Create app.py

app.py

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+# ==============================
+# SECTION 1 — INSTALL + IMPORTS
+# ==============================
+
+import torch
+import gradio as gr
+from PIL import Image
+from transformers import pipeline, BlipProcessor, BlipForQuestionAnswering
+import lpips
+import clip
+from bert_score import score
+import torchvision.transforms as T
+from sentence_transformers import SentenceTransformer
+from rouge_score import rouge_scorer
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+def free_gpu_cache():
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
+# ==============================
+# SECTION 2 — LOAD LIGHTWEIGHT MODELS
+# ==============================
+blip_large_captioner = pipeline(
+    "image-to-text",
+    model="Salesforce/blip-image-captioning-large",
+    device=0 if device=="cuda" else -1
+)
+
+vit_gpt2_captioner = pipeline(
+    "image-to-text",
+    model="nlpconnect/vit-gpt2-image-captioning",
+    device=0 if device=="cuda" else -1
+)
+
+# --- NLP Pipelines ---
+sentiment_model = pipeline("sentiment-analysis")
+ner_model = pipeline("ner", aggregation_strategy="simple")
+topic_model = pipeline("zero-shot-classification",
+                       model="facebook/bart-large-mnli")
+
+# --- Metrics ---
+clip_model, clip_preprocess = clip.load("ViT-B/32", device=device)
+lpips_model = lpips.LPIPS(net='alex').to(device)
+lpips_transform = T.Compose([T.ToTensor(), T.Resize((128,128))])
+sentence_model = SentenceTransformer("all-MiniLM-L6-v2")  # for cosine similarity
+
+# ==============================
+# SECTION 2b — LAZY LOAD HEAVY MODELS
+# ==============================
+blip2_captioner = None
+vqa_processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
+vqa_model = None
+
+def get_blip2():
+    global blip2_captioner
+    if blip2_captioner is None:
+        blip2_captioner = pipeline(
+            "image-to-text",
+            model="Salesforce/blip2-opt-2.7b",
+            device=0 if device=="cuda" else -1
+        )
+    return blip2_captioner
+
+def get_vqa_model():
+    global vqa_model
+    if vqa_model is None:
+        vqa_model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base").to(device)
+    return vqa_model
+
+# ==============================
+# SECTION 3 — FUNCTIONS
+# ==============================
+def make_captions(img):
+    captions = []
+    try: captions.append(blip_large_captioner(img)[0]["generated_text"])
+    except: captions.append("BLIP-large failed.")
+    try: captions.append(vit_gpt2_captioner(img)[0]["generated_text"])
+    except: captions.append("ViT-GPT2 failed.")
+    try:
+        blip2 = get_blip2()
+        captions.append(blip2(img)[0]["generated_text"])
+    except: captions.append("BLIP2-opt failed.")
+    return captions
+
+# ---------------- Metrics Computation ---------------------
+def compute_metrics_button(images, captions, idx1, idx2):
+    # CLIP similarity
+    img1_clip = clip_preprocess(images[idx1]).unsqueeze(0).to(device)
+    img2_clip = clip_preprocess(images[idx2]).unsqueeze(0).to(device)
+    with torch.no_grad():
+        feat1 = clip_model.encode_image(img1_clip)
+        feat2 = clip_model.encode_image(img2_clip)
+        clip_sim = float(torch.cosine_similarity(feat1, feat2).item())
+    
+    # LPIPS
+    img1_lp = lpips_transform(images[idx1]).unsqueeze(0).to(device) * 2 - 1
+    img2_lp = lpips_transform(images[idx2]).unsqueeze(0).to(device) * 2 - 1
+    with torch.no_grad():
+        lpips_score = float(lpips_model(img1_lp, img2_lp).item())
+    
+    # BERTScore
+    _, _, F1 = score([captions[idx1]], [captions[idx2]], lang="en", verbose=False)
+    bert_f1 = float(F1.mean().item())
+    
+    # Cosine similarity of embeddings
+    emb1 = sentence_model.encode([captions[idx1]])
+    emb2 = sentence_model.encode([captions[idx2]])
+    cosine_sim = float(cosine_similarity(emb1, emb2)[0][0])
+    
+    # Jaccard similarity
+    tokens1 = set(captions[idx1].lower().split())
+    tokens2 = set(captions[idx2].lower().split())
+    jaccard_sim = float(len(tokens1 & tokens2) / len(tokens1 | tokens2))
+    
+    # ROUGE
+    scorer = rouge_scorer.RougeScorer(['rouge1','rougeL'], use_stemmer=True)
+    rouge_scores = scorer.score(captions[idx1], captions[idx2])
+    
+    return f"""
+**Metrics Comparison**
+- CLIP Similarity: {clip_sim:.4f}
+- LPIPS Score: {lpips_score:.4f}
+- BERTScore F1: {bert_f1:.4f}
+- Cosine Similarity: {cosine_sim:.4f}
+- Jaccard Similarity: {jaccard_sim:.4f}
+- ROUGE-1: {rouge_scores['rouge1'].fmeasure:.4f}
+- ROUGE-L: {rouge_scores['rougeL'].fmeasure:.4f}
+"""
+
+# ---- NLP ----
+def nlp_bundle(caption):
+    try:
+        sentiment = sentiment_model(caption)
+        sentiment = "<br>".join([f"{s['label']}: {s['score']:.2f}" for s in sentiment])
+    except: sentiment = "Sentiment failed."
