Create app.py

app.py

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+# app.py
+
+# Import the necessary libraries
+import gradio as gr
+from transformers import pipeline
+
+# =============================================================================
+# 1. LOAD YOUR MODEL
+# =============================================================================
+# Use a pipeline for easy text classification. This will automatically
+# load your fine-tuned model and tokenizer from the repository.
+#
+# IMPORTANT: Replace "your-username/your-model-repo-name" with your actual model path.
+# For example: "Tarive/esm2_t12_35M_UR50D-finetuned-pfam-1k"
+#
+model_path = "Tarive/esm2_t12_35M_UR50D-finetuned-pfam-1k"
+classifier = pipeline("text-classification", model=model_path)
+
+
+# =============================================================================
+# 2. DEFINE THE PREDICTION FUNCTION
+# =============================================================================
+# This function takes a sequence as input and returns a formatted result.
+def predict_family(sequence):
+    # The pipeline returns a list of dictionaries.
+    # Example: [{'label': 'PF00042', 'score': 0.979}]
+    predictions = classifier(sequence, top_k=5) # Get the top 5 predictions
+    
+    # Format the results into a more readable dictionary for display.
+    results = {p['label']: p['score'] for p in predictions}
+    
+    return results
+
+# =============================================================================
+# 3. CREATE THE GRADIO INTERFACE
+# =============================================================================
+# This creates the actual web page interface.
+iface = gr.Interface(
+    fn=predict_family,
+    inputs=gr.Textbox(
+        lines=10, 
+        label="Protein Amino Acid Sequence", 
+        placeholder="Paste your protein sequence here..."
+    ),
+    outputs=gr.Label(
+        num_top_classes=5,
+        label="Predicted Families"
+    ),
+    title="Protein Family Classifier",
+    description="This demo uses a fine-tuned ESM-2 model to predict the protein family from its amino acid sequence. Enter a sequence to see the top 5 predictions and their confidence scores.",
+    examples=[
+        ["MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR"],
+        ["MTEYKLVVVGAGDVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVEVDCQQCMILDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHQYREQIKRVKDSDDVPMVLVGNKCDLAARTVESRQAQDLARSYGIPYIETSAKTRQGVEDAFYTLVREIRQHKLRKLNPPDESGGCMS"]
+    ],
+    allow_flagging="never" # Disables the "Flag" button for a cleaner interface
+)
+
+# Launch the interface!
+iface.launch()