Update app.py

app.py

@@ -1,135 +1,135 @@
-import gradio as gr
-import gradio as gr
-from groq import Groq
-import os
-from deep_translator import GoogleTranslator
-from deep_translator import GoogleTranslator  # Import the GoogleTranslator class
-import whisper
-import gradio as gr
-from groq import Groq
-import os
-from deep_translator import GoogleTranslator # Import the GoogleTranslator class
-import pickle
-import whisper
-from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler
-import matplotlib.pyplot as plt
-import torch
-from huggingface_hub import hf_hub_download
-from safetensors.torch import load_file
-
-
-# Replace with your actual API key
-api_key = "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"
-client = Groq(api_key=api_key)
-
-# Load the custom model for image generation
-base = "stabilityai/stable-diffusion-xl-base-1.0"
-repo = "ByteDance/SDXL-Lightning"
-ckpt = "sdxl_lightning_4step_unet.safetensors"  # Ensure the correct checkpoint
-
-# Load the custom UNet and set up the pipeline
-unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cpu", torch.float16)
-unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cpu"))
-pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cpu")
-pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
-
-
-# Function to transcribe, translate, and generate an image
-def process_audio(audio_path, generate_image):
-    if audio_path is None:
-        return "Please upload an audio file.", None, None
-
-    # Step 1: Transcribe audio
-    try:
-        with open(audio_path, "rb") as file:
-            transcription = client.audio.transcriptions.create(
-                file=(os.path.basename(audio_path), file.read()),
-                model="whisper-large-v3",
-                language="ta",
-                response_format="verbose_json",
-            )
-        tamil_text = transcription.text
-    except Exception as e:
-        return f"An error occurred during transcription: {str(e)}", None, None
-
-    # Step 2: Translate Tamil to English
-    try:
-        translator = GoogleTranslator(source='ta', target='en')
-        translation = translator.translate(tamil_text)
-    except Exception as e:
-        return tamil_text, f"An error occurred during translation: {str(e)}", None
-
-    # Step 3: Generate image (if selected)
-    if generate_image:
-        try:
-            # Use the custom model and pipeline to generate an image
-            img = pipe(translation, num_inference_steps=4, guidance_scale=0).images[0]
-            return tamil_text, translation, img
-        except Exception as e:
-            return tamil_text, translation, f"An error occurred during image generation: {str(e)}"
-
-    return tamil_text, translation, None
-
-
-# Function for direct prompt to image generation
-def generate_image_from_prompt(prompt):
-    try:
-        img = pipe(prompt, num_inference_steps=4, guidance_scale=0).images[0]
-        return img
-    except Exception as e:
-        return f"An error occurred during image generation: {str(e)}"
-
-
-# Assuming your 'process_audio' and 'generate_image_from_prompt' functions are defined elsewhere
-
-# Gradio interface with the requested customizations
-with gr.Blocks(css="""
-    .gradio-container {background-color: #D8D2C2;} 
-    .btn-red {background-color: red; color: white;} 
-    .gr-button:hover {color: white !important;} 
-    .gr-button {color: black !important;} 
-    .gr-textbox {color: black !important;}
-    .gr-Tab {color: black !important;}  /* Tab text color set to black */
-""") as iface:
-    
-    # Title
-    gr.Markdown("<h1 style='text-align: center; color:black;'>TransArt - Multimodal Application</h1>")
-
-    # First Tab: Audio to Text -> Image
-    with gr.Tab("Audio to Text"):
-        gr.Markdown("<h3 style='text-align: center; color:black;'>Upload audio file, translate and generate an image</h3>")
-        
-        # Audio input and processing button
-        with gr.Row():
-            audio_input = gr.Audio(type="filepath", label="Upload Audio File")
-            generate_image_checkbox = gr.Checkbox(label="Generate Image", value=False)
-        
-        # Outputs for transcription, translation, and image
-        outputs = [
-            gr.Textbox(label="Tamil Transcription"),
-            gr.Textbox(label="English Translation"),
-            gr.Image(label="Generated Image")  # Expecting an image output
-        ]
-        
-        # Button for processing audio
-        btn = gr.Button("Proceed Audio", elem_classes="btn-red")
-        # Bind the correct function that returns transcription, translation, and an image
-        btn.click(fn=process_audio, inputs=[audio_input, generate_image_checkbox], outputs=outputs)
-    
-    # Second Tab: Direct Prompt to Image Generation
-    with gr.Tab("Prompt to Image"):
-        gr.Markdown("<h3 style='text-align: center; color:black;'>Input a prompt and generate an image</h3>")
-        
-        # Text input for the prompt
-        prompt_input = gr.Textbox(label="Enter Prompt", placeholder="Enter the scene description here...", lines=5)
-
-        # Image output
-        image_output = gr.Image(label="Generated Image")  # Expecting an image output
-
-        # Button for generating the image from the prompt
-        btn_image = gr.Button("Proceed Image Generation", elem_classes="btn-red")
-        # Bind the correct function that returns an image
-        btn_image.click(fn=generate_image_from_prompt, inputs=prompt_input, outputs=image_output)
-
