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import streamlit as st
import os
import librosa
import numpy as np
from transformers import pipeline
from ftplib import FTP
# Streamlit UI
st.title("Sentiment Analysis from FTP Audio Files 🎵")
# User inputs for FTP connection
st.sidebar.header("📡 FTP Login")
host = st.sidebar.text_input("Host", "cph.v4one.co.uk")
username = st.sidebar.text_input("Username", "your_username")
password = st.sidebar.text_input("Password", type="password")
remote_path = "/path/to/audio/folders" # Change based on server
# Connect and List Available Folders
if st.sidebar.button("🔄 Connect & List Folders"):
try:
# Connect to FTP server
ftp = FTP(host, timeout=120)
ftp.login(user=username, passwd=password)
# List available folders (filter date-based ones)
folders = []
ftp.retrlines("LIST", lambda x: folders.append(x.split()[-1])) # Get directory names
available_dates = [folder for folder in folders if folder.startswith("2025")]
ftp.quit()
st.session_state["available_dates"] = available_dates
st.success("✅ Connected! Select a date below.")
except Exception as e:
st.error(f"Connection failed: {e}")
# Dropdown for Date Selection
if "available_dates" in st.session_state:
selected_date = st.selectbox("📅 Select a Date", st.session_state["available_dates"])
if st.button("📥 Download & Analyze"):
try:
ftp = FTP(host)
ftp.login(user=username, passwd=password)
remote_folder = f"{remote_path}/{selected_date}"
ftp.cwd(remote_folder)
local_folder = f"temp_audio/{selected_date}"
os.makedirs(local_folder, exist_ok=True)
audio_files = []
ftp.retrlines("LIST", lambda x: audio_files.append(x.split()[-1])) # Get file names
# Download files
for file in audio_files:
local_file_path = os.path.join(local_folder, file)
with open(local_file_path, "wb") as f:
ftp.retrbinary(f"RETR {file}", f.write)
ftp.quit()
st.success(f"✅ Downloaded {len(audio_files)} files from {selected_date}")
# Sentiment Analysis
sentiment_model = pipeline("sentiment-analysis")
results = []
for file in os.listdir(local_folder):
file_path = os.path.join(local_folder, file)
y, sr = librosa.load(file_path, sr=16000)
mfccs = np.mean(librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13), axis=1)
# Mock transcription (Replace with real ASR model)
text = "This is a sample transcription"
sentiment = sentiment_model(text)
results.append({"File": file, "Sentiment": sentiment[0]["label"], "Confidence": sentiment[0]["score"]})
st.write("### Sentiment Analysis Results")
st.table(results)
except Exception as e:
st.error(f"Download failed: {e}")
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