analyze.")
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