Delete app.py

app.py

@@ -1,64 +0,0 @@
-# -----------------------------
-# 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()