Upload app.py

app.py

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+# -----------------------------
+# app.py
+# -----------------------------
+
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import torch.nn.functional as F
+
+# -----------------------------
+# Load the fine-tuned model
+# -----------------------------
+MODEL_REPO = "umarfarzan/deberta-best-clipworthiness"  # replace with your HF repo
+tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_REPO)
+model.eval()
+
+# -----------------------------
+# Prediction function with colored output
+# -----------------------------
+def predict_viral(transcript):
+    """
+    Takes a transcript and predicts Viral or Not Viral
+    """
+    inputs = tokenizer(transcript, padding="max_length", truncation=True, max_length=512, return_tensors="pt")
+    
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        probs = F.softmax(logits, dim=-1)
+    
+    pred_label = torch.argmax(probs, dim=1).item()
+    pred_prob = probs[0, pred_label].item()
+    
+    if pred_label == 1:
+        label_str = f"<span style='color:green;font-weight:bold;'>Viral</span> ({pred_prob*100:.2f}% confidence)"
+    else:
+        label_str = f"<span style='color:red;font-weight:bold;'>Not Viral</span> ({pred_prob*100:.2f}% confidence)"
+    
+    return label_str
+
+# -----------------------------
+# Gradio Interface
+# -----------------------------
+with gr.Blocks(title="Acliptic - Revolutionising The Future Of Clipping") as demo:
+    
+    gr.Markdown("<h1 style='text-align:center;color:#4B0082;'>Acliptic</h1>")
+    gr.Markdown("<h3 style='text-align:center;color:#6A5ACD;'>Revolutionising The Future Of Clipping</h3>")
+    gr.Markdown("---")
+    
+    with gr.Row():
+        transcript_input = gr.Textbox(
+            lines=10,
+            placeholder="Paste your transcript here...",
+            label="Transcript"
+        )
+    
+    predict_btn = gr.Button("Predict Viral Potential")
+    
+    result_output = gr.HTML(label="Prediction")
+    
+    predict_btn.click(fn=predict_viral, inputs=transcript_input, outputs=result_output)
+
+# Launch the interface
+demo.launch()