app.py

import gradio as gr
from transformers import pipeline
import streamlit as st
import socket

# Load the pre-trained sentiment-analysis pipeline
classifier = pipeline('sentiment-analysis')

# Function to classify sentiment
def classify_text(text):
    result = classifier(text)[0]
    return f"{result['label']} with score {result['score']}"

# Function to find an available port
def find_free_port():
    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
        s.bind(('', 0))
        return s.getsockname()[1]

# Find an available port
port = find_free_port()

# Launch the Gradio interface on the dynamically found port
iface = gr.Interface(fn=classify_text, inputs="text", outputs="text")
iface.launch(server_port=port)

# Streamlit code
st.title('IMDb Sentiment Analysis')
st.write('This project performs sentiment analysis on IMDb movie reviews using Streamlit.')