Update app.py

app.py

@@ -5,11 +5,12 @@ from PIL import Image
 import torch
 import io
 import os
+from typing import Tuple
 
 # Load Hugging Face API key securely
-HF_API_KEY = os.getenv("HF_API_KEY")   # give token
+HF_API_KEY = os.getenv("HF_API_KEY")  # You must set this as an environment variable
 if not HF_API_KEY:
-    raise ValueError("HF_API_KEY is not set. Add it in Hugging Face 'Variables and Secrets'.")
+    raise ValueError("HF_API_KEY is not set. Add it in Hugging Face 'Variables and Secrets' or local environment.")
 
 # API Endpoint for Image Generation
 IMAGE_GEN_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
@@ -30,45 +31,36 @@ generator_tokenizer = AutoTokenizer.from_pretrained(generator_model)
 if generator_tokenizer.pad_token is None:
     generator_tokenizer.pad_token = generator_tokenizer.eos_token
 
-def translate_tamil_to_english(text):
+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):
+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)
     return generator_tokenizer.decode(output[0], skip_special_tokens=True)
 
-def generate_image(prompt):
+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})
-    if response.status_code == 200:
-        return Image.open(io.BytesIO(response.content))
-    return Image.new("RGB", (300, 300), "red")  # Placeholder image for errors
+    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
 
-def process_input(tamil_text):
+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
 
-# Create 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."
-# )
+# Gradio Interface
 interface = gr.Interface(
     fn=process_input,
     inputs=gr.Textbox(label="Enter Tamil Text"),
@@ -78,7 +70,9 @@ interface = gr.Interface(
         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."
+    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
 )
-# Launch the Gradio app
+
+# Launch the app
 interface.launch()