Update app.py

app.py

@@ -9,19 +9,20 @@ 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
 client_gemma = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
 
-# Functions for chat and image handling
 def llava(inputs, history):
-    """Processes image + text input with Llava."""
+    """Processes an image and 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 for input."""
+    """Generate a response based on text or image input."""
     if "files" in message and message["files"]:
+        # Handle image + text input
         inputs = llava(message, history)
         streamer = TextIteratorStreamer(skip_prompt=True, skip_special_tokens=True)
         thread = Thread(target=model.generate, kwargs=dict(inputs=inputs, max_new_tokens=512, streamer=streamer))
@@ -31,53 +32,45 @@ def respond(message, history):
             buffer += new_text
             yield buffer
     else:
+        # Handle text-only input
         user_message = message["text"]
-        history.append([user_message, None])
+        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
+        history[-1][1] = bot_message  # Update the last entry with bot's response
         yield history
 
 def generate_image(prompt):
-    """Generates an image."""
+    """Generates an image based on the user prompt."""
     client = InferenceClient("KingNish/Image-Gen-Pro")
     return client.predict("Image Generation", None, prompt, api_name="/image_gen_pro")
 
-# State management to control visibility
-def show_page(page, state):
-    """Updates the state to show the selected page."""
-    return {"chat_visible": page == "chat", "image_visible": page == "image"}
-
-# Gradio app setup
-with gr.Blocks(title="AI Chat & Tools") as demo:
-    state = gr.State({"chat_visible": True, "image_visible": False})
-
+# Set up Gradio interface
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
     with gr.Row():
-        with gr.Column(scale=1, min_width=200):
-            gr.Markdown("## Navigation")
-            chat_button = gr.Button("Chat Interface")
-            image_button = gr.Button("Image Generation")
-
-        with gr.Column(scale=3):
-            with gr.Row(visible=lambda state: state["chat_visible"], interactive=True):
-                gr.Markdown("## Chat with AI Assistant")
-                chatbot = gr.Chatbot(label="Chat", show_label=False)
-                text_input = gr.Textbox(placeholder="Enter your message...", lines=2, show_label=False)
-                file_input = gr.File(label="Upload an image", file_types=["image/*"])
-                text_input.submit(respond, [text_input, chatbot], [chatbot])
-                file_input.change(respond, [file_input, chatbot], [chatbot])
+        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")
 
-            with gr.Row(visible=lambda state: state["image_visible"], interactive=True):
-                gr.Markdown("## Image Generator")
-                image_prompt = gr.Textbox(placeholder="Describe the image to generate", show_label=False)
-                image_output = gr.Image(label="Generated Image")
-                image_prompt.submit(generate_image, [image_prompt], [image_output])
+    def handle_text(text, history=[]):
+        """Handle text input and generate responses."""
+        return respond({"text": text}, history), history
 
-    # Button actions to switch between pages
-    chat_button.click(lambda: show_page("chat", state.value), None, state)
-    image_button.click(lambda: show_page("image", state.value), None, state)
+    def handle_file_upload(files, history=[]):
+        """Handle file uploads and generate responses."""
+        return respond({"files": files, "text": "Describe this image."}, history), history
 
-# Launch the app
-demo.launch()
+    # Connect components to callbacks
+    text_input.submit(handle_text, [text_input, chatbot], [chatbot])
+    file_input.change(handle_file_upload, [file_input, chatbot], [chatbot])
 
+# Launch the Gradio app
+demo.launch()