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Update app.py
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app.py
CHANGED
@@ -21,10 +21,10 @@ def translate_text(tamil_text):
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translation = translation_tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
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return translation
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def query_gpt_neo(translated_text
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prompt = f"Continue the story based on the following text: {translated_text}"
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inputs = gpt_tokenizer(prompt, return_tensors="pt")
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outputs = gpt_model.generate(inputs['input_ids'], max_length=
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creative_text = gpt_tokenizer.decode(outputs[0], skip_special_tokens=True)
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return creative_text
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@@ -42,13 +42,13 @@ def query_image(payload):
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else:
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return f"Error: {response.status_code} - {response.text}"
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def process_input(tamil_input
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try:
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# Translate the input text
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translated_output = translate_text(tamil_input)
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# Generate creative text using GPT-Neo
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creative_output = query_gpt_neo(translated_output
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# Generate an image using Hugging Face's FLUX model
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image_bytes = query_image({"inputs": translated_output})
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@@ -58,22 +58,17 @@ def process_input(tamil_input, max_words):
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except Exception as e:
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return f"Error occurred: {str(e)}", "", None
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# Create a Gradio interface
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interface = gr.Interface(
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fn=process_input,
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inputs=[
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gr.Textbox(label="Input Tamil Text", placeholder="Enter your Tamil text here..."),
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gr.Slider(label="Max Words for Creative Text", minimum=50, maximum=200, step=10, value=100)
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],
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outputs=[
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gr.Textbox(label="Translated Text"),
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gr.Textbox(label="Creative Text"),
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gr.Image(label="Generated Image")
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],
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title="TRANSART
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description="Enter Tamil text to translate to English
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theme="compact", # Use the 'compact' theme for a cleaner app look
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layout="vertical" # Arrange components vertically for better readability
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)
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interface.launch()
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translation = translation_tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
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return translation
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def query_gpt_neo(translated_text):
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prompt = f"Continue the story based on the following text: {translated_text}"
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inputs = gpt_tokenizer(prompt, return_tensors="pt")
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outputs = gpt_model.generate(inputs['input_ids'], max_length=100, num_return_sequences=1)
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creative_text = gpt_tokenizer.decode(outputs[0], skip_special_tokens=True)
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return creative_text
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else:
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return f"Error: {response.status_code} - {response.text}"
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def process_input(tamil_input):
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try:
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# Translate the input text
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translated_output = translate_text(tamil_input)
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# Generate creative text using GPT-Neo
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creative_output = query_gpt_neo(translated_output)
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# Generate an image using Hugging Face's FLUX model
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image_bytes = query_image({"inputs": translated_output})
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except Exception as e:
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return f"Error occurred: {str(e)}", "", None
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# Create a Gradio interface
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interface = gr.Interface(
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fn=process_input,
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inputs=[gr.Textbox(label="Input Tamil Text")],
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outputs=[
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gr.Textbox(label="Translated Text"),
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gr.Textbox(label="Creative Text"),
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gr.Image(label="Generated Image")
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],
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title="TRANSART",
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description="Enter Tamil text to translate to English and generate an image based on the translated text."
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)
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interface.launch()
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