def predict()

in use-cases/model-fine-tuning-pipeline/model-eval/src/validate_fine_tuned_model.py [0:0]

• 30 lines of code
• 4 McCabe index (conditional complexity)

    def predict(self):
        logger.info("Start prediction evaluation")
        # Send the Request
        headers = {"Content-Type": "application/json"}
        for i in range(len(self.df)):
            user_message = self.df["Question"][i]
            # Request Data
            request_data = {
                "model": self.model_name,
                "messages": [{"role": "user", "content": user_message}],
                "temperature": 0.5,
                "top_k": 1.0,
                "top_p": 1.0,
                "max_tokens": 256,
            }
            # print(f"API Endpoint {self.api_endpoint}")
            response = requests.post(
                self.api_endpoint, headers=headers, data=json.dumps(request_data)
            )

            # Check for Successful Response
            if response.status_code == 200:
                response_data = response.json()
                # Assuming the response structure matches OpenAI's format
                ai_response = response_data["choices"][0]["message"]["content"]

                with open(self.output_file, "a") as f:
                    f.write(ai_response + "\n")  # Append with newline
                    f.write("----------\n")
            else:
                logger.error(f"Error: {response.status_code} - {response.text}")

        # save file to gcs after completion
        model_iteration_tag = self.model_name.rsplit("-", 1)[1]
        client = storage.Client()
        bucket = client.get_bucket(self.gcs_bucket)
        with open(self.output_file, "r") as local_file:
            blob = bucket.blob(f"predictions/{self.output_file}-{model_iteration_tag}")
            blob.upload_from_file(local_file)