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# Install necessary libraries
!pip install transformers gradio librosa
import gradio as gr
from transformers import pipeline
# Load models
# Sentiment Analysis
classifier_sentiment = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
def analyze_sentiment(text):
result = classifier_sentiment(text)[0]
label = result['label']
score = result['score']
return f"Label: {label}, Score: {score:.2f}"
# Translation
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr")
def translate_text(text):
result = translator(text)[0]
translated_text = result["translation_text"]
return translated_text
# Image Classification
classifier_image = pipeline("image-classification", model="google/mobilenet_v2_1.0_224")
def classify_image(image):
results = classifier_image(image)
output = ""
for result in results:
output += f"{result['label']}: {result['score']:.2f}\n"
return output
# Speech to Text
speech_to_text = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
def transcribe_audio(audio):
text = speech_to_text(audio)["text"]
return text
# Text Summarization
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
def summarize_text(text):
summary = summarizer(text, max_length=130, min_length=30, do_sample=False)[0]["summary_text"]
return summary
# Define custom CSS styles
css = """
<style>
body {
background-color: #e9ecef;
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
}
.gradio-container {
border-radius: 15px;
box-shadow: 0 4px 20px rgba(0, 0, 0, 0.15);
padding: 20px;
background-color: #74748a;
max-width: 800px;
margin: auto;
}
h1 {
color: black;
}
</style>
"""
with gr.Blocks(css=css) as demo:
gr.Markdown("<h1 style='text-align: center;'>Multi-functional AI Demo</h1>")
with gr.Tab("Sentiment Analysis😣"):
text_input = gr.Textbox(placeholder="Enter text here...")
text_output = gr.Textbox()
sentiment_button = gr.Button("Analyze")
sentiment_button.click(analyze_sentiment, inputs=text_input, outputs=text_output)
with gr.Tab("Translation📚"):
text_input_trans = gr.Textbox(placeholder="Enter English text here...")
text_output_trans = gr.Textbox()
trans_button = gr.Button("Translate")
trans_button.click(translate_text, inputs=text_input_trans, outputs=text_output_trans)
with gr.Tab("Image Classification🔮"):
image_input = gr.Image(type="pil")
image_output = gr.Textbox()
image_button = gr.Button("Classify")
image_button.click(classify_image, inputs=image_input, outputs=image_output)
with gr.Tab("Speech to Text🔊"):
audio_input = gr.Audio(sources=["microphone"], type="filepath")
audio_output = gr.Textbox()
audio_button = gr.Button("Transcribe")
audio_button.click(transcribe_audio, inputs=audio_input, outputs=audio_output)
with gr.Tab("Text Summarization📑"):
text_input_summ = gr.Textbox(placeholder="Enter text here...")
text_output_summ = gr.Textbox()
summ_button = gr.Button("Summarize")
summ_button.click(summarize_text, inputs=text_input_summ, outputs=text_output_summ)
demo.launch() |