-# Launch the interface
-iface.launch(server_name="0.0.0.0")
+import gradio as gr
+import gradio as gr
+from groq import Groq
+import os
+from deep_translator import GoogleTranslator
+from deep_translator import GoogleTranslator  # Import the GoogleTranslator class
+import whisper
+import gradio as gr
+from groq import Groq
+import os
+from deep_translator import GoogleTranslator # Import the GoogleTranslator class
+import pickle
+import whisper
+from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler
+
+import torch
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+
+
+# Replace with your actual API key
+api_key = "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"
+client = Groq(api_key=api_key)
+
+# Load the custom model for image generation
+base = "stabilityai/stable-diffusion-xl-base-1.0"
+repo = "ByteDance/SDXL-Lightning"
+ckpt = "sdxl_lightning_4step_unet.safetensors"  # Ensure the correct checkpoint
+
+# Load the custom UNet and set up the pipeline
+unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cpu", torch.float16)
+unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cpu"))
+pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cpu")
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
+
+
+# Function to transcribe, translate, and generate an image
+def process_audio(audio_path, generate_image):
+    if audio_path is None:
+        return "Please upload an audio file.", None, None
+
+    # Step 1: Transcribe audio
+    try:
+        with open(audio_path, "rb") as file:
+            transcription = client.audio.transcriptions.create(
+                file=(os.path.basename(audio_path), file.read()),
+                model="whisper-large-v3",
+                language="ta",
+                response_format="verbose_json",
+            )
+        tamil_text = transcription.text
+    except Exception as e:
+        return f"An error occurred during transcription: {str(e)}", None, None
+
+    # Step 2: Translate Tamil to English
+    try:
+        translator = GoogleTranslator(source='ta', target='en')
+        translation = translator.translate(tamil_text)
+    except Exception as e:
+        return tamil_text, f"An error occurred during translation: {str(e)}", None
+
+    # Step 3: Generate image (if selected)
+    if generate_image:
+        try:
+            # Use the custom model and pipeline to generate an image
+            img = pipe(translation, num_inference_steps=4, guidance_scale=0).images[0]
+            return tamil_text, translation, img
+        except Exception as e:
+            return tamil_text, translation, f"An error occurred during image generation: {str(e)}"
+
+    return tamil_text, translation, None
+
+
+# Function for direct prompt to image generation
+def generate_image_from_prompt(prompt):
+    try:
+        img = pipe(prompt, num_inference_steps=4, guidance_scale=0).images[0]
+        return img
+    except Exception as e:
+        return f"An error occurred during image generation: {str(e)}"
+
+
+# Assuming your 'process_audio' and 'generate_image_from_prompt' functions are defined elsewhere
+
+# Gradio interface with the requested customizations
+with gr.Blocks(css="""
+    .gradio-container {background-color: #D8D2C2;} 
+    .btn-red {background-color: red; color: white;} 
+    .gr-button:hover {color: white !important;} 
+    .gr-button {color: black !important;} 
+    .gr-textbox {color: black !important;}
+    .gr-Tab {color: black !important;}  /* Tab text color set to black */
+""") as iface:
+    
+    # Title
+    gr.Markdown("<h1 style='text-align: center; color:black;'>TransArt - Multimodal Application</h1>")
+
+    # First Tab: Audio to Text -> Image
+    with gr.Tab("Audio to Text"):
+        gr.Markdown("<h3 style='text-align: center; color:black;'>Upload audio file, translate and generate an image</h3>")
+        
+        # Audio input and processing button
+        with gr.Row():
+            audio_input = gr.Audio(type="filepath", label="Upload Audio File")
+            generate_image_checkbox = gr.Checkbox(label="Generate Image", value=False)
+        
+        # Outputs for transcription, translation, and image
+        outputs = [
+            gr.Textbox(label="Tamil Transcription"),
+            gr.Textbox(label="English Translation"),
+            gr.Image(label="Generated Image")  # Expecting an image output
+        ]
+        
+        # Button for processing audio
+        btn = gr.Button("Proceed Audio", elem_classes="btn-red")
+        # Bind the correct function that returns transcription, translation, and an image
+        btn.click(fn=process_audio, inputs=[audio_input, generate_image_checkbox], outputs=outputs)
+    
+    # Second Tab: Direct Prompt to Image Generation
+    with gr.Tab("Prompt to Image"):
+        gr.Markdown("<h3 style='text-align: center; color:black;'>Input a prompt and generate an image</h3>")
+        
+        # Text input for the prompt
+        prompt_input = gr.Textbox(label="Enter Prompt", placeholder="Enter the scene description here...", lines=5)
+
+        # Image output
+        image_output = gr.Image(label="Generated Image")  # Expecting an image output
+
+        # Button for generating the image from the prompt
+        btn_image = gr.Button("Proceed Image Generation", elem_classes="btn-red")
+        # Bind the correct function that returns an image
+        btn_image.click(fn=generate_image_from_prompt, inputs=prompt_input, outputs=image_output)
+
+# Launch the interface
+iface.launch(server_name="0.0.0.0")