Update app.py

app.py

@@ -1,35 +1,34 @@
-import torch
-from transformers import AutoProcessor, AutoModel, VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
-from PIL import Image
 import streamlit as st
+from PIL import Image
+import io
 
-# Load the saved model state dictionary
-model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')))
+st.title("Artisan Product Submission Form")
 
-# Load the necessary components
-feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+uploaded_file = st.file_uploader("Choose a file", type=["png", "jpg", "jpeg"])
+ 
+if uploaded_file is not None:
+    # To read file as bytes:
+    bytes_data = uploaded_file.getvalue()
+    st.write("Filename: ", uploaded_file.name)
+    # st.write(bytes_data)  # This will display the raw bytes, typically not useful for users
 
-# Function to generate a caption for an image
-@st.cache_resource
-def generate_caption(image):
-    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
-    output_ids = model.generate(pixel_values, max_length=100, num_beams=5, early_stopping=True)
-    caption = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
-    return caption
+    # To display the image
+    image = Image.open(io.BytesIO(bytes_data))
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
 
-# Streamlit app
-def main():
-    st.title("Image Captioning")
-    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+# Creating text input box
 
-    if uploaded_file is not None:
-        image = Image.open(uploaded_file)
-        st.image(image, caption="Uploaded Image", use_column_width=True)
+st.header("Tell us about your product")
 
-        caption = generate_caption(image)
-        st.write(f"Caption: {caption}")
+# Input fields
+product_type = st.text_input("Type of Product", placeholder="e.g., Handmade Jewelry, Pottery, Painting")
+product_origin = st.text_input("Product Origin", placeholder="e.g., City, Country, Region")
+product_description = st.text_area("Brief Description", placeholder="Provide a brief description of your product")
 
-if __name__ == "__main__":
-    main()
+# Submit button
+if st.button("Submit"):
+    st.write("Thank you for your submission!")
+    st.write("### Product Details")
+    st.write(f"**Type of Product:** {product_type}")
+    st.write(f"**Product Origin:** {product_origin}")
+    st.write(f"**Description:** {product_description}")