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app.py

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+import os
+import torch
+from transformers import MarianMTModel, MarianTokenizer
+import google.generativeai as genai
+from huggingface_hub import InferenceApi, login
+import gradio as gr
+
+# Load API key from environment variable
+os.environ["API_KEY"] = os.getenv("GOOGLE_API_KEY")  # Set your Google API key in your environment variables
+genai.configure(api_key=os.environ["API_KEY"])  # Configure Google Generative AI with the API key
+
+# Load Hugging Face token from environment variable
+hf_token = os.getenv("HF_TOK")  # Set your Hugging Face token in your environment variables
+login(hf_token)  # Log in to Hugging Face using the token
+
+# Initialize the diffusion model for image generation
+diffusion_model = InferenceApi(repo_id="black-forest-labs/FLUX.1-schnell")
+
+# Initialize the generative model for creative writing
+model = genai.GenerativeModel("gemini-1.5-flash")
+
+# Load the pre-trained model for Tamil to English translation
+translator_model_name = "Helsinki-NLP/opus-mt-mul-en"  # Model name for translation
+tokenizer = MarianTokenizer.from_pretrained(translator_model_name)  # Load the tokenizer for the translation model
+translator = MarianMTModel.from_pretrained(translator_model_name)  # Load the translation model
+
+def translate_text(tamil_text):
+    """
+    Translates Tamil text to English using the MarianMT model.
+    
+    Args:
+        tamil_text (str): Text in Tamil to be translated.
+    
+    Returns:
+        str: Translated text in English.
+    """
+    # Prepare the input for the translation model
+    inputs = tokenizer(tamil_text, return_tensors="pt", padding=True)
+    # Generate the translation
+    translated = translator.generate(**inputs)
+    # Decode the generated tokens to text
+    return tokenizer.decode(translated[0], skip_special_tokens=True)
+
+def generate_creative_writing(english_text):
+    """
+    Generates creative writing (e.g., a poem) based on the provided English text.
+    
+    Args:
+        english_text (str): Text in English to generate creative content from.
+    
+    Returns:
+        str: Generated creative writing.
+    """
+    # Generate creative writing content
+    return model.generate_content("poem about " + english_text).text
+
+def generate_image(prompt):
+    """
+    Generates an image based on the provided prompt using a diffusion model.
+    
+    Args:
+        prompt (str): Text prompt for image generation.
+    
+    Returns:
+        Image: Generated image based on the prompt.
+    """
+    # Make an inference request to generate an image
+    response = diffusion_model(inputs=prompt, params={"guidance_scale": 7.5, "num_inference_steps": 50})
+    return response  # Ensure this is in the correct format for display
+
+def process_input(tamil_text):
+    """
+    Processes the input Tamil text to perform translation, creative writing generation, 
+    and image generation.
+    
+    Args:
+        tamil_text (str): Input text in Tamil.
+    
+    Returns:
+        tuple: Translated text, creative writing response, and generated image or error message.
+    """
+    try:
+        # Translate Tamil text to English
+        translated_text = translate_text(tamil_text)
+        # Generate creative writing based on the translated text
+        creative_response = generate_creative_writing(translated_text)
+        # Generate an image based on the translated text
+        generated_image = generate_image(translated_text)
+        return translated_text, creative_response, generated_image
+    except Exception as e:
+        return str(e), "Error occurred during processing", None  # Return error information
+
+# Create a Gradio interface for user interaction
+iface = gr.Interface(
+    fn=process_input,  # Function to process input
+    inputs="text",  # Input type: text box
+    outputs=["text", "text", "image"],  # Outputs: translated text, creative writing, and image
+    title="Creative Writing and Image Generation",  # Title of the interface
+    description="Enter Tamil text to get translated text, a creative response, and an image."  # Description of the interface
+)
+
+# Launch the Gradio app
+iface.launch()  # Start the Gradio interface for user interaction

requirements.txt

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+transformers
+torch
+diffusers
+gradio
+translate
+google.generativeai
+huggingface_hub
+sentencepiece