Create Agent-bert.py

Agent-bert.py

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+import os
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from transformers import pipeline
+model_name = "dbernsohn/roberta-java"
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+def preprocess_input(description):
+    input_text = "Generate an agent that " + description
+    inputs = tokenizer.encode(input_text, return_tensors='pt')
+    return inputs
+    def generate_agent_code(inputs):
+    generated_ids = model.generate(inputs)
+    agent_code = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+    return agent_code
+    import os
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from transformers import pipeline
+
+# Load the pre-trained CodeBERTa model and tokenizer
+model_name = "dbernsohn/roberta-java"
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Function to pre-process user input description
+def preprocess_input(description):
+    input_text = "Generate an agent that " + description
+    inputs = tokenizer.encode(input_text, return_tensors='pt')
+    return inputs
+
+# Function to generate agent code using the fine-tuned model
+def generate_agent_code(inputs):
+    generated_ids = model.generate(inputs)
+    agent_code = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+    return agent_code
+
+# Example usage
+user_description = "can perform sentiment analysis on text data."
+inputs = preprocess_input(user_description)
+generated_code = generate_agent_code(inputs)
+print(generated_code)