Update app.py

app.py

@@ -1,10 +1,11 @@
 import os
 import gradio as gr
 from transformers import DetrForObjectDetection, DetrImageProcessor
+from langchain_google_genai.chat_models import ChatGoogleGenerativeAI  # For Chat Gemini
 from PIL import Image
 import torch
-import requests
 import json
+import requests
 
 # Load credentials (stringified JSON) from environment variable
 credentials_string = os.environ.get("GOOGLE_APPLICATION_CREDENTIALS")
@@ -21,19 +22,19 @@ with open("service_account.json", "w") as f:
 # Set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the temporary file
 os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "service_account.json"
 
-# Initialize Gemini model
+# Initialize Chat Gemini model
 llm = ChatGoogleGenerativeAI(model='gemini-1.5-pro')
 
-# Load the model and processor for DETR
+# Load the model and processor for DETR (for object detection)
 processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
 model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
 
-# Global chat history variable
+# Global chat history
 chat_history = []
 
 def chat_with_gemini(message):
     global chat_history
-    # Get a response from the language model
+    # Get a response from the Gemini model
     bot_response = llm.predict(message)  # This will interact with the Gemini model
     chat_history.append((message, bot_response))
     return chat_history
@@ -56,15 +57,17 @@ def analyze_image(image_path):
 
         # If any objects detected, display labels
         if len(results["labels"]) > 0:
-            bot_response = f"Objects detected: {', '.join(map(str, results['labels'].tolist()))}."
+            detected_objects = [str(label.item()) for label in results["labels"]]
+            bot_response = f"Objects detected: {', '.join(detected_objects)}."
         else:
             bot_response = "No objects detected."
-
+        
+        chat_history.append(("Uploaded an image for analysis", bot_response))
+        return chat_history
     except Exception as e:
-        bot_response = f"Error processing the image: {str(e)}"
-
-    chat_history.append(("Uploaded an image for analysis", bot_response))
-    return chat_history
+        error_msg = f"Error processing the image: {str(e)}"
+        chat_history.append(("Error during image analysis", error_msg))
+        return chat_history
 
 # Build the Gradio interface
 with gr.Blocks() as demo: