Create app.py

app.py

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+from transformers import MarianMTModel, AutoModelForSeq2SeqLM, AutoTokenizer, GPTNeoForCausalLM, GPT2Tokenizer
+import gradio as gr
+import requests
+import io
+from PIL import Image
+import os  # Import os to access environment variables
+
+# Load MarianMT model and tokenizer for Tamil to English translation
+model_name = "Helsinki-NLP/opus-mt-mul-en"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Load GPT-Neo model and tokenizer
+gpt_neo_model_name = "EleutherAI/gpt-neo-125M"
+gpt_neo_model = GPTNeoForCausalLM.from_pretrained(gpt_neo_model_name)
+gpt_neo_tokenizer = GPT2Tokenizer.from_pretrained(gpt_neo_model_name)
+
+# Retrieve the API URL and headers for Flux.1 from environment variables
+API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
+headers = {"Authorization": f"Bearer {os.environ.get('HUGGINGFACE_API_KEY')}"}  # Use the environment variable
+
+def generate_image_from_text(english_text):
+    payload = {"inputs": english_text}
+    response = requests.post(API_URL, headers=headers, json=payload)
+    
+    if response.status_code == 200:
+        image_bytes = response.content
+        image = Image.open(io.BytesIO(image_bytes))
+        return image
+    else:
+        return None  # Handle error appropriately
+
+def translate_tamil_to_english(tamil_text):
+    # Tokenize input and generate translation
+    inputs = tokenizer(tamil_text, return_tensors="pt", padding=True)
+    translated_tokens = model.generate(**inputs)
+    translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
+    return translated_text
+
+def generate_creative_text(english_text):
+    input_ids = gpt_neo_tokenizer.encode(english_text, return_tensors='pt')
+    output = gpt_neo_model.generate(input_ids, max_length=150, num_return_sequences=1)
+    return gpt_neo_tokenizer.decode(output[0], skip_special_tokens=True)
+
+def process_input(tamil_text):
+    # Step 1: Translate Tamil to English
+    translated_text = translate_tamil_to_english(tamil_text)
+    
+    # Step 2: Generate Image from Translated English Text
+    image = generate_image_from_text(translated_text)
+    
+    # Step 3: Generate Creative Text
+    creative_text = generate_creative_text(translated_text)
+    
+    # Return results (translated text, image, and creative text)
+    return translated_text, image, creative_text
+
+# Create a Gradio interface with input and output components
+interface = gr.Interface(
+    fn=process_input, 
+    inputs=gr.Textbox(lines=2, placeholder="Enter Tamil text..."), 
+    outputs=[gr.Textbox(label="Translated Text (English)"), 
+             gr.Image(label="Generated Image"), 
+             gr.Textbox(label="Creative Text")],
+    title="Tamil to Creative Text & Image Generator",
+    description="Enter Tamil text to translate, generate an image, and produce creative content."
+)
+
+# Launch the Gradio app
+interface.launch(debug=True)