+
+    try:
+        ents_list = ner_model(caption)
+        ents = "<br>".join([f"{e['entity_group']}: {e['word']}" for e in ents_list]) or "None"
+    except: ents = "NER failed."
+
+    try:
+        topics_raw = topic_model(caption, candidate_labels=["people","animals","objects","food","nature"])
+        topics = "<br>".join([f"{lbl}: {float(scr):.2f}" for lbl, scr in zip(topics_raw["labels"], topics_raw["scores"])])
+    except: topics = "Topics failed."
+
+    return sentiment, ents, topics
+
+# ---------------- VQA ----------------
+def answer_vqa(question, image):
+    if image is None or question.strip() == "":
+        return "Upload an image and enter a question."
+    model = get_vqa_model()
+    inputs = vqa_processor(images=image, text=question, return_tensors="pt").to(device)
+    with torch.no_grad():
+        generated_ids = model.generate(**inputs)
+        answer = vqa_processor.decode(generated_ids[0], skip_special_tokens=True)
+    free_gpu_cache()
+    return answer
+
+# Convert a PIL.Image to PNG byte stream
+def to_bytes(img):
+    import io
+    buf = io.BytesIO()
+    img.save(buf, format="PNG")
+    return buf.getvalue()
+
+# ==============================
+# SECTION 4 — UI (GRADIO)
+# ==============================
+def build_ui():
+    with gr.Blocks(title="Multimodal AI Image Studio") as demo:
+
+        gr.HTML("""
+        <style>
+        .heading-orange h2, .heading-orange h3 { color: #ff5500 !important; }
+        .orange-btn button { background-color:#ff5500; color:white; border-radius:6px; height:36px; font-weight:bold; }
+        .teal-btn button { background-color:#008080; color:white; border-radius:6px; height:36px; font-weight:bold; }
+        .loading-line {
+            height:4px; background:linear-gradient(90deg,#008080 0%,#00cccc 50%,#008080 100%);
+            background-size:200% 100%; animation: loading 1s linear infinite;
+        }
+        @keyframes loading { 0% {background-position:200% 0;} 100% {background-position:-200% 0;} }
+        .circular-img img {
+        border-radius: 21%;
+        object-fit: cover;
+        width: 400px;
+        height: 200px;
+        box-shadow: inset -10px -10px 30px rgba(255,255,255,0.3),
+                5px 5px 15px rgba(0,0,0,0.3);
+        border: 2px solid rgba(255,255,255,0.6);
+        }
+        </style>
+        """)
+
+        gr.Markdown("## Multimodal AI Image Studio: Comparative Image-to-Text Analysis", elem_classes="heading-orange")
+        images_state = gr.State([])
+        captions_state = gr.State([])
+
+        # ---------------- Image Input ----------------
+        gr.Markdown("### Select Image Source", elem_classes="heading-orange")
+        with gr.Tabs():
+            with gr.Tab("📁 Upload Image"):
+                upload_input = gr.Image(type="pil", sources=["upload"], label="Upload Image", height=900, width=960, elem_classes="circular-img")
+                upload_btn = gr.Button("Generate Captions", elem_classes="orange-btn")
+            with gr.Tab("📷 Webcam"):
+                webcam_input = gr.Image(type="pil", sources=["webcam"], label="Webcam", height=900, width=960, elem_classes="circular-img")
+                webcam_btn = gr.Button("Capture & Generate Captions", elem_classes="orange-btn")
+            with gr.Tab("🔗 From URL"):
+                url_input = gr.Textbox(label="Paste Image URL")
+                url_btn = gr.Button("Fetch & Generate Captions", elem_classes="orange-btn")
+
+        # ---------------- Previews ----------------
+        with gr.Row():
+            with gr.Column(scale=1, min_width=200):
+                preview1 = gr.Image(type="pil",label="Preview 1", interactive=False, height=230)
+                blip_caption_box = gr.Markdown()
+            with gr.Column(scale=1, min_width=200):
+                preview2 = gr.Image(type="pil",label="Preview 2", interactive=False, height=230)
+                vit_caption_box = gr.Markdown()
+            with gr.Column(scale=1, min_width=200):
+                preview3 = gr.Image(type="pil",label="Preview 3", interactive=False, height=230)
+                blip2_caption_box = gr.Markdown()
+
+        # ---------------- Generate Captions ----------------
+        def generate_all(img, images_state, captions_state):
+            if img is None:
+                return (None, None, None, "No image.", "No image.", "No image.", [], [])
