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app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import torch.nn.functional as F
+
+# Load model & tokenizer
+model_path = "Fredaaaaaa/biobert_model"  # or "your-username/your-model-name" if from Hugging Face Hub
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model.eval()
+
+
+
+labels = ["No interaction", "Mild", "Moderate", "Severe"]
+
+def predict_interaction(text):
+    encoding = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**encoding)
+        probs = F.softmax(outputs.logits, dim=1)
+        pred = torch.argmax(probs, dim=1).item()
+    return f"🧠 Prediction: {labels[pred]}"
+
+gr.Interface(fn=predict_interaction, inputs="text", outputs="text", title="Drug Interaction Predictor").launch()

requirements.txt

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+transformers
+torch
+gradio