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import streamlit as st
from transformers import pipeline
# Step 1: Load the Hugging Face model
@st.cache_resource
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 🤗")
import streamlit as st
from transformers import pipeline
from PIL import Image
# Load Hugging Face models
@st.cache_resource
def load_image_classifier():
return pipeline("image-classification", model="google/vit-base-patch16-224")
@st.cache_resource
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 🤗")
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