Update app.py

app.py

@@ -1,88 +1,88 @@
-import gradio as gr
-import os
-import cv2
-import numpy as np
-from easyocr import Reader
-import google.generativeai as genai
-
-# Replace with the name of your secret
-GENERATIVE_AI_API_KEY = os.getenv("GEMINI_API")
-
-genai.configure(api_key=GENERATIVE_AI_API_KEY)  # Replace with your actual API key
-
-def detect_largest_font(image):
-  # Validate loaded image
-  if image is None:
-    print(f"Error: Unable to load image from {image}")
-    return None, None
-  if not isinstance(image, np.ndarray):
-    print("Error: Loaded data is not a NumPy array.")
-    return None, None
-
-  # Convert image to RGB (OpenCV uses BGR by default)
-  rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-  # Initialize EasyOCR reader
-  reader = Reader(['en'])  # Specify languages for OCR
-
-  # Perform OCR on the image
-  result = reader.readtext(rgb_image)
-
-  largest_font_text = ""
-  largest_font_size = 0
-
-  for detection in result:
-    # Extract bounding box and text from detection
-    bbox = detection[0]
-    text = detection[1].strip()
-
-    # Calculate bounding box dimensions
-    x_min, y_min = bbox[0]
-    x_max, y_max = bbox[2]
-    w = x_max - x_min
-    h = y_max - y_min
-
-    # Calculate font size (approximation based on bounding box size)
-    font_size = min(w, h)
-
-    # Update largest font text if found a larger font size
-    if font_size > largest_font_size:
-      largest_font_size = font_size
-      largest_font_text = text
-
-  # Generate movie review using GenerativeAI
-  model = genai.GenerativeModel('gemini-1.0-pro-latest')
-  response = model.generate_content(f"In What Year the movie {largest_font_text} is released and how are the cast and what the movie is about?")
-
-  if getattr(response, '_done', False) and getattr(response, '_result', None):  # Use getattr for flexibility
-    movie_review = response._result.candidates[0].content.parts[0].text
-    return largest_font_text, movie_review
-  else:
-    print("Error: Failed to generate movie review.")
-    return largest_font_text, None
-
-# Gradio interface definition
-interface = gr.Interface(
-  fn=detect_largest_font,
-  inputs=[
-    gr.Image(label="Upload Movie Poster"),
-  ],
-  outputs=[ gr.Text(elem_id="Movie Title"), gr.Text(elem_id="Movie Review")],
-  title="Movie Title and Review from Poster",
-  description="Upload a movie poster and get the title in largest font and a review generated by AI.",
-)
-
-# Add CSS styling (optional)
-interface.css = """
-.gr-output-container {
-  display: flex;
-  flex-direction: column;
-  gap: 10px;  /* Adjust spacing between outputs */
-}
-
-#extracted_title, #movie_review {
-  font-weight: bold;
-}
-"""
-
+import gradio as gr
+import os
+import cv2
+import numpy as np
+from easyocr import Reader
+import google.generativeai as genai
+
+# Replace with the name of your secret
+GENERATIVE_AI_API_KEY = os.getenv("GEMINI_API")
+
+genai.configure(api_key=GENERATIVE_AI_API_KEY) 
+
+def detect_largest_font(image):
+  # Validate loaded image
+  if image is None:
+    print(f"Error: Unable to load image from {image}")
+    return None, None
+  if not isinstance(image, np.ndarray):
+    print("Error: Loaded data is not a NumPy array.")
+    return None, None
+
+  # Convert image to RGB (OpenCV uses BGR by default)
+  rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+  # Initialize EasyOCR reader
+  reader = Reader(['en'])  # Specify languages for OCR
+
+  # Perform OCR on the image
+  result = reader.readtext(rgb_image)
+
+  largest_font_text = ""
+  largest_font_size = 0
+
+  for detection in result:
+    # Extract bounding box and text from detection
+    bbox = detection[0]
+    text = detection[1].strip()
+
+    # Calculate bounding box dimensions
+    x_min, y_min = bbox[0]
+    x_max, y_max = bbox[2]
+    w = x_max - x_min
+    h = y_max - y_min
+
+    # Calculate font size (approximation based on bounding box size)
+    font_size = min(w, h)
+
+    # Update largest font text if found a larger font size
+    if font_size > largest_font_size:
+      largest_font_size = font_size
+      largest_font_text = text
+
+  # Generate movie review using GenerativeAI
+  model = genai.GenerativeModel('gemini-1.0-pro-latest')
+  response = model.generate_content(f"In What Year the movie {largest_font_text} is released and how are the cast and what the movie is about?")
+
+  if getattr(response, '_done', False) and getattr(response, '_result', None):  # Use getattr for flexibility
+    movie_review = response._result.candidates[0].content.parts[0].text
+    return largest_font_text, movie_review
+  else:
+    print("Error: Failed to generate movie review.")
+    return largest_font_text, None
+
+# Gradio interface definition
+interface = gr.Interface(
+  fn=detect_largest_font,
+  inputs=[
+    gr.Image(label="Upload Movie Poster"),
+  ],
+  outputs=[ gr.Text(elem_id="Movie Title"), gr.Text(elem_id="Movie Review")],
+  title="Movie Title and Review from Poster",
+  description="Upload a movie poster and get the title in largest font and a review generated by AI.",
+)
+
+# Add CSS styling (optional)
+interface.css = """
+.gr-output-container {
+  display: flex;
+  flex-direction: column;
+  gap: 10px;  /* Adjust spacing between outputs */
+}
+
+#extracted_title, #movie_review {
+  font-weight: bold;
+}
+"""
+
 interface.launch()