Update app.py

app.py

@@ -1,40 +1,148 @@
+# app.py
+
+# Install the required libraries before running this script:
+# pip install transformers gradio Pillow requests torch
+
+import os
+import requests
+from transformers import MarianMTModel, MarianTokenizer, AutoModelForCausalLM, AutoTokenizer
+from PIL import Image, ImageDraw
+import io
 import gradio as gr
-from transformers import pipeline
+import torch
+
+# Detect if GPU is available
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Load the MarianMT model and tokenizer for translation (Tamil to English)
+model_name = "Helsinki-NLP/opus-mt-mul-en"
+translation_model = MarianMTModel.from_pretrained(model_name).to(device)
+translation_tokenizer = MarianTokenizer.from_pretrained(model_name)
+
+# Load GPT-Neo for creative text generation
+text_generation_model_name = "EleutherAI/gpt-neo-1.3B"
+text_generation_model = AutoModelForCausalLM.from_pretrained(text_generation_model_name).to(device)
+text_generation_tokenizer = AutoTokenizer.from_pretrained(text_generation_model_name)
+
+# Add padding token to GPT-Neo tokenizer if not present
+if text_generation_tokenizer.pad_token is None:
+    text_generation_tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+
+# Set your Hugging Face API key here or set the environment variable before running:
+# export HF_API_KEY='your_actual_api_key'  # on Linux/Mac
+# setx HF_API_KEY "your_actual_api_key"    # on Windows cmd (restart terminal after)
+api_key = os.getenv('HF_API_KEY')
+if api_key is None:
+    raise ValueError("Hugging Face API key is not set. Please set it as environment variable HF_API_KEY.")
+
+headers = {"Authorization": f"Bearer {api_key}"}
 
-# Load models
-translation_pipeline = pipeline("translation", model="Helsinki-NLP/opus-mt-mul-en")
-text_generation_pipeline = pipeline("text-generation", model="gpt2")
+# Define the API URL for image generation (replace with actual model URL)
+API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"  # Replace if you want a different model
 
-# Simulated image generation (replace with Hugging Face Diffusers or similar if needed)
-def generate_image(prompt: str) -> str:
-    # You can integrate actual image generation here
-    return f"https://via.placeholder.com/512?text={prompt.replace(' ', '+')}"
+# Query Hugging Face API to generate image with error handling
+def query(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    if response.status_code != 200:
+        print(f"Error: Received status code {response.status_code}")
+        print(f"Response: {response.text}")
+        return None
+    return response.content
 
-# Main function
-def multimodal_pipeline(tamil_text: str):
+# Translate Tamil text to English
+def translate_text(tamil_text):
+    inputs = translation_tokenizer(tamil_text, return_tensors="pt", padding=True, truncation=True).to(device)
+    translated_tokens = translation_model.generate(**inputs)
+    translation = translation_tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
+    return translation
+
+# Generate an image based on the translated text with error handling
+def generate_image(prompt):
+    image_bytes = query({"inputs": prompt})
+
+    if image_bytes is None:
+        # Return a blank image with error message
+        error_img = Image.new('RGB', (300, 300), color=(255, 0, 0))
+        d = ImageDraw.Draw(error_img)
+        d.text((10, 150), "Image Generation Failed", fill=(255, 255, 255))
+        return error_img
+
+    try:
+        image = Image.open(io.BytesIO(image_bytes))
+        return image
+    except Exception as e:
+        print(f"Error: {e}")
+        # Return an error image in case of failure
+        error_img = Image.new('RGB', (300, 300), color=(255, 0, 0))
+        d = ImageDraw.Draw(error_img)
+        d.text((10, 150), "Invalid Image Data", fill=(255, 255, 255))
+        return error_img
+
+# Generate creative text based on the translated English text
+def generate_creative_text(translated_text):
+    inputs = text_generation_tokenizer(translated_text, return_tensors="pt", padding=True, truncation=True).to(device)
+    generated_tokens = text_generation_model.generate(**inputs, max_length=100)
+    creative_text = text_generation_tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
+    return creative_text
+
+# Function to handle the full workflow
+def translate_generate_image_and_text(tamil_text):
     # Step 1: Translate Tamil to English
-    translated = translation_pipeline(tamil_text)[0]["translation_text"]
+    translated_text = translate_text(tamil_text)
+
+    # Step 2: Generate an image from the translated text
+    image = generate_image(translated_text)
+
+    # Step 3: Generate creative text from the translated text
+    creative_text = generate_creative_text(translated_text)
+
+    return translated_text, creative_text, image
 
-    # Step 2: Generate English text
-    generated = text_generation_pipeline(translated, max_length=50, do_sample=True)[0]["generated_text"]
+# CSS styling for the Gradio app
+css = """
+#transart-title {
+    font-size: 2.5em;
+    font-weight: bold;
+    color: #4CAF50;
+    text-align: center;
+    margin-bottom: 10px;
+}
+#transart-subtitle {
+    font-size: 1.25em;
+    text-align: center;
+    color: #555555;
+    margin-bottom: 20px;
+}
+body {
+    background-color: #f0f0f5;
+}
+.gradio-container {
+    font-family: 'Arial', sans-serif;
+}
+"""
 
-    # Step 3: Generate Image (simulate)
-    image_url = generate_image(generated)
+# Title and subtitle HTML for Gradio markdown
+title_markdown = """
+# <div id="transart-title">TransArt</div>
+### <div id="transart-subtitle">Tamil to English Translation, Creative Text & Image Generation</div>
+"""
 
-    return translated, generated, image_url
+# Build Gradio interface
+with gr.Blocks(css=css) as interface:
+    gr.Markdown(title_markdown)
+    with gr.Row():
+        with gr.Column():
+            tamil_input = gr.Textbox(label="Enter Tamil Text", placeholder="Type Tamil text here...", lines=3)
+        with gr.Column():
+            translated_output = gr.Textbox(label="Translated Text", interactive=False)
+            creative_text_output = gr.Textbox(label="Creative Generated Text", interactive=False)
+            generated_image_output = gr.Image(label="Generated Image")
 
-# Gradio Interface
-interface = gr.Interface(
-    fn=multimodal_pipeline,
-    inputs=gr.Textbox(label="Enter Tamil Text", placeholder="உங்கள் தமிழ் உரையை இங்கே உள்ளிடவும்"),
-    outputs=[
-        gr.Textbox(label="English Translation"),
-        gr.Textbox(label="Generated Prompt"),
-        gr.Image(label="Generated Image"),
-    ],
-    title="Tamil to Image Multimodal App",
-    description="This app translates Tamil to English, generates a descriptive sentence, and creates an image based on it."
-)
+    gr.Button("Generate").click(
+        fn=translate_generate_image_and_text,
+        inputs=tamil_input,
+        outputs=[translated_output, creative_text_output, generated_image_output],
+    )
 
 if __name__ == "__main__":
-    interface.launch()
+    interface.launch(debug=True, server_name="0.0.0.0")