Update app.py

app.py

@@ -7,72 +7,64 @@ import io
 import os
 from typing import Tuple
 
-# Load Hugging Face API key securely
-HF_API_KEY = os.getenv("HF_API_KEY")  # You must set this as an environment variable
+# Load HF token
+HF_API_KEY = os.getenv("HF_API_KEY") or "your_hf_token_here"  # Replace this with your token if local
 if not HF_API_KEY:
-    raise ValueError("HF_API_KEY is not set. Add it in Hugging Face 'Variables and Secrets' or local environment.")
+    raise ValueError("HF_API_KEY is not set.")
 
-# API Endpoint for Image Generation
+# Hugging Face image model
 IMAGE_GEN_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
 HEADERS = {"Authorization": f"Bearer {HF_API_KEY}"}
 
-# Check if GPU is available
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-# Load Tamil-to-English Translation Model
+# Translation model (Tamil to English)
 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
+# 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
+generator_tokenizer.pad_token = generator_tokenizer.eos_token
 
 def translate_tamil_to_english(text: str) -> str:
-    """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: str) -> str:
-    """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)
+    output = generator.generate(**inputs, max_length=100, num_return_sequences=1)
     return generator_tokenizer.decode(output[0], skip_special_tokens=True)
 
 def generate_image(prompt: str) -> Image.Image:
-    """Sends request to API for image generation."""
     response = requests.post(IMAGE_GEN_URL, headers=HEADERS, json={"inputs": prompt})
     try:
         if response.status_code == 200 and response.headers["content-type"].startswith("image"):
             return Image.open(io.BytesIO(response.content))
     except Exception as e:
-        print("Image generation error:", e)
-    return Image.new("RGB", (300, 300), "red")  # Fallback placeholder image
+        print("Image generation failed:", e)
+    return Image.new("RGB", (300, 300), color="gray")
 
 def process_input(tamil_text: str) -> Tuple[str, str, Image.Image]:
-    """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
 
-# 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.",
-    allow_flagging="never"  # Avoids schema-related error in Spaces
-)
+# Gradio app
+with gr.Blocks() as demo:
+    gr.Markdown("## Tamil to English Translator with Text and Image Generator")
 
-# Launch the app
-interface.launch()
+    tamil_input = gr.Textbox(label="Enter Tamil Text")
+    translate_btn = gr.Button("Translate & Generate")
+
+    english_output = gr.Textbox(label="Translated English")
+    creative_output = gr.Textbox(label="Creative Text")
+    image_output = gr.Image(label="Generated Image")
+
+    translate_btn.click(fn=process_input, inputs=tamil_input, outputs=[english_output, creative_output, image_output])
+
+demo.launch()