Update app.py

app.py

@@ -1,67 +1,73 @@
-import cv2
-import pytesseract
-from PIL import Image
-import streamlit as st
-from streamlit_webrtc import webrtc_streamer, VideoTransformerBase
-import numpy as np
-import os
-
-# Set the Tesseract path for Hugging Face Spaces (assuming Tesseract is installed)
-pytesseract.pytesseract.tesseract_cmd = '/usr/bin/tesseract'
-
-# Streamlit UI
-st.title("Quiz Checker App")
-st.header("Upload an Answer Key and Use Webcam for Real-Time Evaluation")
-
-# Upload answer key
-uploaded_file = st.file_uploader("Upload Answer Key Image (JPG/PNG)", type=["jpg", "jpeg", "png"])
-
-if uploaded_file:
-    # Open the uploaded image
-    answer_key_img = Image.open(uploaded_file)
-
-    # Ensure the image is in RGB format
-    answer_key_img = answer_key_img.convert("RGB")
-    
-    # Use Tesseract to extract text from the image
-    answer_key_text = pytesseract.image_to_string(answer_key_img)
-    
-    # Split and clean extracted text
-    answers = [ans.strip() for ans in answer_key_text.splitlines() if ans.strip()]
-    
-    st.success("Answer key loaded successfully!")
-    st.write("Extracted Answers:")
-    st.write(answers)
-
-    class QuizChecker(VideoTransformerBase):
-        def transform(self, frame):
-            frame = frame.to_ndarray(format="bgr24")
-
-            # Convert frame to grayscale
-            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-
-            # Use Tesseract to extract text from the frame
-            student_text = pytesseract.image_to_string(gray)
-            student_answers = [ans.strip() for ans in student_text.splitlines() if ans.strip()]
-
-            # Overlay results on the video feed
-            correct_count = 0
-            for idx, student_answer in enumerate(student_answers):
-                if idx < len(answers) and student_answer == answers[idx]:
-                    color = (0, 255, 0)  # Green for correct
-                    correct_count += 1
-                else:
-                    color = (0, 0, 255)  # Red for incorrect
-                cv2.putText(frame, f"Q{idx+1}: {student_answer}", (10, 50 + idx * 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2)
-
-            # Display score
-            cv2.putText(frame, f"Score: {correct_count}/{len(answers)}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
-
-            return frame
-
-    # Streamlit UI for starting webcam
-    st.header("Start Webcam")
-    webrtc_streamer(key="quiz-checker", video_transformer_factory=QuizChecker, media_stream_constraints={"video": True})
-
-else:
-    st.info("Please upload an answer key image to proceed.")
+import cv2
+import pytesseract
+from PIL import Image
+import streamlit as st
+from streamlit_webrtc import webrtc_streamer, VideoTransformerBase
+import numpy as np
+import os
+
+# # Set the Tesseract path for Hugging Face Spaces (assuming Tesseract is installed)
+# pytesseract.pytesseract.tesseract_cmd = '/usr/bin/tesseract'
+
+# Print the Tesseract path to verify its location
+tesseract_path = pytesseract.pytesseract.tesseract_cmd
+
+# Display the path in the Streamlit app
+st.write(f"Tesseract path: {tesseract_path}")
+
+# Streamlit UI
+st.title("Quiz Checker App")
+st.header("Upload an Answer Key and Use Webcam for Real-Time Evaluation")
+
+# Upload answer key
+uploaded_file = st.file_uploader("Upload Answer Key Image (JPG/PNG)", type=["jpg", "jpeg", "png"])
+
+if uploaded_file:
+    # Open the uploaded image
+    answer_key_img = Image.open(uploaded_file)
+
+    # Ensure the image is in RGB format
+    answer_key_img = answer_key_img.convert("RGB")
+    
+    # Use Tesseract to extract text from the image
+    answer_key_text = pytesseract.image_to_string(answer_key_img)
+    
+    # Split and clean extracted text
+    answers = [ans.strip() for ans in answer_key_text.splitlines() if ans.strip()]
+    
+    st.success("Answer key loaded successfully!")
+    st.write("Extracted Answers:")
+    st.write(answers)
+
+    class QuizChecker(VideoTransformerBase):
+        def transform(self, frame):
+            frame = frame.to_ndarray(format="bgr24")
+
+            # Convert frame to grayscale
+            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+            # Use Tesseract to extract text from the frame
+            student_text = pytesseract.image_to_string(gray)
+            student_answers = [ans.strip() for ans in student_text.splitlines() if ans.strip()]
+
+            # Overlay results on the video feed
+            correct_count = 0
+            for idx, student_answer in enumerate(student_answers):
+                if idx < len(answers) and student_answer == answers[idx]:
+                    color = (0, 255, 0)  # Green for correct
+                    correct_count += 1
+                else:
+                    color = (0, 0, 255)  # Red for incorrect
+                cv2.putText(frame, f"Q{idx+1}: {student_answer}", (10, 50 + idx * 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2)
+
+            # Display score
+            cv2.putText(frame, f"Score: {correct_count}/{len(answers)}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
+
+            return frame
+
+    # Streamlit UI for starting webcam
+    st.header("Start Webcam")
+    webrtc_streamer(key="quiz-checker", video_transformer_factory=QuizChecker, media_stream_constraints={"video": True})
+
+else:
+    st.info("Please upload an answer key image to proceed.")