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import gradio as gr
from transformers import pipeline
import requests
import os
import time
asr = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
def download_screenplay(screenplay):
filename = "generated_screenplay.txt"
with open(filename, "w", encoding="utf-8") as file:
file.write(screenplay)
display(HTML(f'<a href="{filename}" download>Download Screenplay</a>'))
def speech_to_text(speech):
text = asr(speech)["text"]
return text
PALM_KEY = "AIzaSyArKo4frsgJqoAiuJJ0GVsiIlDAjqD08z4"
cons = """
-elaborate everything
-no repetations
-add naration
- 'Close up':
- 'Medium':
- 'Wide':
Begin your narration with these cues, and let the screenplay unfold based on the user's input. We want to experience the heart-pounding excitement of this crucial match, and you are our guide through this thrilling journey.
"""
def generate_text(prompt, length=500, genre=None):
url = "https://generativelanguage.googleapis.com/v1beta3/models/text-bison-001:generateText?key=" + PALM_KEY
headers = {
"Content-Type": "application/json",
}
payload = {
"prompt": {
"text": cons+prompt+f" Genre: {genre} Length: {length} words.",
},
}
response = requests.post(url, headers=headers, json=payload)
response.raise_for_status()
response_json = response.json()
return response_json['candidates'][0]['output']
def download_screenplay(screenplay):
filename = "generated_screenplay.txt"
with open(filename, "w", encoding="utf-8") as file:
file.write(screenplay)
def submit_feedback(feedback):
feedback_message = "Thank you for your feedback! We appreciate your input."
return feedback_message
demo = gr.Blocks(
css="""
.gradio-container {background-color: beige;font-family: Roboto, sans-serif;}footer {visibility: hidden; }
.Markdown-body { color: visible;}"""
)
with demo:
gr.Markdown(
"""
<center><b><div style="font-size: 28px; color:#333333">
A web based application which generates movie screenplay with the input of voice
</div></b></center>
""")
with gr.Tab("Audio to screenplay generation"):
demo.heading = "Problem statement: Generate a movie screenplay from an audio file."
audio_file = gr.Audio(type="filepath")
live=True
length = gr.Slider(label="Length in words", step=1)
genre = gr.Dropdown(label="Genre", choices=["Action", "Adventure", "Comedy", "Drama", "Fantasy", "Horror", "Mystery", "Sci-Fi", "Thriller"])
text = gr.Textbox(label="Speech to Text")
generated_screenplay = gr.Textbox(label="Required screenplay")
with gr.Row():
b1 = gr.Button("CONVERT")
b2 = gr.Button("SCREENPLAY")
b3 = gr.Button("DOWNLOAD")
b1.click(speech_to_text, inputs=audio_file, outputs=text)
b2.click(generate_text, inputs=[text, length, genre], outputs=[generated_screenplay])
b3.click(download_screenplay, inputs=[generated_screenplay])
feedback = gr.Textbox(label="Feedback", placeholder="Share your feedback here")
feedback_button = gr.Button("Submit Feedback")
feedback_output = gr.Textbox(label="Feedback Status")
feedback_button.click(submit_feedback, inputs=[feedback], outputs=[feedback_output])
demo.launch() |