start

app.py

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+import streamlit as st
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
+import nltk
+
+# Download NLTK data
+nltk.download('punkt')
+
+# Initialize the image captioning pipeline
+captioner = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
+
+# Load the tokenizer and model for tag generation
+tokenizer = AutoTokenizer.from_pretrained("fabiochiu/t5-base-tag-generation")
+model = AutoModelForSeq2SeqLM.from_pretrained("fabiochiu/t5-base-tag-generation")
+
+# Streamlit app title
+st.title("Multi-purpose Machine Learning App")
+
+# Create tabs for different functionalities
+tab1, tab2 = st.tabs(["Image Captioning", "Text Tag Generation"])
+
+# Image Captioning Tab
+with tab1:
+    st.header("Image Captioning")
+    
+    # Input for image URL
+    image_url = st.text_input("Enter the URL of the image:")
+    
+    # If an image URL is provided
+    if image_url:
+        try:
+            # Display the image
+            st.image(image_url, caption="Provided Image", use_column_width=True)
+            
+            # Generate the caption
+            caption = captioner(image_url)
+            
+            # Display the caption
+            st.write("**Generated Caption:**")
+            st.write(caption[0]['generated_text'])
+        except Exception as e:
+            st.error(f"An error occurred: {e}")
+
+# Text Tag Generation Tab
+with tab2:
+    st.header("Text Tag Generation")
+    
+    # Text area for user input
+    text = st.text_area("Enter the text for tag extraction:", height=200)
+    
+    # Button to generate tags
+    if st.button("Generate Tags"):
+        if text:
+            try:
+                # Tokenize and encode the input text
+                inputs = tokenizer([text], max_length=512, truncation=True, return_tensors="pt")
+                
+                # Generate tags
+                output = model.generate(**inputs, num_beams=8, do_sample=True, min_length=10, max_length=64)
+                
+                # Decode the output
+                decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
+                
+                # Extract unique tags
+                tags = list(set(decoded_output.strip().split(", ")))
+                
+                # Display the tags
+                st.write("**Generated Tags:**")
+                st.write(tags)
+            except Exception as e:
+                st.error(f"An error occurred: {e}")
+        else:
+            st.warning("Please enter some text to generate tags.")
+
+# To run this app, save this code to a file (e.g., `app.py`) and run `streamlit run app.py` in your terminal.