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import streamlit as st
from PIL import Image
import torch
from transformers import CLIPProcessor, CLIPModel
import matplotlib.pyplot as plt
# Load the pre-trained CLIP model and processor
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16")
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch16")
# Function to make predictions from the image
def predict_image_description(image):
# Preprocess the image and generate text inputs
inputs = processor(text=["a photo of an animal", "a photo of a human", "a photo of a car", "a photo of a tree", "a photo of a house"],
images=image,
return_tensors="pt",
padding=True)
# Get model predictions
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1) # Softmax to get probabilities
# Return top 3 predictions
top_3_probabilities, top_3_indices = torch.topk(probs, 3)
labels = ["an animal", "a human", "a car", "a tree", "a house"]
predictions = []
for i in range(3):
prediction = labels[top_3_indices[0][i]] # Get the label
probability = top_3_probabilities[0][i].item() # Get probability
predictions.append(f"{prediction}: {probability * 100:.2f}%")
return predictions
# Streamlit UI
st.title("Real-Time Image-to-Text Generator")
st.markdown("Upload an image, and I will tell you what it is!")
# Image upload feature
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Open the uploaded image
image = Image.open(uploaded_file)
# Display the image
st.image(image, caption="Uploaded Image", use_column_width=True)
# Predict the description
predictions = predict_image_description(image)
# Display the predictions
st.write("Predictions:")
for prediction in predictions:
st.write(prediction) |