Create app.py

app.py

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+	
+import streamlit as st
+	from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
+	import torch
+	from PIL import Image
+	import requests
+	import os
+	
+
+	# Load the model and tokenizer
+	model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+	feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+	tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+	
+
+	device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+	model.to(device)
+	
+
+	# Define the image captioning function
+	def generate_caption(image):
+	    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
+	    pixel_values = pixel_values.to(device)
+	
+
+	    output_ids = model.generate(pixel_values, max_length=16, num_beams=4)
+	    caption = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
+	    return caption.strip()
+	
+
+	# Function to fetch product information from GROQ API
+	def fetch_product_info(caption):
+	    # Define your GROQ API endpoint
+	    api_url = "GroqCloud"  # Replace with your actual GROQ API URL
+
+	
+
+	    # Retrieve the API key from environment variables
+	    api_key = os.getenv('groq_api')  # Use environment variable for API key
+	
+
+	    if not api_key:
+	        st.error("API key not found. Set the 'GROQ_API_KEY' environment variable.")
+	        return None
+	
+
+	    # Set up the headers with the Bearer token
+	    headers = {"Authorization": f"Bearer {groq_api}"}
+	
+
+	    # Query the API
+	    params = {"query": caption}
+	    try:
+	        response = requests.get(api_url, headers=headers, params=params)
+	        response.raise_for_status()  # Raise an exception for HTTP errors
+	
+
+	        data = response.json()
+	        if data:
+	            product = data.get('products', [])[0]  # Assuming the first product is relevant
+	            ingredients = product.get('ingredients', 'N/A')
+	            usage = product.get('usage', 'N/A')
+	            barcode = product.get('barcode', 'N/A')
+	            return ingredients, usage, barcode
+	        else:
+	            st.write("No products found in the response.")
+	            return None
+	    except requests.RequestException as e:
+	        st.error(f"Error fetching data from GROQ API: {e}")
+	        return None
+	
+
+	# Streamlit UI
+	st.title("Image Captioning and Product Information")
+	st.write("Upload an image to get a caption and related product information.")
+	
+
+	# Upload image
+	uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+	
+
+	if uploaded_image:
+	    image = Image.open(uploaded_image).convert("RGB")
+	    st.image(image, caption="Uploaded Image", use_column_width=True)
+	    
+	    # Generate and display caption
+	    with st.spinner("Generating caption..."):
+	        caption = generate_caption(image)
+	        st.write(f"**Caption:** {caption}")
+	
+
+	        # Fetch and display additional information from GROQ API
+	        info = fetch_product_info(caption)
+	        
+	        if info:
+	            ingredients, usage, barcode = info
+	            st.write("### Additional Information:")
+	            st.write(f"**Ingredients:** {ingredients}")
+	            st.write(f"**Usage:** {usage}")
+	            st.write(f"**Barcoding:** {barcode}")
+	        else:
+	            st.write("No additional information found for this product.")
+	
+
+	
+
+	
+