Update app.py

app.py

@@ -5,6 +5,7 @@ import requests
 import io
 from PIL import Image
 import os
+import time  # Importing time to add delays for sequential execution
 
 # Set up your OpenAI API key (make sure it's stored as an environment variable)
 openai_api_key = os.getenv("OPENAI_API_KEY")
@@ -19,7 +20,7 @@ tokenizer = MBart50Tokenizer.from_pretrained(model_name)
 model = MBartForConditionalGeneration.from_pretrained(model_name)
 
 # Use the Hugging Face API key from environment variables for text-to-image model
-hf_api_key = os.getenv("full_token")
+hf_api_key = os.getenv("hf_token")
 if hf_api_key is None:
     raise ValueError("Hugging Face API key not found! Please set 'hf_token' environment variable.")
 else:
@@ -30,6 +31,7 @@ API_URL = "https://api-inference.huggingface.co/models/ZB-Tech/Text-to-Image"
 # Define the OpenAI GPT-3 text generation function with error handling
 def generate_with_gpt3(prompt, max_tokens=150, temperature=0.7):
     try:
+        print("Generating text with GPT-3...")
         response = openai.Completion.create(
             engine="text-davinci-003",  # Use "text-davinci-003" for high-quality outputs
             prompt=prompt,
@@ -39,39 +41,53 @@ def generate_with_gpt3(prompt, max_tokens=150, temperature=0.7):
             frequency_penalty=0.0,
             presence_penalty=0.0
         )
-        return response.choices[0].text.strip()
+        generated_text = response.choices[0].text.strip()
+        print("Text generation completed.")
+        return generated_text
     except Exception as e:
         print(f"OpenAI API Error: {e}")
         return "Error generating text with GPT-3. Check the OpenAI API settings."
 
 # Define the translation, GPT-3 text generation, and image generation function
 def translate_and_generate_image(tamil_text):
+    # Step 1: Translate Tamil text to English using mbart-large-50
     try:
-        # Step 1: Translate Tamil text to English using mbart-large-50
+        print("Translating Tamil text to English...")
         tokenizer.src_lang = "ta_IN"
         inputs = tokenizer(tamil_text, return_tensors="pt")
         translated_tokens = 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]
+        print(f"Translation completed: {translated_text}")
     except Exception as e:
         return "Error during translation: " + str(e), "", None
 
+    # Ensure sequential flow by waiting before moving to the next step
+    time.sleep(1)  # Optional: Add a small delay to ensure proper execution order
+
+    # Step 2: Generate high-quality descriptive text using OpenAI's GPT-3
     try:
-        # Step 2: Generate high-quality descriptive text using OpenAI's GPT-3
+        print("Generating descriptive text from translated English text...")
         prompt = f"Create a detailed and creative description based on the following text: {translated_text}"
         generated_text = generate_with_gpt3(prompt, max_tokens=150, temperature=0.7)
+        print(f"Text generation completed: {generated_text}")
     except Exception as e:
         return translated_text, f"Error during text generation: {e}", None
 
+    # Ensure sequential flow by waiting before moving to the next step
+    time.sleep(1)  # Optional: Add a small delay to ensure proper execution order
+
+    # Step 3: Use the generated English text to create an image
     try:
-        # Step 3: Use the generated English text to create an image
+        print("Generating image from the generated descriptive text...")
         def query(payload):
             response = requests.post(API_URL, headers=headers, json=payload)
             response.raise_for_status()  # Raise error if request fails
             return response.content
 
-        # Generate image using the generated text
+        # Generate image using the descriptive text
         image_bytes = query({"inputs": generated_text})
         image = Image.open(io.BytesIO(image_bytes))
+        print("Image generation completed.")
     except Exception as e:
         return translated_text, generated_text, f"Error during image generation: {e}"