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import streamlit as st | |
from transformers import pipeline | |
# Step 1: Load the Hugging Face model | |
def load_model(): | |
return pipeline("text-generation", model="gpt2") # Replace 'gpt2' with another model if needed | |
generator = load_model() | |
# Step 2: Design the Streamlit layout | |
st.title("Hugging Face Text Generator") | |
st.write("Generate creative text using GPT-2!") | |
# Get user input | |
user_input = st.text_area("Enter a prompt for text generation:", "Once upon a time") | |
# Generate text when the button is clicked | |
if st.button("Generate Text"): | |
with st.spinner("Generating..."): | |
results = generator(user_input, max_length=50, num_return_sequences=1) | |
generated_text = results[0]["generated_text"] | |
st.subheader("Generated Text:") | |
st.write(generated_text) | |
st.write("Powered by Streamlit and Hugging Face π€") | |
import streamlit as st | |
from transformers import pipeline | |
from PIL import Image | |
# Load Hugging Face models | |
def load_image_classifier(): | |
return pipeline("image-classification", model="google/vit-base-patch16-224") | |
def load_text_classifier(): | |
return pipeline("sentiment-analysis") # Default model for sentiment analysis | |
# Initialize models | |
image_classifier = load_image_classifier() | |
text_classifier = load_text_classifier() | |
# App title and navigation | |
st.title("Hugging Face Classification App") | |
st.sidebar.title("Choose Task") | |
task = st.sidebar.selectbox("Select a task", ["Image Classification", "Text Classification"]) | |
if task == "Image Classification": | |
st.header("Image Classification") | |
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"]) | |
if uploaded_file is not None: | |
# Display uploaded image | |
image = Image.open(uploaded_file) | |
st.image(image, caption="Uploaded Image", use_column_width=True) | |
# Classify the image | |
if st.button("Classify Image"): | |
with st.spinner("Classifying..."): | |
results = image_classifier(image) | |
st.subheader("Classification Results") | |
for result in results: | |
st.write(f"**{result['label']}**: {result['score']:.2f}") | |
elif task == "Text Classification": | |
st.header("Text Classification") | |
text_input = st.text_area("Enter text for classification", "Streamlit is an amazing tool!") | |
# Classify the text | |
if st.button("Classify Text"): | |
with st.spinner("Classifying..."): | |
results = text_classifier(text_input) | |
st.subheader("Classification Results") | |
for result in results: | |
st.write(f"**{result['label']}**: {result['score']:.2f}") | |
st.write("Powered by Streamlit and Hugging Face π€") | |