Update app.py

app.py

@@ -1,96 +1,37 @@
-import streamlit as st
-from transformers import TrOCRProcessor, VisionEncoderDecoderModel
-from PIL import Image
-import cv2
-import numpy as np
-
-# Load the model and processor
-@st.cache_resource
-def load_model():
-    processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
-    model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
-    return processor, model
-
-processor, model = load_model()
-
-# Helper function to preprocess the image and detect lines
-def detect_lines(image, min_height=20, min_width=100):
-    # Convert the PIL image to a NumPy array
-    image_np = np.array(image)
-
-    # Convert to grayscale
-    gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
-
-    # Apply binary thresholding
-    _, binary = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
-
-    # Dilate to merge nearby text
-    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
-    dilated = cv2.dilate(binary, kernel, iterations=1)
-
-    # Find contours
-    contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-
-    # Sort contours top-to-bottom
-    bounding_boxes = [cv2.boundingRect(c) for c in contours]
-    bounding_boxes = sorted(bounding_boxes, key=lambda b: b[1])  # Sort by y-coordinate
-
-    # Filter out small contours and merge nearby ones
-    filtered_boxes = []
-    for x, y, w, h in bounding_boxes:
-        if h >= min_height and w >= min_width:  # Filter small boxes
-            filtered_boxes.append((x, y, w, h))
-
-    # Extract individual lines as images
-    line_images = []
-    for (x, y, w, h) in filtered_boxes:
-        line = image_np[y:y+h, x:x+w]
-        line_images.append(line)
-
-    return line_images
-
-# Streamlit app
-st.title("OCR API Service with Multiline Support")
-
-uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
-
-if uploaded_file is not None:
-    try:
-        # Load and display the uploaded image
-        image = Image.open(uploaded_file).convert("RGB")
-        st.image(image, caption="Uploaded Image", use_column_width=True)
-
-        # Detect lines in the image
-        st.write("Detecting lines...")
-        line_images = detect_lines(image, min_height=30, min_width=100)
-        st.write(f"Detected {len(line_images)} lines in the image.")
-
-        # Perform OCR on each detected line
-        extracted_text = ""
-        for idx, line_img in enumerate(line_images):
-            # Convert the line image to PIL format
-            line_pil = Image.fromarray(line_img)
-
-            # Prepare the image for OCR
-            pixel_values = processor(images=line_pil, return_tensors="pt").pixel_values
-
-            # Generate text from the line image
-            generated_ids = model.generate(pixel_values)
-            generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-
-            # Append the extracted text
-            extracted_text += f"{generated_text}\n"
-
-        # Display extracted text
-        st.subheader("Extracted Text:")
-        st.text_area("Output Text", extracted_text, height=300)
-
-        # Simulate API-like JSON response
-        json_response = {"extracted_text": extracted_text}
-        st.write("API Response:")
-        st.json(json_response)
-
-    except Exception as e:
-        st.error(f"An error occurred: {e}")
-else:
-    st.info("Please upload an image to start the OCR process.")
+<?php
+defined('BASEPATH') or exit('No direct script access allowed');
+
+class OcrClient extends CI_Controller
+{
+    public function process_image()
+    {
+        $url = 'http://<your-streamlit-app-url>/'; // Update with your Streamlit app URL
+        $imagePath = '/path/to/image.jpg'; // Path to the image file on the server
+
+        $curl = curl_init();
+        curl_setopt($curl, CURLOPT_URL, $url);
+        curl_setopt($curl, CURLOPT_POST, 1);
+        curl_setopt($curl, CURLOPT_RETURNTRANSFER, true);
+
+        // Attach the image file
+        $cfile = curl_file_create($imagePath, 'image/jpeg', 'image.jpg');
+        curl_setopt($curl, CURLOPT_POSTFIELDS, ['file' => $cfile]);
+
+        // Execute the cURL request
+        $response = curl_exec($curl);
+        $httpCode = curl_getinfo($curl, CURLINFO_HTTP_CODE);
+        curl_close($curl);
+
+        // Handle the response
+        if ($httpCode === 200) {
+            $result = json_decode($response, true);
+            if (isset($result['extracted_text'])) {
+                echo "Extracted Text: " . $result['extracted_text'];
+            } else {
+                echo "Error: Unexpected response from the API.";
+            }
+        } else {
+            echo "Error: Unable to connect to the OCR service.";
+        }
+    }
+}