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tejovk311 commited on Jun 11, 2025

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a0df8b3

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1 Parent(s): 057cb30

Update app.py

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app.py +147 -147

app.py CHANGED Viewed

@@ -1,158 +1,23 @@
-from flask import Flask, request, send_from_directory, render_template_string
-import os
-from datetime import datetime
-app = Flask(__name__)
-UPLOAD_FOLDER = '/tmp/received_images'
-os.makedirs(UPLOAD_FOLDER, exist_ok=True)
-latest_filename = None  # Store the latest uploaded filename globally
-@app.route('/upload', methods=['POST'])
-def upload_file():
-    global latest_filename
-    img_data = request.data
-    filename = datetime.now().strftime("%Y%m%d_%H%M%S") + ".jpeg"
-    filepath = os.path.join(UPLOAD_FOLDER, filename)
-    with open(filepath, 'wb') as f:
-        f.write(img_data)
-    latest_filename = filename
-    return 'OK', 200
-@app.route('/images/<filename>')
-def uploaded_file(filename):
-    return send_from_directory(UPLOAD_FOLDER, filename)
-@app.route('/')
-def show_latest_image():
-    global latest_filename
-    if not latest_filename:
-        return "<h1>No image received yet</h1>"
-    # Auto-refresh the page every 3 seconds
-    html = '''
-    <html>
-    <head>
-        <meta http-equiv="refresh" content="3">
-    </head>
-    <body>
-        <h1>Latest Image</h1>
-        <img src="/images/{{ file }}" style="max-width:600px;"><br>
-        <small>{{ file }}</small>
-    </body>
-    </html>
-    '''
-    return render_template_string(html, file=latest_filename)
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=7860)
-##########################################
 # from flask import Flask, request, send_from_directory, render_template_string
 # import os
 # from datetime import datetime
-# import cv2
-# import torch
-# from ultralytics import YOLO
-# import base64
-# import numpy as np
-# import google.generativeai as genai
-# # Initialize Flask app
 # app = Flask(__name__)
 # UPLOAD_FOLDER = '/tmp/received_images'
 # os.makedirs(UPLOAD_FOLDER, exist_ok=True)
-# latest_filename = None
-# last_extraction_result = []
-# # Set your Gemini API key
-# genai.configure(api_key="YOUR_GEMINI_API_KEY")  # Replace for local or private space only
-# gemma_model = genai.GenerativeModel(model_name="models/gemma-3-12b-it")
-# def detect_and_extract_text(image_path, yolo_model_path):
-#     if not os.path.exists(image_path):
-#         print(f"Error: Image file not found at {image_path}")
-#         return []
-#     try:
-#         yolo = YOLO(yolo_model_path)
-#     except Exception as e:
-#         print(f"Error loading YOLO model: {e}")
-#         return []
-#     img = cv2.imread(image_path)
-#     if img is None:
-#         print(f"Error: Could not load image at {image_path}")
-#         return []
-#     try:
-#         results = yolo(image_path)
-#     except Exception as e:
-#         print(f"Error during YOLO inference: {e}")
-#         return []
-#     output = []
-#     for i, r in enumerate(results):
