Update app.py

app.py

@@ -14,14 +14,6 @@ hf_token = os.getenv("HF_TOK")  # Set your Hugging Face token in your environmen
 login(hf_token)  # Log in using the Hugging Face token
 
 model = genai.GenerativeModel("gemini-1.5-flash")
-diffusion_model = InferenceApi(repo_id="black-forest-labs/FLUX.1-schnell")
-
-def generate_response(tamil_text):
-    translated_text = translate_text(tamil_text)
-    creative_result = model.generate_content('poem about'+translated_text)
-    response = diffusion_model(inputs=translated_text, 
-                                params={"guidance_scale": 7.5, "num_inference_steps": 50})
-    return translated_text, creative_result.text, response
 
 # Load the pre-trained model for Tamil to English translation
 translator_model_name = "Helsinki-NLP/opus-mt-mul-en"
@@ -35,21 +27,22 @@ def translate_text(tamil_text):
     return tokenizer.decode(translated[0], skip_special_tokens=True)
 
 def generate_creative_writing(english_text):
-    # Placeholder for creative writing generation
-    # Replace with your preferred model or API call
-    return model.generate_content('poem about'+english_text).text
+    # Generate creative writing
+    return 'model.generate_content('poem about ' + english_text).text'
 
 def generate_image(prompt):
     # Make an inference request to generate an image
     response = diffusion_model(inputs=prompt, params={"guidance_scale": 7.5, "num_inference_steps": 50})
-    return response  # Assuming the response is a URL to the generated image
+    return response  # Ensure this is the correct format
 
 def process_input(tamil_text):
-    # Process input for translation, creative writing, and image generation
-    translated_text = translate_text(tamil_text)
-    creative_response = generate_creative_writing(translated_text)
-    generated_image = generate_image(translated_text)
-    return translated_text, creative_response, generated_image
+    try:
+        translated_text = translate_text(tamil_text)
+        creative_response = generate_creative_writing(translated_text)
+        generated_image = generate_image(translated_text)
+        return translated_text, creative_response, generated_image
+    except Exception as e:
+        return str(e), "Error occurred during processing", None
 
 # Create a Gradio interface
 iface = gr.Interface(
@@ -62,3 +55,4 @@ iface = gr.Interface(
 
 # Launch the Gradio app
 iface.launch()
+