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| import gradio as gr | |
| from transformers import pipeline | |
| from diffusers import StableDiffusionPipeline | |
| import torch | |
| # --- Load NLP pipelines --- | |
| clf = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english") | |
| ner = pipeline("ner", model="dslim/bert-base-NER", aggregation_strategy="simple") | |
| mlm = pipeline("fill-mask", model="bert-base-uncased") | |
| qa = pipeline("question-answering", model="distilbert-base-cased-distilled-squad") | |
| # --- Vision pipelines --- | |
| img_clf = pipeline("image-classification", model="google/vit-base-patch16-224") | |
| det = pipeline("object-detection", model="facebook/detr-resnet-50") | |
| seg = pipeline("image-segmentation", model="facebook/mask2former-swin-large-coco") | |
| # --- Diffusion model for text-to-image --- | |
| sd_pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) | |
| sd_pipe = sd_pipe.to("cuda" if torch.cuda.is_available() else "cpu") | |
| # --- Speech --- | |
| asr = pipeline("automatic-speech-recognition", model="openai/whisper-small") | |
| tts = pipeline("text-to-speech", model="espnet/kan-bayashi_ljspeech_vits") | |
| # --- Functions --- | |
| def classify_text(text): | |
| return clf(text) | |
| def ner_text(text): | |
| return ner(text) | |
| def fill_blank(text): | |
| return mlm(text) | |
| def answer_question(context, question): | |
| return qa(question=question, context=context) | |
| def classify_image(image): | |
| return img_clf(image) | |
| def detect_objects(image): | |
| return det(image) | |
| def segment_image(image): | |
| return seg(image) | |
| def generate_image(prompt): | |
| image = sd_pipe(prompt).images[0] | |
| return image | |
| def transcribe(audio): | |
| return asr(audio)["text"] | |
| def speak_text(text): | |
| audio = tts(text) | |
| return (audio["sample_rate"], audio["audio"]) | |
| # --- Gradio Interface --- | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# 🌍 Environmental AI Toolkit") | |
| with gr.Tab("Sentence Classification"): | |
| txt_in = gr.Textbox(label="Enter text") | |
| txt_out = gr.JSON(label="Classification Result") | |
| txt_in.submit(classify_text, txt_in, txt_out) | |
| with gr.Tab("NER"): | |
| ner_in = gr.Textbox(label="Enter text") | |
| ner_out = gr.JSON(label="Entities") | |
| ner_in.submit(ner_text, ner_in, ner_out) | |
| with gr.Tab("Fill-in-the-Blank"): | |
| mlm_in = gr.Textbox(label="Enter sentence with [MASK]") | |
| mlm_out = gr.JSON(label="Predictions") | |
| mlm_in.submit(fill_blank, mlm_in, mlm_out) | |
| with gr.Tab("Question Answering"): | |
| context = gr.Textbox(label="Context") | |
| question = gr.Textbox(label="Question") | |
| qa_out = gr.JSON(label="Answer") | |
| gr.Button("Answer").click(answer_question, [context, question], qa_out) | |
| with gr.Tab("Image Classification"): | |
| img_in = gr.Image(type="pil") | |
| img_out = gr.JSON(label="Labels") | |
| img_in.upload(classify_image, img_in, img_out) | |
| with gr.Tab("Object Detection"): | |
| det_in = gr.Image(type="pil") | |
| det_out = gr.JSON(label="Objects") | |
| det_in.upload(detect_objects, det_in, det_out) | |
| with gr.Tab("Segmentation"): | |
| seg_in = gr.Image(type="pil") | |
| seg_out = gr.JSON(label="Segments") | |
| seg_in.upload(segment_image, seg_in, seg_out) | |
| with gr.Tab("Image Generation"): | |
| gen_in = gr.Textbox(label="Prompt") | |
| gen_out = gr.Image(label="Generated Image") | |
| gr.Button("Generate").click(generate_image, gen_in, gen_out) | |
| with gr.Tab("Speech Recognition"): | |
| audio_in = gr.Audio(type="filepath") | |
| audio_out = gr.Textbox(label="Transcription") | |
| audio_in.change(transcribe, audio_in, audio_out) | |
| with gr.Tab("Text-to-Speech"): | |
| tts_in = gr.Textbox(label="Text to Speak") | |
| tts_out = gr.Audio(label="Generated Speech") | |
| gr.Button("Speak").click(speak_text, tts_in, tts_out) | |
| demo.launch() | |