-#         for j, (box, cls) in enumerate(zip(r.boxes.xyxy, r.boxes.cls)):
-#             x1, y1, x2, y2 = map(int, box)
-#             cropped_img = img[y1:y2, x1:x2]
-#             class_name = yolo.names[int(cls)]
-#             try:
-#                 cropped_img_rgb = cv2.cvtColor(cropped_img, cv2.COLOR_BGR2RGB)
-#                 _, buffer = cv2.imencode('.jpg', cropped_img_rgb)
-#                 image_data = base64.b64encode(buffer).decode('utf-8')
-#                 response = gemma_model.generate_content([
-#                     {
-#                         "role": "user",
-#                         "parts": [
-#                             {"text": "Extract the text from this image."},
-#                             {"inline_data": {"mime_type": "image/jpeg", "data": image_data}}
-#                         ]
-#                     }
-#                 ])
-#                 extracted_text = response.text.strip()
-#                 output.append({
-#                     "class_name": class_name,
-#                     "extracted_text": extracted_text,
-#                     "bounding_box": [x1, y1, x2, y2]
-#                 })
-#             except Exception as e:
-#                 print(f"Error processing object {j+1} (Class={class_name}): {e}")
-#                 output.append({
-#                     "class_name": class_name,
-#                     "extracted_text": "Error during text extraction",
-#                     "bounding_box": [x1, y1, x2, y2]
-#                 })
-#     return output
 # @app.route('/upload', methods=['POST'])
 # def upload_file():
-#     global latest_filename, last_extraction_result
 #     img_data = request.data
 #     filename = datetime.now().strftime("%Y%m%d_%H%M%S") + ".jpeg"
 #     filepath = os.path.join(UPLOAD_FOLDER, filename)
 #     with open(filepath, 'wb') as f:
 #         f.write(img_data)
 #     latest_filename = filename
-#     # Perform detection + text extraction
-#     try:
-#         last_extraction_result = detect_and_extract_text(filepath, "best.pt")
-#     except Exception as e:
-#         last_extraction_result = [{"class_name": "Error", "extracted_text": str(e), "bounding_box": []}]
 #     return 'OK', 200
 # @app.route('/images/<filename>')
@@ -161,10 +26,11 @@ if __name__ == '__main__':
 # @app.route('/')
 # def show_latest_image():
-#     global latest_filename, last_extraction_result
 #     if not latest_filename:
 #         return "<h1>No image received yet</h1>"
 #     html = '''
 #     <html>
 #     <head>
@@ -174,17 +40,151 @@ if __name__ == '__main__':
 #         <h1>Latest Image</h1>
 #         <img src="/images/{{ file }}" style="max-width:600px;"><br>
 #         <small>{{ file }}</small>
-#         <h2>Extracted Information</h2>
-#         <ul>
-#         {% for item in result %}
-#             <li><b>{{ item.class_name }}</b>: {{ item.extracted_text }} [{{ item.bounding_box }}]</li>
-#         {% endfor %}
-#         </ul>
 #     </body>
 #     </html>
 #     '''
-#     return render_template_string(html, file=latest_filename, result=last_extraction_result)
 # if __name__ == '__main__':
 #     app.run(host='0.0.0.0', port=7860)

