Create app.py

app.py

@@ -0,0 +1,60 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load the original pre-trained model
+def load_model(model_name):
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    return tokenizer, model
+
+# Models to compare
+original_model_name = "bert-base-uncased"  # Replace with your original model
+fine_tuned_model_name = "Vishwas1/bert-base-imdb"  # Replace with your fine-tuned model's repo ID
+
+# Load models
+original_tokenizer, original_model = load_model(original_model_name)
+fine_tuned_tokenizer, fine_tuned_model = load_model(fine_tuned_model_name)
+
+# Ensure models are in evaluation mode
+original_model.eval()
+fine_tuned_model.eval()
+
+def compare_models(text):
+    # Original model prediction
+    inputs_orig = original_tokenizer(text, return_tensors='pt', truncation=True, padding=True)
+    with torch.no_grad():
+        outputs_orig = original_model(**inputs_orig)
+    logits_orig = outputs_orig.logits
+    probs_orig = torch.softmax(logits_orig, dim=1)
+    pred_orig = torch.argmax(probs_orig, dim=1).item()
+    confidence_orig = probs_orig[0][pred_orig].item()
+
+    # Fine-tuned model prediction
+    inputs_fine = fine_tuned_tokenizer(text, return_tensors='pt', truncation=True, padding=True)
+    with torch.no_grad():
+        outputs_fine = fine_tuned_model(**inputs_fine)
+    logits_fine = outputs_fine.logits
+    probs_fine = torch.softmax(logits_fine, dim=1)
+    pred_fine = torch.argmax(probs_fine, dim=1).item()
+    confidence_fine = probs_fine[0][pred_fine].item()
+
+    # Map predictions to labels (adjust based on your model's labels)
+    labels = {0: "Negative", 1: "Positive"}
+
+    result = {
+        "Original Model Prediction": f"{labels[pred_orig]} ({confidence_orig:.2f})",
+        "Fine-Tuned Model Prediction": f"{labels[pred_fine]} ({confidence_fine:.2f})"
+    }
+    return result
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=compare_models,
+    inputs=gr.Textbox(lines=5, placeholder="Enter text here...", label="Input Text"),
+    outputs=gr.JSON(label="Model Predictions"),
+    title="Compare Original and Fine-Tuned Models",
+    description="Enter text to see predictions from the original and fine-tuned models."
+)
+
+iface.launch()