Update app.py

app.py

@@ -1,25 +1,22 @@
 import requests
 import io
-import os
 from PIL import Image
 import gradio as gr
 from transformers import MarianMTModel, MarianTokenizer
+import os
 
 model_name = "Helsinki-NLP/opus-mt-mul-en"
 model = MarianMTModel.from_pretrained(model_name)
 tokenizer = MarianTokenizer.from_pretrained(model_name)
 
-gemini_api_key = os.getenv("GEMINI_API_KEY")
-huggingface_api_key = os.getenv("HUGGINGFACE_API_KEY")
-
-
 def translate_text(tamil_text):
     inputs = tokenizer(tamil_text, return_tensors="pt")
     translated_tokens = model.generate(**inputs)
     translation = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
     return translation
 
-def query_gemini_api(translated_text, gemini_api_key, max_output_tokens=300):
+def query_gemini_api(translated_text, max_output_tokens=300):
+    gemini_api_key = os.getenv('GEMINI_API_KEY')
     url = f"https://generativelanguage.googleapis.com/v1beta2/models/gemini-1.5:generateText?key={gemini_api_key}"
     headers = {"Content-Type": "application/json"}
     payload = {
@@ -38,23 +35,25 @@ def query_gemini_api(translated_text, gemini_api_key, max_output_tokens=300):
         return f"Error: {response.status_code} - {response.text}"
 
 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"
     headers = {"Authorization": f"Bearer {huggingface_api_key}"}
     response = requests.post(API_URL, headers=headers, json=payload)
     return response.content
 
-
-def process_input(tamil_input):
+def process_input(tamil_input, max_output_tokens):
     translated_output = translate_text(tamil_input)
-    creative_output = query_gemini_api(translated_output)
+    creative_output = query_gemini_api(translated_output, max_output_tokens)
     image_bytes = query_image({"inputs": translated_output})
     image = Image.open(io.BytesIO(image_bytes))
     return translated_output, creative_output, image
 
-
 iface = gr.Interface(
     fn=process_input,
-    inputs=gr.Textbox(label="Input Tamil Text"),
+    inputs=[
+        gr.Textbox(label="Input Tamil Text"),
+        gr.Slider(50, 1000, value=300, step=50, label="Max Output Tokens for Creative Text")
+    ],
     outputs=[
         gr.Textbox(label="Translated Text"),
         gr.Textbox(label="Creative Text"),