Update app.py

app.py

@@ -10,9 +10,9 @@ model_name = "Helsinki-NLP/opus-mt-mul-en"
 model = MarianMTModel.from_pretrained(model_name)
 tokenizer = MarianTokenizer.from_pretrained(model_name)
 
-# Define a function for language translation
+
 def translate_text(input_text, language):
-    # Dictionary mapping the selected language to the respective language tag
+    
     language_map = {
         "Tamil": "ta",
         "French": "fr",
@@ -20,17 +20,17 @@ def translate_text(input_text, language):
         "German": "de"
     }
     
-    # Prepare input with the appropriate language code for the model
-    lang_prefix = f">>{language_map[language]}<< "  # Add the prefix needed for the model
+    
+    lang_prefix = f">>{language_map[language]}<< 
     text_with_lang = lang_prefix + input_text
     inputs = tokenizer(text_with_lang, return_tensors="pt", padding=True)
     
-    # Perform translation
+    
     translated_tokens = model.generate(**inputs)
     translation = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
     return translation
 
-# Function to query the Gemini API for creative text generation
+
 def query_gemini_api(translated_text, gemini_api_key):
     url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-latest:generateContent"
     headers = {"Content-Type": "application/json"}
@@ -47,7 +47,7 @@ def query_gemini_api(translated_text, gemini_api_key):
     else:
         return f"Error: {response.status_code} - {response.text}"
 
-# Function to query the image generation API
+
 def query_image(payload):
     huggingface_api_key = os.getenv('HUGGINGFACE_API_KEY')
     API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
@@ -55,17 +55,17 @@ def query_image(payload):
     response = requests.post(API_URL, headers=headers, json=payload)
     return response.content
 
-# Function to process the entire input
+
 def process_input(input_text, language):
-    gemini_api_key = os.getenv('GEMINI_API_KEY')  # Use your Gemini API key
-    translated_output = translate_text(input_text, language)  # Translate text from selected language to English
-    creative_output = query_gemini_api(translated_output, gemini_api_key)  # Generate creative text
-    image_bytes = query_image({"inputs": translated_output})  # Generate an image based on the translated text
-    image = Image.open(io.BytesIO(image_bytes))  # Convert bytes to image
+    gemini_api_key = os.getenv('GEMINI_API_KEY')  
+    translated_output = translate_text(input_text, language)  
+    creative_output = query_gemini_api(translated_output, gemini_api_key)  
+    image_bytes = query_image({"inputs": translated_output})  
+    image = Image.open(io.BytesIO(image_bytes))  
     
     return translated_output, creative_output, image
 
-# Create Gradio Interface
+
 iface = gr.Interface(
     fn=process_input,
     inputs=[gr.Textbox(label="Enter text in the selected language"),