Create app.py

app.py

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+# Import necessary libraries
+import requests
+import io
+from PIL import Image
+import matplotlib.pyplot as plt
+from transformers import MarianMTModel, MarianTokenizer, pipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+
+# Constants for model names and API URLs
+class Constants:
+    TRANSLATION_MODEL_NAME = "Helsinki-NLP/opus-mt-mul-en"
+    IMAGE_GENERATION_API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
+    GPT_NEO_MODEL_NAME = "EleutherAI/gpt-neo-125M"
+    HEADERS = {"Authorization": "Bearer hf_token"}
+    
+
+
+# Translation Class
+class Translator:
+    def __init__(self):
+        self.tokenizer = MarianTokenizer.from_pretrained(Constants.TRANSLATION_MODEL_NAME)
+        self.model = MarianMTModel.from_pretrained(Constants.TRANSLATION_MODEL_NAME)
+        self.pipeline = pipeline("translation", model=self.model, tokenizer=self.tokenizer)
+
+    def translate(self, tamil_text):
+        """Translate Tamil text to English."""
+        try:
+            translation = self.pipeline(tamil_text, max_length=40)
+            return translation[0]['translation_text']
+        except Exception as e:
+            return f"Translation error: {str(e)}"
+
+
+# Image Generation Class
+class ImageGenerator:
+    def __init__(self):
+        self.api_url = Constants.IMAGE_GENERATION_API_URL
+
+    def generate(self, prompt):
+        """Generate an image based on the given prompt."""
+        try:
+            response = requests.post(self.api_url, headers=Constants.HEADERS, json={"inputs": prompt})
+            if response.status_code == 200:
+                image_bytes = response.content
+                return Image.open(io.BytesIO(image_bytes))
+            else:
+                print(f"Image generation failed: Status code {response.status_code}")
+                return None
+        except Exception as e:
+            print(f"Image generation error: {str(e)}")
+            return None
+
+
+# Creative Text Generation Class
+class CreativeTextGenerator:
+    def __init__(self):
+        self.tokenizer = AutoTokenizer.from_pretrained(Constants.GPT_NEO_MODEL_NAME)
+        self.model = AutoModelForCausalLM.from_pretrained(Constants.GPT_NEO_MODEL_NAME)
+
+    def generate(self, translated_text):
+        """Generate creative text based on translated text."""
+        input_ids = self.tokenizer(translated_text, return_tensors='pt').input_ids
+        generated_text_ids = self.model.generate(input_ids, max_length=100)
+        return self.tokenizer.decode(generated_text_ids[0], skip_special_tokens=True)
+
+
+# Main Application Class
+class TransArtApp:
+    def __init__(self):
+        self.translator = Translator()
+        self.image_generator = ImageGenerator()
+        self.creative_text_generator = CreativeTextGenerator()
+
+    def process(self, tamil_text):
+        """Handle the full workflow: translate, generate image, and creative text."""
+        translated_text = self.translator.translate(tamil_text)
+        image = self.image_generator.generate(translated_text)
+        creative_text = self.creative_text_generator.generate(translated_text)
+        return translated_text, creative_text, image
+
+
+# Function to display images
+def show_image(image):
+    """Display an image using matplotlib."""
+    if image:
+        plt.imshow(image)
+        plt.axis('off')  # Hide axes
+        plt.show()
+    else:
+        print("No image to display.")
+
+
+# Create an instance of the TransArt app
+app = TransArtApp()
+
+# Gradio interface function
+def gradio_interface(tamil_text):
+    """Interface function for Gradio."""
+    translated_text, creative_text, image = app.process(tamil_text)
+    return translated_text, creative_text, image
+
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=gradio_interface,
+    inputs="text",
+    outputs=["text", "text", "image"],
+    title="Tamil to English Translation, Image Generation & Creative Text",
+    description="Enter Tamil text to translate to English, generate an image, and create creative text based on the translation."
+)
+
+# Launch Gradio app
+if __name__ == "__main__":
+    interface.launch()