Update app.py

app.py

@@ -7,7 +7,11 @@ from PIL import Image
 import os
 
 # Set up your OpenAI API key (make sure it's stored as an environment variable)
-openai.api_key = os.getenv("OPENAI_API_KEY")
+openai_api_key = os.getenv("OPENAI_API_KEY")
+if openai_api_key is None:
+    raise ValueError("OpenAI API key not found! Please set 'OPENAI_API_KEY' environment variable.")
+else:
+    openai.api_key = openai_api_key
 
 # Load the translation model and tokenizer
 model_name = "facebook/mbart-large-50-many-to-one-mmt"
@@ -15,42 +19,61 @@ 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("hf_token")
+if hf_api_key is None:
+    raise ValueError("Hugging Face API key not found! Please set 'hf_token' environment variable.")
+else:
+    headers = {"Authorization": f"Bearer {hf_api_key}"}
+
 API_URL = "https://api-inference.huggingface.co/models/ZB-Tech/Text-to-Image"
-headers = {"Authorization": f"Bearer {os.getenv('full_token')}"}
 
-# Define the OpenAI GPT-3 text generation function
+# Define the OpenAI GPT-3 text generation function with error handling
 def generate_with_gpt3(prompt, max_tokens=150, temperature=0.7):
-    response = openai.Completion.create(
-        engine="text-davinci-003",  # You can also use "text-davinci-002" or "curie"
-        prompt=prompt,
-        max_tokens=max_tokens,
-        temperature=temperature,
-        top_p=0.9,
-        frequency_penalty=0.0,
-        presence_penalty=0.0
-    )
-    return response.choices[0].text.strip()
+    try:
+        response = openai.Completion.create(
+            engine="text-davinci-003",  # Use "text-davinci-003" for high-quality outputs
+            prompt=prompt,
+            max_tokens=max_tokens,
+            temperature=temperature,
+            top_p=0.9,
+            frequency_penalty=0.0,
+            presence_penalty=0.0
+        )
+        return response.choices[0].text.strip()
+    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
-    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]
+    try:
+        # Step 1: Translate Tamil text to English using mbart-large-50
+        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]
+    except Exception as e:
+        return "Error during translation: " + str(e), "", None
 
-    # Step 2: Generate high-quality descriptive text using OpenAI's GPT-3
-    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)
+    try:
+        # Step 2: Generate high-quality descriptive text using OpenAI's GPT-3
+        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)
+    except Exception as e:
+        return translated_text, f"Error during text generation: {e}", None
 
-    # Step 3: Use the generated English text to create an image
-    def query(payload):
-        response = requests.post(API_URL, headers=headers, json=payload)
-        return response.content
+    try:
+        # Step 3: Use the generated English text to create an image
+        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
-    image_bytes = query({"inputs": generated_text})
-    image = Image.open(io.BytesIO(image_bytes))
+        # Generate image using the generated text
+        image_bytes = query({"inputs": generated_text})
+        image = Image.open(io.BytesIO(image_bytes))
+    except Exception as e:
+        return translated_text, generated_text, f"Error during image generation: {e}"
 
     return translated_text, generated_text, image