Update app.py

app.py

@@ -1,11 +1,26 @@
 import gradio as gr
 from utils import generate_response
+from transformers import BlipProcessor, BlipForConditionalGeneration
+import torch
+from PIL import Image
 
-# Placeholder function for image description (replace this with a real model or API)
+# Load the BLIP image captioning model and processor
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+# Function to generate description for an image using BLIP
 def describe_image(image):
-    # This is a placeholder. In a real implementation, use a model like CLIP or other image captioning models.
-    # You can integrate an API or a pre-trained model to generate descriptions for images.
-    return "This is a placeholder description for the uploaded image."
+    try:
+        # Convert the image to the format the model expects
+        inputs = processor(images=image, return_tensors="pt")
+
+        # Generate a caption for the image
+        out = model.generate(**inputs)
+        description = processor.decode(out[0], skip_special_tokens=True)
+        
+        return description
+    except Exception as e:
+        return f"Error describing the image: {e}"
 
 # Define chatbot interaction function
 def chat(user_input, chat_history, image):
@@ -13,7 +28,7 @@ def chat(user_input, chat_history, image):
         # Generate text response
         response = generate_response(user_input)
         
-        # If an image is uploaded, describe it
+        # If an image is uploaded, describe it using the image captioning model
         if image is not None:
             image_description = describe_image(image)
             response += f"\n\n[Image Description]: {image_description}"