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app.py

@@ -0,0 +1,63 @@
+import streamlit as st
+from PIL import Image
+import easyocr
+import os
+import datetime
+
+# Initialize OCR reader
+reader = easyocr.Reader(['en', 'hi', 'te', 'ta', 'bn', 'ml', 'gu', 'mr'], gpu=False)
+
+# Create a directory to save uploads
+UPLOAD_DIR = "uploaded_data"
+os.makedirs(UPLOAD_DIR, exist_ok=True)
+
+st.set_page_config(page_title="Indian Landmark Mapper", layout="centered")
+
+st.title("📍 Indian Landmark Mapper")
+st.write("Help preserve India's heritage. Upload a local landmark photo and describe it in your native language.")
+
+# File uploader
+image_file = st.file_uploader("Upload a photo of the landmark", type=['jpg', 'jpeg', 'png'])
+
+# Text input
+description = st.text_area("Write a short description in your local language")
+
+# Optional: OCR toggle
+run_ocr = st.checkbox("Run OCR on uploaded image (optional)")
+
+# Optional: Location
+location = st.text_input("Enter location (optional: village, town, district)")
+
+# Submission button
+if st.button("Submit"):
+    if image_file is None or not description.strip():
+        st.warning("Please upload an image and enter a description.")
+    else:
+        # Save image
+        timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
+        image_path = os.path.join(UPLOAD_DIR, f"{timestamp}_{image_file.name}")
+        with open(image_path, "wb") as f:
+            f.write(image_file.getbuffer())
+
+        # Save metadata
+        metadata = {
+            "description": description.strip(),
+            "location": location,
+            "image_filename": image_path,
+            "timestamp": timestamp
+        }
+
+        # In real deployment, replace this with saving to Hugging Face dataset or DB
+        st.success("✅ Submission received! Thank you for preserving your local heritage.")
+        st.json(metadata)
+
+# OCR Section
+if image_file and run_ocr:
+    st.subheader("🧠 OCR Result")
+    img = Image.open(image_file)
+    st.image(img, caption="Uploaded Image", use_column_width=True)
+
+    with st.spinner("Running OCR..."):
+        result = reader.readtext(np.array(img))
+        extracted_text = "\n".join([line[1] for line in result])
+        st.code(extracted_text)

requirements.txt

@@ -1,3 +1,4 @@
-altair
-pandas
-streamlit
+streamlit
+easyocr
+Pillow
+numpy