Update app.py

app.py

@@ -689,62 +689,96 @@ def generate_map(location_names):
     map_html = m._repr_html_()
     return map_html
 
-#Flux Code
-
-import spaces
-import gradio as gr
-import torch
-from PIL import Image
 from diffusers import DiffusionPipeline
-import random
+import torch
+
+# Load the FLUX model
+def load_flux_model():
+    pipeline = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell")
+    if torch.cuda.is_available():
+        pipeline = pipeline.to("cuda")
+    return pipeline
+
+flux_pipeline = load_flux_model()
+
+# Function to generate an image based on a selected prompt
+def generate_flux_image(selected_prompt):
+    prompt_map = {
+        "Toyota Truck in Birmingham": "A high quality cinematic image for Toyota Truck in Birmingham skyline shot in the style of Michael Mann",
+        "Alabama Quarterback": "A high quality cinematic image for Alabama Quarterback close up emotional shot in the style of Michael Mann",
+        "Taylor Swift Concert": "A high quality cinematic image for Taylor Swift concert in Birmingham skyline style of Michael Mann"
+    }
+    prompt = prompt_map.get(selected_prompt, "")
+    if not prompt:
+        return None  # In case no prompt is matched
+
+    # Generate the image using the FLUX model
+    image = flux_pipeline(prompt).images[0]
+    
+    # Save the image to a temporary file
+    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as f:
+        image.save(f.name)
+        return f.name
+
+# Predefine the three prompts for automatic image generation
+default_prompts = [
+    "Toyota Truck in Birmingham",
+    "Alabama Quarterback",
+    "Taylor Swift Concert"
+]
+
+# Add image-related components and integrate it with Gradio
+with gr.Blocks(theme='Pijush2023/scikit-learn-pijush') as demo:
+
+    with gr.Row():
+        with gr.Column():
+            state = gr.State()
+
+            chatbot = gr.Chatbot([], elem_id="RADAR:Channel 94.1", bubble_full_width=False)
+            choice = gr.Radio(label="Select Style", choices=["Details", "Conversational"], value="Conversational")
+            retrieval_mode = gr.Radio(label="Retrieval Mode", choices=["VDB", "KGF"], value="VDB")
+            model_choice = gr.Dropdown(label="Choose Model", choices=["LM-1", "LM-2", "LM-3"], value="LM-1")
 
-# Initialize the base model and specific LoRA
-base_model = "black-forest-labs/FLUX.1-dev"
-pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
+            # Link the dropdown change to handle_model_choice_change
+            model_choice.change(fn=handle_model_choice_change, inputs=model_choice, outputs=[retrieval_mode, choice, choice])
 
-lora_repo = "XLabs-AI/flux-RealismLora"
-trigger_word = ""  # Leave trigger_word blank if not used.
-pipe.load_lora_weights(lora_repo)
+            gr.Markdown("<h1 style='color: red;'>Talk to RADAR</h1>", elem_id="voice-markdown")
 
-pipe.to("cuda")
+            chat_input = gr.Textbox(show_copy_button=True, interactive=True, show_label=False, label="ASK Radar !!!", placeholder="Hey Radar...!!")
+            tts_choice = gr.Radio(label="Select TTS System", choices=["Alpha", "Beta"], value="Alpha")
+        
+            retriever_button = gr.Button("Retriever")
 
-MAX_SEED = 2**32-1
+            clear_button = gr.Button("Clear")
+            clear_button.click(lambda: [None, None], outputs=[chat_input, state])
 
-# Hardcoded prompts for generating images
-hardcoded_prompt_1 = "A high quality cinematic image for Toyota Truck in Birmingham skyline shot in the style of Michael Mann"
-hardcoded_prompt_2 = "A high quality cinematic image for Alabama Quarterback close up emotional shot in the style of Michael Mann"
-hardcoded_prompt_3 = "A high quality cinematic image for Taylor Swift concert in Birmingham skyline style of Michael Mann"
+            gr.Markdown("<h1 style='color: red;'>Radar Map</h1>", elem_id="Map-Radar")
+            location_output = gr.HTML()
+            audio_output = gr.Audio(interactive=False, autoplay=True)
 
