Create app.py

app.py

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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()