Create App.py

App.py

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+import gradio as gr
+import torch
+from transformers import BertTokenizer, BertForMaskedLM
+
+# Load the fine-tuned BERT model
+model_name = "/content/fine_tuned_bert_model"
+tokenizer = BertTokenizer.from_pretrained(model_name)
+model = BertForMaskedLM.from_pretrained(model_name)
+model.to("cuda" if torch.cuda.is_available() else "cpu")
+
+# Function to answer questions using the fine-tuned model
+def answer_question(context, question):
+    # Preprocess the context and question
+    context_tokens = tokenizer(context, truncation=True, padding="max_length", max_length=128, return_tensors="pt")
+    question_tokens = tokenizer(question, truncation=True, padding="max_length", max_length=16, return_tensors="pt")
+
+    # Move tensors to device
+    context_tokens = context_tokens.to(model.device)
+    question_tokens = question_tokens.to(model.device)
+
+    with torch.no_grad():
+        # Generate masked LM predictions for each token in the question
+        outputs = model(**question_tokens)
+        predictions = torch.argmax(outputs.logits, dim=-1)
+
+    # Replace masked tokens in the question with predicted tokens
+    answer_tokens = []
+    for i in range(len(question_tokens["input_ids"][0])):
+        if question_tokens["input_ids"][0][i] == tokenizer.mask_token_id:
+            answer_tokens.append(predictions[0][i].item())
+        else:
+            answer_tokens.append(question_tokens["input_ids"][0][i].item())
+
+    # Decode tokens and remove special tokens
+    answer = tokenizer.decode(answer_tokens, skip_special_tokens=True)
+
+    # Return the answer
+    return answer
+
+# Define example questions
+examples = [
+    ["Where did the Enron scandal occur?", "The Enron scandal occurred in [MASK]."],
+    ["What was the outcome of the Enron scandal?", "The outcome of the Enron scandal was [MASK]."],
+    ["When did Enron file for bankruptcy?", "Enron filed for bankruptcy in [MASK]."],
+    ["How did Enron's stock price change during the scandal?", "During the Enron scandal, Enron's stock price [MASK]."]
+]
+
+# Gradio interface with examples
+iface = gr.Interface(
+    fn=answer_question,
+    inputs=["text", "text"],
+    outputs="text",
+    title="Enron Email Analysis",
+    description="Ask questions about the Enron email dataset using a fine-tuned BERT model.",
+    examples=examples
+)
+
+# Launch the Gradio interface
+iface.launch(share=True)