Create app.py

app.py

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+import gradio as gr
+import requests
+from transformers import MarianMTModel, MarianTokenizer, AutoModelForCausalLM, AutoTokenizer
+from PIL import Image
+import torch
+import io
+
+# Check if GPU is available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load Tamil-to-English Translation Model
+translator_model = "Helsinki-NLP/opus-mt-mul-en"
+translator = MarianMTModel.from_pretrained(translator_model).to(device)
+translator_tokenizer = MarianTokenizer.from_pretrained(translator_model)
+
+# Load Text Generation Model
+generator_model = "EleutherAI/gpt-neo-1.3B"
+generator = AutoModelForCausalLM.from_pretrained(generator_model).to(device)
+generator_tokenizer = AutoTokenizer.from_pretrained(generator_model)
+if generator_tokenizer.pad_token is None:
+    generator_tokenizer.pad_token = generator_tokenizer.eos_token
+
+# Hugging Face API for Image Generation
+HF_API_KEY = "my_token"  # Replace with your API key
+IMAGE_GEN_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
+# Get the API key from environment variables or Hugging Face secrets
+HEADERS = {"Authorization": f"Bearer {HF_API_KEY}"}
+
+def translate_tamil_to_english(text):
+    """Translates Tamil text to English."""
+    inputs = translator_tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
+    output = translator.generate(**inputs)
+    return translator_tokenizer.decode(output[0], skip_special_tokens=True)
+
+def generate_text(prompt):
+    """Generates a creative text based on English input."""
+    inputs = generator_tokenizer(prompt, return_tensors="pt", padding=True, truncation=True).to(device)
+    output = generator.generate(**inputs, max_length=100)
+    return generator_tokenizer.decode(output[0], skip_special_tokens=True)
+
+def generate_image(prompt):
+    """Sends request to API for image generation."""
+    response = requests.post(IMAGE_GEN_URL, headers=HEADERS, json={"inputs": prompt})
+    if response.status_code == 200:
+        return Image.open(io.BytesIO(response.content))
+    return Image.new("RGB", (300, 300), "red")  # Error placeholder image
+
+def process_input(tamil_text):
+    """Complete pipeline: Translation, Text Generation, and Image Generation."""
+    english_text = translate_tamil_to_english(tamil_text)
+    creative_text = generate_text(english_text)
+    image = generate_image(english_text)
+    return english_text, creative_text, image
+
+# Create Gradio Interface
+interface = gr.Interface(
+    fn=process_input,
+    inputs=gr.Textbox(label="Enter Tamil Text"),
+    outputs=[
+        gr.Textbox(label="Translated English Text"),
+        gr.Textbox(label="Creative Text"),
+        gr.Image(label="Generated Image")
+    ],
+    title="Tamil to English Translator & Image Generator",
+    description="Enter Tamil text, and this app will translate it, generate a creative description, and create an image based on the text."
+)
+
+# Launch the Gradio app
+interface.launch()