Update app.py

app.py

@@ -9,18 +9,19 @@ model_id = "llava-hf/llava-interleave-qwen-0.5b-hf"
 processor = LlavaProcessor.from_pretrained(model_id)
 model = LlavaForConditionalGeneration.from_pretrained(model_id).to("cpu")
 
-# Initialize inference clients
+# Initialize inference client
 client_gemma = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
 
+# Functions
 def llava(inputs, history):
-    """Processes an image and text input using Llava."""
+    """Processes image + text input using Llava."""
     image = Image.open(inputs["files"][0]).convert("RGB")
     prompt = f"<|im_start|>user <image>\n{inputs['text']}<|im_end|>"
     processed = processor(prompt, image, return_tensors="pt").to("cpu")
     return processed
 
 def respond(message, history):
-    """Generate a response based on text or image input."""
+    """Generate a response for text or image input."""
     if "files" in message and message["files"]:
         # Handle image + text input
         inputs = llava(message, history)
@@ -32,45 +33,56 @@ def respond(message, history):
             buffer += new_text
             yield buffer
     else:
-        # Handle text-only input
+        # Handle text input
         user_message = message["text"]
         history.append([user_message, None])  # Append user message to history
-
-        # Prepare prompt for the language model
         prompt = [{"role": "user", "content": msg[0]} for msg in history if msg[0]]
         response = client_gemma.chat_completion(prompt, max_tokens=200)
-
-        # Extract response and update history
         bot_message = response["choices"][0]["message"]["content"]
-        history[-1][1] = bot_message  # Update the last entry with bot's response
+        history[-1][1] = bot_message  # Update history with bot's response
         yield history
 
 def generate_image(prompt):
-    """Generates an image based on the user prompt."""
+    """Generates an image based on user prompt."""
     client = InferenceClient("KingNish/Image-Gen-Pro")
     return client.predict("Image Generation", None, prompt, api_name="/image_gen_pro")
 
-# Set up Gradio interface
-with gr.Blocks() as demo:
-    chatbot = gr.Chatbot()
-    with gr.Row():
-        with gr.Column():
-            text_input = gr.Textbox(placeholder="Enter your message...")
-            file_input = gr.File(label="Upload an image")
-        with gr.Column():
-            output = gr.Image(label="Generated Image")
+# Gradio app setup with multi-page and sidebar
+with gr.Blocks(title="AI Chat & Tools", theme="compact") as demo:
+    with gr.Sidebar():
+        gr.Markdown("## AI Assistant Sidebar")
+        gr.Markdown("Navigate through features and try them out.")
+        gr.Button("Open Chat").click(None, [], [], _js="() => window.location.hash='#chat'")
+        gr.Button("Generate Image").click(None, [], [], _js="() => window.location.hash='#image'")
+
+    with gr.Page("chat", title="Chat Interface"):
+        chatbot = gr.Chatbot(label="Chat with AI Assistant", show_label=False)
+        with gr.Row():
+            text_input = gr.Textbox(placeholder="Enter your message...", lines=2, show_label=False)
+            file_input = gr.File(label="Upload an image", file_types=["image/*"])
+
+        def handle_text(text, history=[]):
+            """Handle text input."""
+            return respond({"text": text}, history), history
+
+        def handle_file(files, history=[]):
+            """Handle file upload."""
+            return respond({"files": files, "text": "Describe this image."}, history), history
+
+        # Connect callbacks
+        text_input.submit(handle_text, [text_input, chatbot], [chatbot])
+        file_input.change(handle_file, [file_input, chatbot], [chatbot])
 
-    def handle_text(text, history=[]):
-        """Handle text input and generate responses."""
-        return respond({"text": text}, history), history
+    with gr.Page("image", title="Generate Image"):
+        gr.Markdown("### Image Generator")
+        image_prompt = gr.Textbox(placeholder="Describe the image to generate", show_label=False)
+        image_output = gr.Image(label="Generated Image")
 
-    def handle_file_upload(files, history=[]):
-        """Handle file uploads and generate responses."""
-        return respond({"files": files, "text": "Describe this image."}, history), history
+        def generate_image_callback(prompt):
+            """Handle image generation."""
+            return generate_image(prompt)
 
-    # Connect components to callbacks
-    text_input.submit(handle_text, [text_input, chatbot], [chatbot])
-    file_input.change(handle_file_upload, [file_input, chatbot], [chatbot])
+        image_prompt.submit(generate_image_callback, [image_prompt], [image_output])
 
-# Launch the Gradio app
+# Launch Gradio app
 demo.launch()