Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 from transformers import pipeline
 from diffusers import StableDiffusionPipeline
 import torch
+from PIL import Image
 
 # --- Load NLP pipelines ---
 clf = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
@@ -15,16 +16,33 @@ det = pipeline("object-detection", model="facebook/detr-resnet-50")
 seg = pipeline("image-segmentation", model="facebook/mask2former-swin-base-coco-instance")
 
 # --- Diffusion model for text-to-image ---
-sd_pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+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")
 
+# --- Sample sentences ---
+SAMPLE_SENTENCES = [
+    "The Amazon rainforest is losing trees at an alarming rate due to deforestation.",
+    "Ocean pollution is harming marine life around the world.",
+    "Renewable energy like solar and wind can reduce global warming.",
+    "Wildlife conservation is essential to protect endangered species.",
+    "Sustainable farming practices improve soil health and reduce pollution."
+]
 
 # --- Functions ---
 def classify_text(text):
-    return clf(text)
+    result = clf(text)[0]
+    label = result['label']
+    score = result['score']
+    # Map to Positive/Negative/Neutral
+    sentiment_map = {"POSITIVE": "Positive 😊", "NEGATIVE": "Negative 😟"}
+    sentiment = sentiment_map.get(label, "Neutral 😐")
+    return {"Sentiment": sentiment, "Confidence": round(score, 3)}
 
 def ner_text(text):
-    return ner(text)
+    entities = ner(text)
+    return entities
 
 def fill_blank(text):
     return mlm(text)
@@ -45,52 +63,83 @@ def generate_image(prompt):
     image = sd_pipe(prompt).images[0]
     return image
 
-
-
 # --- Gradio Interface ---
-with gr.Blocks() as demo:
-    gr.Markdown("# 🌍 Environmental AI Toolkit")
-
-    with gr.Tab("Sentence Classification"):
-        txt_in = gr.Textbox(label="Enter text")
+with gr.Blocks(title="🌍 Environmental AI Toolkit") as demo:
+    gr.Markdown("""
+    # 🌱 Environmental AI Toolkit
+    This toolkit provides multiple AI-powered tools for environmental text and image analysis:
+    - **Sentence Classification**: Analyze environmental text sentiment (Positive/Negative)
+    - **NER**: Identify environmental entities in text
+    - **Fill-in-the-Blank**: Complete environmental sentences using AI
+    - **Question Answering**: Ask questions based on provided context
+    - **Image Classification, Detection & Segmentation**
+    - **Image Generation**: Generate environmental scenes from prompts
+    """)
+
+    with gr.Tab("🏷 Sentence Classification"):
+        gr.Markdown("""
+        ### Classify environmental sentences into sentiment
+        Enter any environmental sentence, and the tool will tell you if the sentiment is Positive 😊, Negative 😟, or Neutral 😐.
+        """)
+        txt_in = gr.Textbox(label="Enter text", placeholder="E.g., 'The Amazon rainforest is being deforested'")
         txt_out = gr.JSON(label="Classification Result")
+        sample_btn = gr.Button("Load Sample Sentence")
         txt_in.submit(classify_text, txt_in, txt_out)
+        sample_btn.click(lambda: SAMPLE_SENTENCES[0], None, txt_in)
 
-    with gr.Tab("NER"):
+    with gr.Tab("🔍 Named Entity Recognition"):
+        gr.Markdown("""
+        ### Extract named entities
+        Enter environmental text, and the model will extract entities like **locations, organizations, species**, etc.
+        """)
         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"):
+    with gr.Tab("📝 Fill-in-the-Blank"):
+        gr.Markdown("""
+        ### Complete sentences
+        Enter a sentence with `[MASK]` token, and AI will predict possible words to fill.
+        """)
         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"):
+    with gr.Tab("❓ Question Answering"):
+        gr.Markdown("""
+        ### Ask questions based on context
+        Provide context and ask a question. AI will try to answer from the given text.
+        """)
         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"):
+    with gr.Tab("🖼 Image Classification"):
+        gr.Markdown("### Upload an environmental image to classify it")
         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"):
+    with gr.Tab("🕵️ Object Detection"):
+        gr.Markdown("### Detect objects in environmental images")
         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"):
+    with gr.Tab("🧩 Segmentation"):
+        gr.Markdown("### Segment environmental images into regions")
         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"):
+    with gr.Tab("🎨 Image Generation"):
+        gr.Markdown("""
+        ### Generate environmental scenes from a text prompt
+        Describe a scene, e.g., "A lush green forest with tall trees and wildlife".
+        """)
         gen_in = gr.Textbox(label="Prompt")
         gen_out = gr.Image(label="Generated Image")
         gr.Button("Generate").click(generate_image, gen_in, gen_out)
 
-
 demo.launch()