Update app.py

app.py

@@ -1,5 +1,4 @@
 import gradio as gr
-from utils import generate_response
 from transformers import BlipProcessor, BlipForConditionalGeneration
 import torch
 from PIL import Image
@@ -9,12 +8,12 @@ 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):
+def describe_image(image: Image.Image):
     try:
-        # Convert the image to the format the model expects
+        # Preprocess the image and pass it to the model
         inputs = processor(images=image, return_tensors="pt")
-
-        # Generate a caption for the image
+        
+        # Generate caption using the model
         out = model.generate(**inputs)
         description = processor.decode(out[0], skip_special_tokens=True)
         
@@ -25,8 +24,8 @@ def describe_image(image):
 # Define chatbot interaction function
 def chat(user_input, chat_history, image):
     try:
-        # Generate text response
-        response = generate_response(user_input)
+        # Generate text response (text processing happens via your custom function)
+        response = f"AI Response: {user_input}"  # Placeholder response for now
         
         # If an image is uploaded, describe it using the image captioning model
         if image is not None:
@@ -35,11 +34,11 @@ def chat(user_input, chat_history, image):
         else:
             image_description = "No image uploaded."
 
-        # Update chat history
+        # Update chat history with both user input and AI response
         chat_history.append(("User", user_input))
         chat_history.append(("AI", response))
         
-        # Format chat history for display with a conversational style
+        # Format chat history for display
         formatted_history = "\n".join(
             [f"{role}: {message}" for role, message in chat_history]
         )