-def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
-    # Set random seed for reproducibility
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator(device="cuda").manual_seed(seed)
+            # Image output section
+            gr.Markdown("<h2>Generated Images on Load</h2>")
+            image_output_1 = gr.Image(type="filepath", label="Generated Image 1", width=400, height=400)
+            image_output_2 = gr.Image(type="filepath", label="Generated Image 2", width=400, height=400)
+            image_output_3 = gr.Image(type="filepath", label="Generated Image 3", width=400, height=400)
 
-    # Update progress bar (0% saat mulai)
-    progress(0, "Starting image generation...")
+            # Automatically generate and display the three images on startup using the predefined prompts
+            startup_image_1 = generate_flux_image(default_prompts[0])
+            startup_image_2 = generate_flux_image(default_prompts[1])
+            startup_image_3 = generate_flux_image(default_prompts[2])
 
-    # Generate image with progress updates
-    for i in range(1, steps + 1):
-        # Simulate the processing step (in a real scenario, you would integrate this with your image generation process)
-        if i % (steps // 10) == 0:  # Update every 10% of the steps
-            progress(i / steps * 100, f"Processing step {i} of {steps}...")
+            image_output_1.update(value=startup_image_1)
+            image_output_2.update(value=startup_image_2)
+            image_output_3.update(value=startup_image_3)
 
-    # Generate image using the pipeline
-    image = pipe(
-        prompt=f"{prompt} {trigger_word}",
-        num_inference_steps=steps,
-        guidance_scale=cfg_scale,
-        width=width,
-        height=height,
-        generator=generator,
-        joint_attention_kwargs={"scale": lora_scale},
-    ).images[0]
+        with gr.Column():
+            weather_output = gr.HTML(value=fetch_local_weather())
+            news_output = gr.HTML(value=fetch_local_news())
+            events_output = gr.HTML(value=fetch_local_events())
 
-    # Final update (100%)
-    progress(100, "Completed!")
+    # Rest of the Gradio interface setup
 
-    yield image, seed
+demo.launch(show_error=True)
 
 
 
@@ -1495,13 +1529,20 @@ with gr.Blocks(theme='Pijush2023/scikit-learn-pijush') as demo:
 
         with gr.Column():
 
-           # Display images generated using Flux with LoRA integration
-            image_output_1 = gr.Image(value=next(run_lora(hardcoded_prompt_1, 7.5, 50, True, 42, 512, 512, 0.5)), width=400, height=400)
-            image_output_2 = gr.Image(value=next(run_lora(hardcoded_prompt_2, 7.5, 50, True, 42, 512, 512, 0.5)), width=400, height=400)
-            image_output_3 = gr.Image(value=next(run_lora(hardcoded_prompt_3, 7.5, 50, True, 42, 512, 512, 0.5)), width=400, height=400)
+            # Image output section
+            gr.Markdown("<h2>Generated Images on Load</h2>")
+            image_output_1 = gr.Image(type="filepath", label="Generated Image 1", width=400, height=400)
+            image_output_2 = gr.Image(type="filepath", label="Generated Image 2", width=400, height=400)
+            image_output_3 = gr.Image(type="filepath", label="Generated Image 3", width=400, height=400)
+
+            # Automatically generate and display the three images on startup using the predefined prompts
+            startup_image_1 = generate_flux_image(default_prompts[0])
+            startup_image_2 = generate_flux_image(default_prompts[1])
+            startup_image_3 = generate_flux_image(default_prompts[2])
 
-            refresh_button = gr.Button("Refresh Images")
-            refresh_button.click(fn=update_images, inputs=None, outputs=[image_output_1, image_output_2, image_output_3])
+            image_output_1.update(value=startup_image_1)
+            image_output_2.update(value=startup_image_2)
+            image_output_3.update(value=startup_image_3)