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import streamlit as st
from PIL import Image
import pytesseract
from transformers import RagRetriever, RagTokenForGeneration, RagTokenizer
from datasets import load_dataset
# --- App Title ---
st.set_page_config(page_title="Landmark Mapper", layout="wide")
st.title("🗺️ Landmark Mapper - Discover, Describe & Contribute")
# --- Image Upload ---
uploaded_file = st.file_uploader("Upload a landmark image", type=["jpg", "jpeg", "png"])
# --- OCR + Description Input ---
description = ""
if uploaded_file:
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
if st.checkbox("Run OCR to extract text from image"):
with st.spinner("Extracting text..."):
ocr_text = pytesseract.image_to_string(image)
st.text_area("Extracted Text", ocr_text, height=100)
description = st.text_area("Enter a description in your local language", height=150)
# --- RAG Integration ---
if st.button("Analyze with AI") and description:
with st.spinner("Running RAG model..."):
# Load RAG model components
tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-base")
model = RagTokenForGeneration.from_pretrained("facebook/rag-token-base")
retriever = RagRetriever.from_pretrained("facebook/rag-token-base", index_name="legacy")
# Encode and retrieve
input_dict = tokenizer.prepare_seq2seq_batch(description, return_tensors="pt")
input_dict["input_ids"] = input_dict["input_ids"][:, :128] # limit input length
input_dict["retrieval_kwargs"] = {"n_docs": 5}
generated = model.generate(
input_ids=input_dict["input_ids"],
context_input_ids=None,
context_attention_mask=None,
num_beams=2,
min_length=30,
max_length=128
)
output = tokenizer.batch_decode(generated, skip_special_tokens=True)
st.subheader("📘 AI-Enhanced Landmark Info")
st.write(output[0])
# --- Corpus Contribution ---
if description:
st.success("✅ Thank you! Your description is now part of the landmark language corpus.")
st.markdown("Help us map Indian culture, one landmark at a time.")
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