Create app.py
Browse files
app.py
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import streamlit as st
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from PIL import Image
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from transformers import CLIPProcessor, CLIPModel
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import os
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# Initialize Hugging Face Model
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processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
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model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
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# Streamlit app layout
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st.title("AI Multimodal File Processing App")
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st.write("This app uses Hugging Face's CLIP model to analyze and edit image and text files.")
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# Option to upload an image
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st.subheader("Upload an Image")
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uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
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if uploaded_image is not None:
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image = Image.open(uploaded_image)
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st.image(image, caption="Uploaded Image", use_column_width=True)
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text = st.text_input("Enter text for image description", "A picture of a cat") # Default text
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if st.button("Analyze Image"):
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# Process the image with CLIP
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inputs = processor(text=text, images=image, return_tensors="pt", padding=True)
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outputs = model(**inputs)
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# Display the similarity score between text and image
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logits_per_image = outputs.logits_per_image # Similarity score
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st.write(f"Similarity Score: {logits_per_image.item()}")
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st.write(f"Description: {text}")
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# Option to upload a text file
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st.subheader("Upload a Text File")
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uploaded_textfile = st.file_uploader("Choose a text file...", type=["txt"])
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if uploaded_textfile is not None:
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text_data = uploaded_textfile.read().decode('utf-8')
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st.text_area("Text File Content", text_data, height=200)
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if st.button("Process Text"):
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# Example: Modify the text (e.g., convert to uppercase)
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processed_text = text_data.upper()
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st.write("Processed Text:")
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st.text_area("Processed Text Output", processed_text, height=200)
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# Optional: File download button
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if uploaded_image is not None or uploaded_textfile is not None:
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st.download_button("Download Processed File", data="Some file data here", file_name="processed_file.txt")
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