Update app.py

app.py

@@ -1,72 +1,73 @@
-import streamlit as st
-from ocr_cpu import extract_text_got  # The updated OCR function
-import json
-
-# --- UI Styling ---
-st.set_page_config(page_title="DualTextOCRFusion",
-                   layout="centered", page_icon="🔍")
-
-st.markdown(
-    """
-    <style>
-    .reportview-container {
-        background: #f4f4f4;
-    }
-    .sidebar .sidebar-content {
-        background: #e0e0e0;
-    }
-    h1 {
-        color: #007BFF;
-    }
-    .upload-btn {
-        background-color: #007BFF;
-        color: white;
-        padding: 10px;
-        border-radius: 5px;
-        text-align: center;
-    }
-    </style>
-    """, unsafe_allow_html=True
-)
-
-# --- Title ---
-st.title("🔍 DualTextOCRFusion")
-st.write("Upload an image with **Hindi** and **English** text to extract and search for keywords.")
-
-# --- Image Upload Section ---
-uploaded_file = st.file_uploader(
-    "Choose an image file", type=["jpg", "jpeg", "png"])
-
-if uploaded_file is not None:
-    st.image(uploaded_file, caption='Uploaded Image', use_column_width=True)
-
-    # Extract text from the image using the selected OCR function (GOT)
-    with st.spinner("Extracting text using the model..."):
-        try:
-            extracted_text = extract_text_got(
-                uploaded_file)  # Pass uploaded_file directly
-            if not extracted_text.strip():
-                st.warning("No text extracted from the image.")
-        except Exception as e:
-            st.error(f"Error during text extraction: {str(e)}")
-            extracted_text = ""
-
-    # Display extracted text
-    st.subheader("Extracted Text")
-    st.text_area("Text", extracted_text, height=250)
-
-    # Save extracted text for search
-    if extracted_text:
-        with open("extracted_text.json", "w") as json_file:
-            json.dump({"text": extracted_text}, json_file)
-
-        # --- Keyword Search ---
-        st.subheader("Search for Keywords")
-        keyword = st.text_input(
-            "Enter a keyword to search in the extracted text")
-
-        if keyword:
-            if keyword.lower() in extracted_text.lower():
-                st.success(f"Keyword **'{keyword}'** found in the text!")
-            else:
-                st.error(f"Keyword **'{keyword}'** not found.")
+import streamlit as st
+from ocr_cpu import extract_text_got, clean_text  # Import OCR and text cleaning functions
+import json
+
+# --- UI Styling ---
+st.set_page_config(page_title="DualTextOCRFusion", layout="centered", page_icon="🔍")
+
+st.markdown(
+    """
+    <style>
+    .reportview-container {
+        background: #f4f4f4;
+    }
+    .sidebar .sidebar-content {
+        background: #e0e0e0;
+    }
+    h1 {
+        color: #007BFF;
+    }
+    .upload-btn {
+        background-color: #007BFF;
+        color: white;
+        padding: 10px;
+        border-radius: 5px;
+        text-align: center;
+    }
+    </style>
+    """, 
+    unsafe_allow_html=True
+)
+
+# --- Title ---
+st.title("🔍 DualTextOCRFusion")
+st.write("Upload an image with **Hindi**, **English**, or **Hinglish** text to extract and clean text for keyword search.")
+
+# --- Image Upload Section ---
+uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    st.image(uploaded_file, caption='Uploaded Image', use_column_width=True)
+
+    # Extract text from the image using GOT OCR function
+    with st.spinner("Extracting text from the image..."):
+        try:
+            extracted_text = extract_text_got(uploaded_file)  # Use GOT OCR to extract text
+            if not extracted_text.strip():
+                st.warning("No text extracted from the image.")
+            else:
+                # Clean the extracted text to remove extra spaces
+                cleaned_text = clean_text(extracted_text)
+                st.success("Text extraction and cleaning successful.")
+        except Exception as e:
+            st.error(f"Error during text extraction: {str(e)}")
+            extracted_text = cleaned_text = ""
+
+    # Display cleaned text
+    st.subheader("Cleaned Extracted Text")
+    st.text_area("Cleaned Text", cleaned_text, height=250)
+
+    # Save cleaned text for search
+    if cleaned_text:
+        with open("extracted_text.json", "w") as json_file:
+            json.dump({"text": cleaned_text}, json_file)
+
+        # --- Keyword Search ---
+        st.subheader("Search for Keywords")
+        keyword = st.text_input("Enter a keyword to search in the cleaned text")
+
+        if keyword:
+            if keyword.lower() in cleaned_text.lower():
+                st.success(f"Keyword **'{keyword}'** found in the text!")
+            else:
+                st.error(f"Keyword **'{keyword}'** not found.")