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Create app.py

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  1. app.py +51 -0
app.py ADDED
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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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+
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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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+
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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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+
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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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+
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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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+
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+ text = st.text_input("Enter text for image description", "A picture of a cat") # Default text
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+