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import gradio as gr
from transformers import pipeline
import random
# Load sentiment analysis model
sentiment_pipe = pipeline("sentiment-analysis")
# Load summarization model
summarizer = pipeline("summarization")
# Load text-to-speech model
tts_pipe = pipeline("text-to-speech", model="suno/bark-small")
## real work now
# Sentiment Analysis Function
def get_sentiment(input_text):
analysis = sentiment_pipe(input_text)[0]
return analysis['label'], str(round(analysis['score'], 4))
# Summarization Function
def summarize_text(input_text):
summary = summarizer(input_text, max_length=150, min_length=30, do_sample=False)[0]
return summary['summary_text']
# Text-to-Speech Function
def text_to_speech(input_text):
speech = tts_pipe(input_text)
return speech["path"]
# Chatbot Logic
def chat(message, history):
history = history or []
if message.startswith("How many"):
response = str(random.randint(1, 10))
elif message.startswith("How"):
response = random.choice(["Great", "Good", "Okay", "Bad"])
elif message.startswith("Where"):
response = random.choice(["Here", "There", "Somewhere"])
else:
response = "I don't know"
history.append((message, response))
return history, history
# Create Gradio Interface
with gr.Blocks(title="TrailTrek AI Suite") as demo:
gr.Markdown("# TrailTrek Gears Co. AI Prototype")
with gr.Tabs():
# Sentiment Analysis Tab
with gr.Tab("Sentiment Analysis"):
gr.Markdown("## Analyze Text Sentiment")
with gr.Row():
text_input = gr.Textbox(label="Input Text")
with gr.Column():
sentiment_label = gr.Textbox(label="Sentiment")
score_output = gr.Textbox(label="Confidence Score")
analyze_btn = gr.Button("Analyze")
analyze_btn.click(
fn=get_sentiment,
inputs=text_input,
outputs=[sentiment_label, score_output]
)
# Chatbot Tab
with gr.Tab("Chatbot"):
gr.Markdown("## Interactive Chat")
chatbot = gr.Chatbot()
msg = gr.Textbox(label="Your Message")
clear = gr.Button("Clear")
msg.submit(chat, [msg, chatbot], [chatbot, msg])
clear.click(lambda: None, None, chatbot, queue=False)
# Summarization Tab
with gr.Tab("Summarization"):
gr.Markdown("## Text Summarization")
with gr.Row():
long_text = gr.Textbox(label="Input Text", lines=5)
summary = gr.Textbox(label="Summary", lines=5)
summarize_btn = gr.Button("Summarize")
summarize_btn.click(
fn=summarize_text,
inputs=long_text,
outputs=summary
)
# Text-to-Speech Tab
with gr.Tab("Text-to-Speech"):
gr.Markdown("## Web Accessibility Prototype")
with gr.Row():
tts_input = gr.Textbox(label="Enter Text")
tts_output = gr.Audio(label="Generated Speech")
tts_btn = gr.Button("Convert to Speech")
tts_btn.click(
fn=text_to_speech,
inputs=tts_input,
outputs=tts_output
)
# Launch the Gradio app
demo.launch() |