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import gradio as gr
import onnxruntime
from transformers import AutoTokenizer
import torch
import os
from transformers import pipeline
import subprocess
import moviepy.editor as mp
import base64
# token = AutoTokenizer.from_pretrained('ProsusAI/finbert')
inf_session = onnxruntime.InferenceSession('classifier1-quantized.onnx')
input_name = inf_session.get_inputs()[0].name
output_name = inf_session.get_outputs()[0].name
classes = ['Art', 'Astrology', 'Biology', 'Chemistry', 'Economics', 'History', 'Literature', 'Philosophy', 'Physics', 'Politics', 'Psychology', 'Sociology']
### --- Audio/Video to txt ---###
device = "cuda:0" if torch.cuda.is_available() else "cpu"
pipe = pipeline("automatic-speech-recognition",
model="openai/whisper-tiny.en",
chunk_length_s=30, device=device)
### --- Text Summary --- ###
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", device=device)
def video_identity(video):
transcription = pipe(video)["text"]
return transcription
def summary(text):
text = text.split('.')
max_chunk = 500
current_chunk = 0
chunks = []
for t in text:
if len(chunks) == current_chunk + 1:
if len(chunks[current_chunk]) + len(t.split(' ')) <= max_chunk:
chunks[current_chunk].extend(t.split(' '))
else:
current_chunk += 1
chunks.append(t.split(' '))
else:
chunks.append(t.split(' '))
for chunk in range(len(chunks)):
chunks[chunk] =' '.join(chunks[chunk])
summ = summarizer(chunks,max_length = 100)
return summ
def classify(video_file,encoded_video):
if encoded_video != "":
decoded_file_data = base64.b64decode(encoded_video)
with open("temp_video.mp4", "wb") as f:
f.write(decoded_file_data)
video_file = "temp_video.mp4"
clip = mp.VideoFileClip(video_file)
clip.audio.write_audiofile(r"audio.wav")
full_text = video_identity(r"audio.wav")
sum = summary(full_text)[0]['summary_text']
# input_ids = token(sum)['input_ids'][:512]
logits = inf_session.run([output_name],{input_name : [sum]})[0]
logits = torch.FloatTensor(logits)
probs = torch.sigmoid(logits)[0]
probs = list(probs)
label = classes[probs.index(max(probs))]
final = {
'text':full_text,
'summary':sum,
'label':label,
}
return final
text1 = gr.Textbox(label="Text")
text2 = gr.Textbox(label="Summary")
iface = gr.Interface(fn=classify,
inputs=['video','text'],
outputs = ['json'])
iface.launch(inline=False)
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