Update app.py
Browse files
app.py
CHANGED
@@ -1,36 +1,32 @@
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import gradio as gr
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from transformers import pipeline
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from diffusers import StableDiffusionPipeline
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import torch
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# 1. Tamil to English Translator
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translator = pipeline("translation", model="Helsinki-NLP/opus-mt-
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# 2. English Text Generator
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generator = pipeline("text-generation", model="gpt2")
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# 3. Image Generator using Stable Diffusion
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device = "cuda" if torch.cuda.is_available() else "cpu"
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image_pipe = StableDiffusionPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-4",
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torch_dtype=torch.float16 if device == "cuda" else torch.float32
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)
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image_pipe = image_pipe.to(device)
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# π Combined function
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def generate_image_from_tamil(tamil_input):
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# Step 1: Translate Tamil β English
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translated = translator(tamil_input, max_length=100)[0]['translation_text']
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# Step 2: Generate English sentence based on translated input
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generated = generator(translated, max_length=50, num_return_sequences=1)[0]['generated_text']
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# Step 3: Generate Image based on English text
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image = image_pipe(generated).images[0]
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return translated, generated, image
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# π¨ Gradio UI
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iface = gr.Interface(
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fn=generate_image_from_tamil,
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inputs=gr.Textbox(lines=2, label="Enter Tamil Text"),
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@@ -39,8 +35,8 @@ iface = gr.Interface(
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gr.Textbox(label="Generated English Prompt"),
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gr.Image(label="Generated Image")
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],
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title="Tamil to Image Generator
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description="Translates Tamil β English, generates
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)
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iface.launch()
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import gradio as gr
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from transformers import pipeline
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from diffusers import StableDiffusionPipeline
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import torch
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import os
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device = "cuda" if torch.cuda.is_available() else "cpu"
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HF_TOKEN = os.getenv("HUGGINGFACE_TOKEN") # Make sure to set your token in env variables
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# 1. Tamil to English Translator
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translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ta-en")
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# 2. English Text Generator
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generator = pipeline("text-generation", model="gpt2")
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# 3. Image Generator using Stable Diffusion with token
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image_pipe = StableDiffusionPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-4",
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use_auth_token=HF_TOKEN,
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torch_dtype=torch.float16 if device == "cuda" else torch.float32
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)
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image_pipe = image_pipe.to(device)
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def generate_image_from_tamil(tamil_input):
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translated = translator(tamil_input, max_length=100)[0]['translation_text']
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generated = generator(translated, max_length=50, num_return_sequences=1)[0]['generated_text']
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image = image_pipe(generated).images[0]
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return translated, generated, image
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iface = gr.Interface(
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fn=generate_image_from_tamil,
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inputs=gr.Textbox(lines=2, label="Enter Tamil Text"),
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gr.Textbox(label="Generated English Prompt"),
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gr.Image(label="Generated Image")
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],
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title="Tamil to Image Generator",
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description="Translates Tamil β English, generates text β creates image using Stable Diffusion."
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)
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iface.launch()
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