+            captions = make_captions(img)
+            return (img, img, img, captions[0], captions[1], captions[2], [img], captions)
+
+        upload_btn.click(generate_all, inputs=[upload_input, images_state, captions_state],
+                         outputs=[preview1, preview2, preview3, blip_caption_box, vit_caption_box, blip2_caption_box, images_state, captions_state])
+        webcam_btn.click(generate_all, inputs=[webcam_input, images_state, captions_state],
+                         outputs=[preview1, preview2, preview3, blip_caption_box, vit_caption_box, blip2_caption_box, images_state, captions_state])
+
+        def load_from_url(url, images_state, captions_state):
+            import requests
+            from io import BytesIO
+            try:
+                img = Image.open(BytesIO(requests.get(url).content))
+            except:
+                return (None, None, None, "Bad URL.", "Bad URL.", "Bad URL.", [], [])
+            return generate_all(img, images_state, captions_state)
+
+        url_btn.click(load_from_url, inputs=[url_input, images_state, captions_state],
+                      outputs=[preview1, preview2, preview3, blip_caption_box, vit_caption_box, blip2_caption_box, images_state, captions_state])
+
+        # ---------------- Metrics ----------------
+        gr.Markdown("### Compute Pairwise Metrics", elem_classes="heading-orange")
+        metrics_btn = gr.Button("Compute Metrics for All Pairs", elem_classes="teal-btn")
+        metrics_A = gr.Markdown()
+        metrics_B = gr.Markdown()
+        metrics_C = gr.Markdown()
+
+        def compute_metrics_all_pairs_ui(images, captions):
+            yield ("<div class='loading-line'></div>", "<div class='loading-line'></div>", "<div class='loading-line'></div>")
+            if len(images) < 1 or len(captions) < 3:
+                msg = "Upload 1 image and generate all 3 captions."
+                yield msg, msg, msg
+                return
+            imgs = images * 3
+            A = compute_metrics_button(imgs, captions, 0, 1)
+            B = compute_metrics_button(imgs, captions, 0, 2)
+            C = compute_metrics_button(imgs, captions, 1, 2)
+            yield (f"**BLIP-large ↔ ViT-GPT2**<br>{A}",
+                   f"**BLIP-large ↔ BLIP2**<br>{B}",
+                   f"**ViT-GPT2 ↔ BLIP2**<br>{C}")
+
+        metrics_btn.click(compute_metrics_all_pairs_ui, inputs=[images_state, captions_state],
+                          outputs=[metrics_A, metrics_B, metrics_C])
+
+        # ---------------- NLP ----------------
+        gr.Markdown("### NLP Analysis", elem_classes="heading-orange")
+        nlp_btn = gr.Button("Analyze Captions", elem_classes="teal-btn")
+        nlp_out = gr.HTML()
+
+        def do_nlp(captions):
+            yield "<div class='loading-line'></div>"
+            if len(captions) < 3:
+                yield "<b>All captions required.</b>"
+                return
+            labels = ["BLIP-large", "ViT-GPT2", "BLIP2"]
+            blocks = []
+            for label, cap in zip(labels, captions):
+                s, e, t = nlp_bundle(cap)
+                block = f"""
+                <div style='flex:1;padding:10px;min-width:240px;'>
+                    <h3><u>{label}</u></h3>
+                    <b>Sentiment</b><br>{s}<br><br>
+                    <b>Entities</b><br>{e}<br><br>
+                    <b>Topics</b><br>{t}
+                </div>
+                """
+                blocks.append(block)
+            yield f"<div style='display:flex; gap:20px;'>{''.join(blocks)}</div>"
+
+        nlp_btn.click(do_nlp, inputs=[captions_state], outputs=[nlp_out])
+
+        # ---------------- VQA ----------------
+        gr.Markdown("### Visual Question Answering (VQA)", elem_classes="heading-orange")
+        with gr.Row():
+            vqa_input = gr.Textbox(label="Ask about the image")
+            vqa_btn = gr.Button("Get Answer", elem_classes="teal-btn")
+            vqa_out = gr.Markdown()
+
+        def vqa_ui(question, image):
+            yield "<div class='loading-line'></div>"
+            yield answer_vqa(question, image)
+
+        vqa_btn.click(vqa_ui, inputs=[vqa_input, preview1], outputs=[vqa_out])
+
+    return demo
+
+# ==============================
+# LAUNCH
+# ==============================
+demo = build_ui()
+demo.launch(share=True, debug=False)