Create app.py

app.py

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+from transformers import MBartForConditionalGeneration, MBart50Tokenizer, AutoModelForCausalLM, AutoTokenizer, pipeline
+import gradio as gr
+import requests
+import io
+from PIL import Image
+import os
+
+# Set up the Hugging Face API key from environment variables
+hf_api_key = os.getenv("new_hf_token")
+if not hf_api_key:
+    raise ValueError("Hugging Face API key not found! Please set the 'HF_API_KEY' environment variable.")
+headers = {"Authorization": f"Bearer {hf_api_key}"}
+
+# Define the text-to-image model URL
+API_URL = "https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4"
+
+# Load the translation model and tokenizer
+translation_model_name = "facebook/mbart-large-50-many-to-one-mmt"
+tokenizer = MBart50Tokenizer.from_pretrained(translation_model_name)
+translation_model = MBartForConditionalGeneration.from_pretrained(translation_model_name)
+
+# Load a text generation model from Hugging Face
+text_generation_model_name = "EleutherAI/gpt-neo-2.7B"  # Use "EleutherAI/gpt-j-6B" for better quality
+text_tokenizer = AutoTokenizer.from_pretrained(text_generation_model_name)
+text_model = AutoModelForCausalLM.from_pretrained(text_generation_model_name, device_map="auto", torch_dtype=torch.float32)
+
+# Create a pipeline for text generation
+text_generator = pipeline("text-generation", model=text_model, tokenizer=text_tokenizer)
+
+# Function to generate an image using Hugging Face's text-to-image model
+def generate_image_from_text(translated_text):
+    try:
+        # Send the translated text to the text-to-image model
+        response = requests.post(API_URL, headers=headers, json={"inputs": translated_text})
+        
+        # Check if the response is successful
+        if response.status_code != 200:
+            return None, f"Error generating image: {response.text}"
+        
+        # Read and return the generated image
+        image_bytes = response.content
+        image = Image.open(io.BytesIO(image_bytes))
+        return image, None
+    except Exception as e:
+        return None, f"Error during image generation: {e}"
+
+# Define the function to translate Tamil text, generate an image, and create a descriptive text
+def translate_generate_image_and_text(tamil_text):
+    try:
+        # Step 1: Translate Tamil text to English
+        tokenizer.src_lang = "ta_IN"
+        inputs = tokenizer(tamil_text, return_tensors="pt")
+        translated_tokens = translation_model.generate(**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
+        translated_text = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
+    except Exception as e:
+        return f"Error during translation: {e}", None, None
+
+    try:
+        # Step 2: Use the translated English text to generate an image directly
+        image, error_message = generate_image_from_text(translated_text)
+        if error_message:
+            return translated_text, None, error_message
+    except Exception as e:
+        return translated_text, None, f"Error during image generation: {e}"
+
+    try:
+        # Step 3: Generate a descriptive English text using GPT-Neo based on the translated text
+        descriptive_text = text_generator(translated_text, max_length=100, num_return_sequences=1, temperature=0.7, top_p=0.9)[0]['generated_text']
+    except Exception as e:
+        return translated_text, image, f"Error during text generation: {e}"
+
+    return translated_text, image, descriptive_text
+
+# Gradio interface setup
+iface = gr.Interface(
+    fn=translate_generate_image_and_text,
+    inputs=gr.Textbox(lines=2, placeholder="Enter Tamil text here..."),
+    outputs=[gr.Textbox(label="Translated English Text"), 
+             gr.Image(label="Generated Image"),
+             gr.Textbox(label="Generated Descriptive Text")],
+    title="Tamil to English Translation, Image Creation, and Descriptive Text Generation",
+    description="Translate Tamil text to English using Facebook's mbart-large-50 model, create an image using the translated text, and generate a descriptive text based on the translated content.",
+)
+
+# Launch the Gradio app
+iface.launch()