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Update app.py
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app.py
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@@ -11,12 +11,12 @@ model_name = "facebook/mbart-large-50-many-to-one-mmt"
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tokenizer = MBart50Tokenizer.from_pretrained(model_name)
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model = MBartForConditionalGeneration.from_pretrained(model_name)
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# Use
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text_gen_model = "EleutherAI/gpt-
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pipe = pipeline(
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"text-generation",
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model=text_gen_model,
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torch_dtype=torch.float32,
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device_map="auto"
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)
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@@ -32,17 +32,9 @@ def translate_and_generate_image(tamil_text):
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translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
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translated_text = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
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# Step 2: Generate
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prompt = f"Create a detailed description based on the following text: {translated_text}"
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generated_text = pipe(
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prompt,
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max_length=100,
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num_return_sequences=1,
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temperature=0.7,
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top_p=0.9,
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top_k=50,
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truncation=True
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)[0]['generated_text']
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# Step 3: Use the generated English text to create an image
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def query(payload):
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@@ -63,7 +55,7 @@ iface = gr.Interface(
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gr.Textbox(label="Generated Descriptive Text"),
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gr.Image(label="Generated Image")],
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title="Tamil to English Translation, Text Generation, and Image Creation",
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description="Translate Tamil text to English using Facebook's mbart-large-50 model, generate
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)
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# Launch Gradio app
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tokenizer = MBart50Tokenizer.from_pretrained(model_name)
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model = MBartForConditionalGeneration.from_pretrained(model_name)
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# Use a more powerful text generation model, e.g., GPT-J-6B
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text_gen_model = "EleutherAI/gpt-j-6B" # Or use 'EleutherAI/gpt-neox-20b' for better results
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pipe = pipeline(
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"text-generation",
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model=text_gen_model,
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torch_dtype=torch.float32,
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device_map="auto"
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)
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translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
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translated_text = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
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# Step 2: Generate high-quality English text using GPT-J
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prompt = f"Create a detailed description based on the following text: {translated_text}"
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generated_text = pipe(prompt, max_length=150, temperature=0.7, top_p=0.9, top_k=50, truncation=True)[0]['generated_text']
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# Step 3: Use the generated English text to create an image
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def query(payload):
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gr.Textbox(label="Generated Descriptive Text"),
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gr.Image(label="Generated Image")],
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title="Tamil to English Translation, Text Generation, and Image Creation",
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description="Translate Tamil text to English using Facebook's mbart-large-50 model, generate high-quality text using GPT-J, and create an image using the generated text.",
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)
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# Launch Gradio app
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