 # from flask import Flask, request, send_from_directory, render_template_string
 # import os
 # from datetime import datetime
 # app = Flask(__name__)
 # UPLOAD_FOLDER = '/tmp/received_images'
 # os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+# latest_filename = None  # Store the latest uploaded filename globally
 # @app.route('/upload', methods=['POST'])
 # def upload_file():
+#     global latest_filename
 #     img_data = request.data
 #     filename = datetime.now().strftime("%Y%m%d_%H%M%S") + ".jpeg"
 #     filepath = os.path.join(UPLOAD_FOLDER, filename)
 #     with open(filepath, 'wb') as f:
 #         f.write(img_data)
 #     latest_filename = filename
 #     return 'OK', 200
 # @app.route('/images/<filename>')
 # @app.route('/')
 # def show_latest_image():
+#     global latest_filename
 #     if not latest_filename:
 #         return "<h1>No image received yet</h1>"
+#     # Auto-refresh the page every 3 seconds
 #     html = '''
 #     <html>
 #     <head>
 #         <h1>Latest Image</h1>
 #         <img src="/images/{{ file }}" style="max-width:600px;"><br>
 #         <small>{{ file }}</small>
 #     </body>
 #     </html>
 #     '''
+#     return render_template_string(html, file=latest_filename)
 # if __name__ == '__main__':
 #     app.run(host='0.0.0.0', port=7860)
+##########################################
+from flask import Flask, request, send_from_directory, render_template_string
+import os
+from datetime import datetime
+import cv2
+import torch
+from ultralytics import YOLO
+import base64
+import numpy as np
+import google.generativeai as genai
+# Initialize Flask app
+app = Flask(__name__)
+UPLOAD_FOLDER = '/tmp/received_images'
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+latest_filename = None
+last_extraction_result = []
+# Set your Gemini API key
+genai.configure(api_key="AIzaSyD7aLN4NphvsPuyB6N3FwVw5Nxzv7gxzv4")  # Replace for local or private space only
+gemma_model = genai.GenerativeModel(model_name="models/gemma-3-12b-it")
+def detect_and_extract_text(image_path, yolo_model_path):
+    if not os.path.exists(image_path):
+        print(f"Error: Image file not found at {image_path}")
+        return []
+    try:
+        yolo = YOLO(yolo_model_path)
+    except Exception as e:
+        print(f"Error loading YOLO model: {e}")
+        return []
+    img = cv2.imread(image_path)
+    if img is None:
+        print(f"Error: Could not load image at {image_path}")
+        return []
+    try:
+        results = yolo(image_path)
+    except Exception as e:
+        print(f"Error during YOLO inference: {e}")
+        return []
+    output = []
+    for i, r in enumerate(results):
+        for j, (box, cls) in enumerate(zip(r.boxes.xyxy, r.boxes.cls)):
+            x1, y1, x2, y2 = map(int, box)
+            cropped_img = img[y1:y2, x1:x2]
+            class_name = yolo.names[int(cls)]
+            try:
+                cropped_img_rgb = cv2.cvtColor(cropped_img, cv2.COLOR_BGR2RGB)
+                _, buffer = cv2.imencode('.jpg', cropped_img_rgb)
+                image_data = base64.b64encode(buffer).decode('utf-8')
+                response = gemma_model.generate_content([
+                    {
+                        "role": "user",
+                        "parts": [
+                            {"text": "Extract the text from this image."},
+                            {"inline_data": {"mime_type": "image/jpeg", "data": image_data}}
+                        ]
+                    }
+                ])
+                extracted_text = response.text.strip()
+                output.append({
+                    "class_name": class_name,
+                    "extracted_text": extracted_text,
+                    "bounding_box": [x1, y1, x2, y2]
+                })
+            except Exception as e:
+                print(f"Error processing object {j+1} (Class={class_name}): {e}")
+                output.append({
+                    "class_name": class_name,
+                    "extracted_text": "Error during text extraction",
+                    "bounding_box": [x1, y1, x2, y2]
+                })
+    return output
+@app.route('/upload', methods=['POST'])
+def upload_file():
+    global latest_filename, last_extraction_result
+    img_data = request.data
+    filename = datetime.now().strftime("%Y%m%d_%H%M%S") + ".jpeg"
+    filepath = os.path.join(UPLOAD_FOLDER, filename)
+    with open(filepath, 'wb') as f:
+        f.write(img_data)
+    latest_filename = filename
+    # Perform detection + text extraction
+    try:
+        last_extraction_result = detect_and_extract_text(filepath, "best.pt")
+    except Exception as e:
+        last_extraction_result = [{"class_name": "Error", "extracted_text": str(e), "bounding_box": []}]
+    return 'OK', 200
+@app.route('/images/<filename>')
+def uploaded_file(filename):
+    return send_from_directory(UPLOAD_FOLDER, filename)
+@app.route('/')
+def show_latest_image():
+    global latest_filename, last_extraction_result
+    if not latest_filename:
+        return "<h1>No image received yet</h1>"
+    html = '''
+    <html>
+    <head>
+        <meta http-equiv="refresh" content="3">
+    </head>
+    <body>
+        <h1>Latest Image</h1>
+        <img src="/images/{{ file }}" style="max-width:600px;"><br>
+        <small>{{ file }}</small>
+        <h2>Extracted Information</h2>
+        <ul>
+        {% for item in result %}
+            <li><b>{{ item.class_name }}</b>: {{ item.extracted_text }} [{{ item.bounding_box }}]</li>
+        {% endfor %}
+        </ul>
+    </body>
+    </html>
+    '''
+    return render_template_string(html, file=latest_filename, result=last_extraction_result)
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)