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## Alternative movie poster generator
import streamlit as st
import pandas as pd
import numpy as np
import json
import requests
import os
from streamlit import session_state as session
from datetime import time, datetime
from zipfile import ZipFile
from kaggle.api.kaggle_api_extended import KaggleApi
from sentence_transformers import SentenceTransformer
from diffusers import DiffusionPipeline
###############################
## ------- FUNCTIONS ------- ##
###############################
#@st.cache(persist=True, show_spinner=False, suppress_st_warning=True)
@st.experimental_memo(persist=True, show_spinner=False, suppress_st_warning=True)
def load_dataset():
"""
Load Dataset from Kaggle
-return: dataframe containing dataset
"""
# Downloading Movies dataset
api.dataset_download_file('rounakbanik/the-movies-dataset', 'movies_metadata.csv')
# Extract data
zf = ZipFile('movies_metadata.csv.zip')
zf.extractall()
zf.close()
# Create dataframe
data = pd.read_csv('movies_metadata.csv', low_memory=False)
return data
@st.cache
def load_model():
#return DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
return DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
def query_summarization(text):
"""
Get summarization from HuggingFace Inference API
-param text: text to be summarized
-return: summarized text
"""
API_URL = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn"
headers = {"Authorization": f"Bearer {st.secrets['hf_token']}"}
payload = {"inputs": f"{text}",}
response = requests.request("POST", API_URL, headers=headers, json=payload).json()
return response[0].get('summary_text')
def generate_poster(movie_data):
"""
Function for recommending movies
-param movie_data: metadata of movie selected by user
-return: image of generated alternative poster
"""
# Get summarization of movie synopsis
with st.spinner("Please wait while the synopsis is being summarized..."):
synopsis_sum = query_summarization(movie_data.overview.values[0])
st.text(synopsis_sum)
# Get image based on synopsis
pipeline = load_model()
#pipeline = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-2")
#image = pipe(prompt).images[0]
#st.image(image, caption=movie_data.title)
return None #poster_image
###############################
## --- CONNECT TO KAGGLE --- ##
###############################
# Authenticate Kaggle account
os.environ['KAGGLE_USERNAME'] = st.secrets['username']
os.environ['KAGGLE_KEY'] = st.secrets['key']
api_token = {"username":st.secrets['username'],"key":st.secrets['key']}
with open('/home/appuser/.kaggle/kaggle.json', 'w') as file:
json.dump(api_token, file)
# Activate Kaggle API
try:
api = KaggleApi()
api.authenticate()
except:
with open('/home/appuser/.kaggle/kaggle.json', 'w') as file:
json.dump(api_token, file)
api = KaggleApi()
api.authenticate()
###############################
## --------- MAIN ---------- ##
###############################
image = None
# Create dataset
data = load_dataset()
st.title("""
Alternative Movie Poster Generator :film_frames:
This is a movie poster generator based on movie's synopsis :sunglasses:
Just select the title of a movie to generate an alternative poster.
""")
st.text("")
st.text("")
st.text("")
st.text("")
session.selected_movie = st.selectbox(label="Select a movie to generate alternative poster", options=data.title)
st.text("")
st.text("")
buffer1, col1, buffer2 = st.columns([1.3, 1, 1])
is_clicked = col1.button(label="Generate poster!")
if is_clicked:
image = generate_poster(data[data.title==session.selected_movie])
st.text("")
st.text("")
st.text("")
st.text("")
#if data is not None:
# st.table(data)
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