Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,104 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import cv2
+import pytesseract
+from googletrans import Translator
+from PIL import Image, ImageDraw, ImageFont
+import numpy as np
+
+# Step 1: Load the image
+def load_image(image_file):
+    # Read the image using OpenCV
+    img = cv2.imdecode(np.fromstring(image_file.read(), np.uint8), cv2.IMREAD_COLOR)
+    return img
+
+# Step 2: Extract text and coordinates using pytesseract
+def extract_text_with_position(image):
+    # Convert the image to grayscale for better OCR performance
+    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    
+    # Use pytesseract to get OCR results along with bounding boxes
+    details = pytesseract.image_to_data(gray_image, output_type=pytesseract.Output.DICT)
+    
+    text_data = []
+    for i in range(len(details['text'])):
+        if details['text'][i].strip() != '':
+            text_data.append({
+                'text': details['text'][i],
+                'left': details['left'][i],
+                'top': details['top'][i],
+                'width': details['width'][i],
+                'height': details['height'][i]
+            })
+    
+    return text_data
+
+# Step 3: Translate the extracted text using Google Translate
+def translate_text(text, target_language='en'):
+    translator = Translator()
+    translation = translator.translate(text, dest=target_language)
+    return translation.text
+
+# Step 4: Recreate the image with translated text
+def recreate_image_with_translated_text(original_img, text_data, target_language):
+    # Convert OpenCV image to Pillow image for easier manipulation
+    pil_img = Image.fromarray(cv2.cvtColor(original_img, cv2.COLOR_BGR2RGB))
+    draw = ImageDraw.Draw(pil_img)
+    
+    try:
+        # Load a default font (you can use a specific font if you want)
+        font = ImageFont.load_default()
+    except IOError:
+        font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 30)
+
+    for item in text_data:
+        # Translate each piece of text
+        translated_text = translate_text(item['text'], target_language)
+        
+        # Draw the translated text on the new image at the same position
+        draw.text((item['left'], item['top']), translated_text, font=font, fill="black")
+
+    # Convert the image back to an OpenCV format
+    output_img = cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR)
+    return output_img
+
+# Step 5: Display or download the translated image
+def display_or_download_image(output_img):
+    # Convert image to bytes to allow for download
+    _, img_bytes = cv2.imencode('.png', output_img)
+    st.image(output_img, channels="BGR", caption="Translated Image", use_column_width=True)
+    
+    # Provide a button to download the image
+    st.download_button(
+        label="Download Translated Image",
+        data=img_bytes.tobytes(),
+        file_name="translated_image.png",
+        mime="image/png"
+    )
+
+# Streamlit Interface
+def main():
+    st.title("Image Translation App")
+
+    # Upload image
+    image_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
+
+    if image_file is not None:
+        # Load the image and show it
+        img = load_image(image_file)
+        st.image(img, caption="Uploaded Image", channels="BGR", use_column_width=True)
+
+        # Get the target language from the user
+        target_language = st.text_input("Enter the target language code (e.g., 'es' for Spanish):")
+
+        if target_language:
+            # Extract text from image
+            text_data = extract_text_with_position(img)
+
+            # Recreate the image with translated text
+            translated_img = recreate_image_with_translated_text(img, text_data, target_language)
+
+            # Display or allow download of the translated image
+            display_or_download_image(translated_img)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+if __name__ == "__main__":
+    main()