Update app.py

app.py

@@ -1,14 +1,14 @@
 from transformers import BlipForQuestionAnswering, AutoProcessor
 from PIL import Image
 import gradio as gr
-import openai  # For OpenAI API
+import openai
 
 # Load the BLIP model and processor
 model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base")
 processor = AutoProcessor.from_pretrained("Salesforce/blip-vqa-base")
 
 # Set your OpenAI API key
-openai.api_key = "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"  # Replace with your OpenAI API key
+openai.api_key = "your_openai_api_key_here"  # Replace with your OpenAI API key
 
 # Function to generate the initial answer with BLIP and expand it with OpenAI API
 def qna(image, question):
@@ -28,12 +28,19 @@ def qna(image, question):
             max_tokens=200  # Adjust max_tokens as needed for response length
         )
         detailed_answer = response.choices[0].text.strip()
+
+    except openai.error.OpenAIError as e:
+        # Print detailed error information
+        print(f"OpenAI API error: {e}")
+        detailed_answer = f"Failed to get response from OpenAI API: {e}"
+
     except Exception as e:
-        print(f"Exception occurred: {e}")
-        detailed_answer = "Failed to get response from OpenAI API."
+        print(f"General exception: {e}")
+        detailed_answer = "Failed to connect to OpenAI API."
 
     return detailed_answer
 
 # Create Gradio interface
 interf = gr.Interface(fn=qna, inputs=["image", "text"], outputs="text")
 interf.launch()
+